├── .DS_Store
├── NLTK_nlp
    ├── Bigrams.ipynb
    ├── NLP_plural.ipynb
    ├── NLTK_notebook.ipynb
    ├── basic_sentiment.py
    ├── movie-pang02.csv
    ├── negetive.csv
    ├── nlp_ner.py
    ├── nltk_2.ipynb
    ├── nltk_tf_idf.py
    ├── positive.csv
    └── readme.md
├── Other
    ├── Part-of-speech tagging.ipynb
    ├── gensim_similar.py
    └── readme.md
├── Parser
    └── readme.md
├── README.md
└── spacy_nlp
    ├── .DS_Store
    ├── .ipynb_checkpoints
        └── Intro_spaCy_NLP-checkpoint.ipynb
    ├── Generated.txt
    ├── Intro_spaCy_NLP.ipynb
    ├── parsing.py
    ├── pos.py
    ├── readme.md
    ├── sentiment.py
    ├── text1.txt
    ├── text2.txt
    ├── text_generate.py
    ├── tokens.py
    └── word2vec_model.py


/.DS_Store:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/kashyap32/NLP-Examples/8e55f5490187b2ffb54c6f4aac96d1fa51880610/.DS_Store


--------------------------------------------------------------------------------
/NLTK_nlp/Bigrams.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "import nltk\n"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "code",
 16 |    "execution_count": 36,
 17 |    "metadata": {
 18 |     "collapsed": true
 19 |    },
 20 |    "outputs": [],
 21 |    "source": [
 22 |     "text=\"I think it might rain today.\""
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "code",
 27 |    "execution_count": 37,
 28 |    "metadata": {
 29 |     "collapsed": false
 30 |    },
 31 |    "outputs": [],
 32 |    "source": [
 33 |     "tokens=nltk.word_tokenize(text)"
 34 |    ]
 35 |   },
 36 |   {
 37 |    "cell_type": "code",
 38 |    "execution_count": 38,
 39 |    "metadata": {
 40 |     "collapsed": false
 41 |    },
 42 |    "outputs": [
 43 |     {
 44 |      "data": {
 45 |       "text/plain": [
 46 |        "['I', 'think', 'it', 'might', 'rain', 'today', '.']"
 47 |       ]
 48 |      },
 49 |      "execution_count": 38,
 50 |      "metadata": {},
 51 |      "output_type": "execute_result"
 52 |     }
 53 |    ],
 54 |    "source": [
 55 |     "tokens"
 56 |    ]
 57 |   },
 58 |   {
 59 |    "cell_type": "code",
 60 |    "execution_count": 39,
 61 |    "metadata": {
 62 |     "collapsed": true
 63 |    },
 64 |    "outputs": [],
 65 |    "source": [
 66 |     "bigrams=nltk.bigrams(tokens)"
 67 |    ]
 68 |   },
 69 |   {
 70 |    "cell_type": "code",
 71 |    "execution_count": 40,
 72 |    "metadata": {
 73 |     "collapsed": false
 74 |    },
 75 |    "outputs": [
 76 |     {
 77 |      "name": "stdout",
 78 |      "output_type": "stream",
 79 |      "text": [
 80 |       "('I', 'think')\n",
 81 |       "('think', 'it')\n",
 82 |       "('it', 'might')\n",
 83 |       "('might', 'rain')\n",
 84 |       "('rain', 'today')\n",
 85 |       "('today', '.')\n"
 86 |      ]
 87 |     }
 88 |    ],
 89 |    "source": [
 90 |     "for item in bigrams:\n",
 91 |     "    print item"
 92 |    ]
 93 |   },
 94 |   {
 95 |    "cell_type": "code",
 96 |    "execution_count": 41,
 97 |    "metadata": {
 98 |     "collapsed": true
 99 |    },
100 |    "outputs": [],
101 |    "source": [
102 |     "trigrams=nltk.trigrams(tokens)"
103 |    ]
104 |   },
105 |   {
106 |    "cell_type": "code",
107 |    "execution_count": 42,
108 |    "metadata": {
109 |     "collapsed": false
110 |    },
111 |    "outputs": [
112 |     {
113 |      "name": "stdout",
114 |      "output_type": "stream",
115 |      "text": [
116 |       "('I', 'think', 'it')\n",
117 |       "('think', 'it', 'might')\n",
118 |       "('it', 'might', 'rain')\n",
119 |       "('might', 'rain', 'today')\n",
120 |       "('rain', 'today', '.')\n"
121 |      ]
122 |     }
123 |    ],
124 |    "source": [
125 |     "for item in trigrams:\n",
126 |     "    print item"
127 |    ]
128 |   },
129 |   {
130 |    "cell_type": "code",
131 |    "execution_count": 43,
132 |    "metadata": {
133 |     "collapsed": true
134 |    },
135 |    "outputs": [],
136 |    "source": [
137 |     "text2=text+\"If it is good ,I will go to the beach\""
138 |    ]
139 |   },
140 |   {
141 |    "cell_type": "code",
142 |    "execution_count": 44,
143 |    "metadata": {
144 |     "collapsed": false
145 |    },
146 |    "outputs": [
147 |     {
148 |      "data": {
149 |       "text/plain": [
150 |        "'I think it might rain today.If it is good ,I will go to the beach'"
151 |       ]
152 |      },
153 |      "execution_count": 44,
154 |      "metadata": {},
155 |      "output_type": "execute_result"
156 |     }
157 |    ],
158 |    "source": [
159 |     "text2"
160 |    ]
161 |   },
162 |   {
163 |    "cell_type": "code",
164 |    "execution_count": 45,
165 |    "metadata": {
166 |     "collapsed": false
167 |    },
168 |    "outputs": [],
169 |    "source": [
170 |     "ngrams1=nltk.ngrams(tokens,2)"
171 |    ]
172 |   },
173 |   {
174 |    "cell_type": "code",
175 |    "execution_count": 46,
176 |    "metadata": {
177 |     "collapsed": false
178 |    },
179 |    "outputs": [
180 |     {
181 |      "name": "stdout",
182 |      "output_type": "stream",
183 |      "text": [
184 |       "('I', 'think')\n",
185 |       "('think', 'it')\n",
186 |       "('it', 'might')\n",
187 |       "('might', 'rain')\n",
188 |       "('rain', 'today')\n",
189 |       "('today', '.')\n"
190 |      ]
191 |     }
192 |    ],
193 |    "source": [
194 |     "for item in ngrams1:\n",
195 |     "    print item"
196 |    ]
197 |   },
198 |   {
199 |    "cell_type": "code",
200 |    "execution_count": 47,
201 |    "metadata": {
202 |     "collapsed": true
203 |    },
204 |    "outputs": [],
205 |    "source": [
206 |     "ngrams1=nltk.ngrams(tokens,4)"
207 |    ]
208 |   },
209 |   {
210 |    "cell_type": "code",
211 |    "execution_count": 48,
212 |    "metadata": {
213 |     "collapsed": false
214 |    },
215 |    "outputs": [
216 |     {
217 |      "name": "stdout",
218 |      "output_type": "stream",
219 |      "text": [
220 |       "('I', 'think', 'it', 'might')\n",
221 |       "('think', 'it', 'might', 'rain')\n",
222 |       "('it', 'might', 'rain', 'today')\n",
223 |       "('might', 'rain', 'today', '.')\n"
224 |      ]
225 |     }
226 |    ],
227 |    "source": [
228 |     "for item in ngrams1:\n",
229 |     "    print item"
230 |    ]
231 |   },
232 |   {
233 |    "cell_type": "code",
234 |    "execution_count": 58,
235 |    "metadata": {
236 |     "collapsed": true
237 |    },
238 |    "outputs": [],
239 |    "source": [
240 |     "def n_gram(texts,n):\n",
241 |     "    tokens=nltk.word_tokenize(texts)\n",
242 |     "    ngram=nltk.ngrams(tokens,n)\n",
243 |     "    return ngram\n",
244 |     "    #for item in ngram:\n",
245 |     "     #   return item"
246 |    ]
247 |   },
248 |   {
249 |    "cell_type": "code",
250 |    "execution_count": 59,
251 |    "metadata": {
252 |     "collapsed": false
253 |    },
254 |    "outputs": [],
255 |    "source": [
256 |     "ngrams=n_gram(text2,5)"
257 |    ]
258 |   },
259 |   {
260 |    "cell_type": "code",
261 |    "execution_count": 60,
262 |    "metadata": {
263 |     "collapsed": false
264 |    },
265 |    "outputs": [
266 |     {
267 |      "name": "stdout",
268 |      "output_type": "stream",
269 |      "text": [
270 |       "('I', 'think', 'it', 'might', 'rain')\n",
271 |       "('think', 'it', 'might', 'rain', 'today.If')\n",
272 |       "('it', 'might', 'rain', 'today.If', 'it')\n",
273 |       "('might', 'rain', 'today.If', 'it', 'is')\n",
274 |       "('rain', 'today.If', 'it', 'is', 'good')\n",
275 |       "('today.If', 'it', 'is', 'good', ',')\n",
276 |       "('it', 'is', 'good', ',', 'I')\n",
277 |       "('is', 'good', ',', 'I', 'will')\n",
278 |       "('good', ',', 'I', 'will', 'go')\n",
279 |       "(',', 'I', 'will', 'go', 'to')\n",
280 |       "('I', 'will', 'go', 'to', 'the')\n",
281 |       "('will', 'go', 'to', 'the', 'beach')\n"
282 |      ]
283 |     }
284 |    ],
285 |    "source": [
286 |     "for item in ngrams:\n",
287 |     "    print item"
288 |    ]
289 |   },
290 |   {
291 |    "cell_type": "code",
292 |    "execution_count": 61,
293 |    "metadata": {
294 |     "collapsed": true
295 |    },
296 |    "outputs": [],
297 |    "source": [
298 |     "import re"
299 |    ]
300 |   },
301 |   {
302 |    "cell_type": "code",
303 |    "execution_count": 62,
304 |    "metadata": {
305 |     "collapsed": true
306 |    },
307 |    "outputs": [],
308 |    "source": [
309 |     "alice=nltk.corpus.gutenberg.words(\"carroll-alice.txt\")"
310 |    ]
311 |   },
312 |   {
313 |    "cell_type": "code",
314 |    "execution_count": 64,
315 |    "metadata": {
316 |     "collapsed": false
317 |    },
318 |    "outputs": [
319 |     {
320 |      "data": {
321 |       "text/plain": [
322 |        "{u'new', u'newspapers'}"
323 |       ]
324 |      },
325 |      "execution_count": 64,
326 |      "metadata": {},
327 |      "output_type": "execute_result"
328 |     }
329 |    ],
330 |    "source": [
331 |     "set([word for word in alice if re.search(\"^new\",word)])"
332 |    ]
333 |   },
334 |   {
335 |    "cell_type": "code",
336 |    "execution_count": 65,
337 |    "metadata": {
338 |     "collapsed": false
339 |    },
340 |    "outputs": [
341 |     {
342 |      "data": {
343 |       "text/plain": [
344 |        "{u'Advice',\n",
345 |        " u'Alice',\n",
346 |        " u'Arithmetic',\n",
347 |        " u'Back',\n",
348 |        " u'Because',\n",
349 |        " u'Caucus',\n",
350 |        " u'Classics',\n",
351 |        " u'Distraction',\n",
352 |        " u'Duchess',\n",
353 |        " u'Duck',\n",
354 |        " u'Each',\n",
355 |        " u'Exactly',\n",
356 |        " u'Jack',\n",
357 |        " u'Lacie',\n",
358 |        " u'Luckily',\n",
359 |        " u'Mock',\n",
360 |        " u'Multiplication',\n",
361 |        " u'Quick',\n",
362 |        " u'Race',\n",
363 |        " u'Such',\n",
364 |        " u'Treacle',\n",
365 |        " u'Uglification',\n",
366 |        " u'Which',\n",
367 |        " u'acceptance',\n",
368 |        " u'accident',\n",
369 |        " u'accidentally',\n",
370 |        " u'account',\n",
371 |        " u'accounting',\n",
372 |        " u'accounts',\n",
373 |        " u'accusation',\n",
374 |        " u'accustomed',\n",
375 |        " u'ache',\n",
376 |        " u'across',\n",
377 |        " u'act',\n",
378 |        " u'actually',\n",
379 |        " u'advice',\n",
380 |        " u'affectionately',\n",
381 |        " u'back',\n",
382 |        " u'backs',\n",
383 |        " u'became',\n",
384 |        " u'because',\n",
385 |        " u'become',\n",
386 |        " u'becoming',\n",
387 |        " u'blacking',\n",
388 |        " u'cackled',\n",
389 |        " u'character',\n",
390 |        " u'checked',\n",
391 |        " u'choice',\n",
392 |        " u'chuckled',\n",
393 |        " u'clock',\n",
394 |        " u'collected',\n",
395 |        " u'conduct',\n",
396 |        " u'contradicted',\n",
397 |        " u'crocodile',\n",
398 |        " u'crouched',\n",
399 |        " u'cucumber',\n",
400 |        " u'decided',\n",
401 |        " u'decidedly',\n",
402 |        " u'declare',\n",
403 |        " u'declared',\n",
404 |        " u'difficult',\n",
405 |        " u'difficulties',\n",
406 |        " u'difficulty',\n",
407 |        " u'directed',\n",
408 |        " u'direction',\n",
409 |        " u'directions',\n",
410 |        " u'directly',\n",
411 |        " u'duck',\n",
412 |        " u'each',\n",
413 |        " u'educations',\n",
414 |        " u'effect',\n",
415 |        " u'energetic',\n",
416 |        " u'exact',\n",
417 |        " u'exactly',\n",
418 |        " u'execute',\n",
419 |        " u'executed',\n",
420 |        " u'executes',\n",
421 |        " u'execution',\n",
422 |        " u'executioner',\n",
423 |        " u'executions',\n",
424 |        " u'expected',\n",
425 |        " u'expecting',\n",
426 |        " u'face',\n",
427 |        " u'faces',\n",
428 |        " u'fact',\n",
429 |        " u'fireplace',\n",
430 |        " u'flock',\n",
431 |        " u'frontispiece',\n",
432 |        " u'graceful',\n",
433 |        " u'idiotic',\n",
434 |        " u'introduce',\n",
435 |        " u'introduced',\n",
436 |        " u'justice',\n",
437 |        " u'kick',\n",
438 |        " u'knock',\n",
439 |        " u'knocked',\n",
440 |        " u'knocking',\n",
441 |        " u'knuckles',\n",
442 |        " u'licking',\n",
443 |        " u'lock',\n",
444 |        " u'locked',\n",
445 |        " u'locks',\n",
446 |        " u'luckily',\n",
447 |        " u'machines',\n",
448 |        " u'magic',\n",
449 |        " u'mice',\n",
450 |        " u'much',\n",
451 |        " u'muchness',\n",
452 |        " u'music',\n",
453 |        " u'neck',\n",
454 |        " u'nice',\n",
455 |        " u'nicely',\n",
456 |        " u'notice',\n",
457 |        " u'noticed',\n",
458 |        " u'noticing',\n",
459 |        " u'obstacle',\n",
460 |        " u'occasional',\n",
461 |        " u'occasionally',\n",
462 |        " u'occurred',\n",
463 |        " u'officer',\n",
464 |        " u'officers',\n",
465 |        " u'pace',\n",
466 |        " u'pack',\n",
467 |        " u'particular',\n",
468 |        " u'patriotic',\n",
469 |        " u'perfectly',\n",
470 |        " u'picked',\n",
471 |        " u'picking',\n",
472 |        " u'picture',\n",
473 |        " u'pictured',\n",
474 |        " u'pictures',\n",
475 |        " u'piece',\n",
476 |        " u'pieces',\n",
477 |        " u'place',\n",
478 |        " u'placed',\n",
479 |        " u'places',\n",
480 |        " u'pocket',\n",
481 |        " u'practice',\n",
482 |        " u'proceed',\n",
483 |        " u'procession',\n",
484 |        " u'processions',\n",
485 |        " u'produced',\n",
486 |        " u'producing',\n",
487 |        " u'prosecute',\n",
488 |        " u'protection',\n",
489 |        " u'quick',\n",
490 |        " u'quicker',\n",
491 |        " u'quickly',\n",
492 |        " u'race',\n",
493 |        " u'reach',\n",
494 |        " u'reaching',\n",
495 |        " u'received',\n",
496 |        " u'recognised',\n",
497 |        " u'recovered',\n",
498 |        " u'reduced',\n",
499 |        " u'respect',\n",
500 |        " u'respectable',\n",
501 |        " u'respectful',\n",
502 |        " u'rich',\n",
503 |        " u'ridiculous',\n",
504 |        " u'rock',\n",
505 |        " u'rocket',\n",
506 |        " u'saucepan',\n",
507 |        " u'saucepans',\n",
508 |        " u'saucer',\n",
509 |        " u'second',\n",
510 |        " u'secondly',\n",
511 |        " u'secret',\n",
512 |        " u'shock',\n",
513 |        " u'spectacles',\n",
514 |        " u'speech',\n",
515 |        " u'stick',\n",
516 |        " u'sticks',\n",
517 |        " u'stockings',\n",
518 |        " u'struck',\n",
519 |        " u'subject',\n",
520 |        " u'subjects',\n",
521 |        " u'succeeded',\n",
522 |        " u'such',\n",
523 |        " u'teaching',\n",
524 |        " u'teacup',\n",
525 |        " u'teacups',\n",
526 |        " u'thick',\n",
527 |        " u'touch',\n",
528 |        " u'treacle',\n",
529 |        " u'trickling',\n",
530 |        " u'tricks',\n",
531 |        " u'tucked',\n",
532 |        " u'twice',\n",
533 |        " u'unlocking',\n",
534 |        " u'verdict',\n",
535 |        " u'voice',\n",
536 |        " u'voices',\n",
537 |        " u'which'}"
538 |       ]
539 |      },
540 |      "execution_count": 65,
541 |      "metadata": {},
542 |      "output_type": "execute_result"
543 |     }
544 |    ],
545 |    "source": [
546 |     "set([word for word in alice if re.search(\"[aeiou]c\",word)])"
547 |    ]
548 |   },
549 |   {
550 |    "cell_type": "code",
551 |    "execution_count": 67,
552 |    "metadata": {
553 |     "collapsed": false
554 |    },
555 |    "outputs": [
556 |     {
557 |      "data": {
558 |       "text/plain": [
559 |        "{u'Exactly',\n",
560 |        " u'Luckily',\n",
561 |        " u'accidentally',\n",
562 |        " u'actually',\n",
563 |        " u'affectionately',\n",
564 |        " u'decidedly',\n",
565 |        " u'difficulty',\n",
566 |        " u'directly',\n",
567 |        " u'exactly',\n",
568 |        " u'luckily',\n",
569 |        " u'nicely',\n",
570 |        " u'occasionally',\n",
571 |        " u'perfectly',\n",
572 |        " u'quickly',\n",
573 |        " u'secondly'}"
574 |       ]
575 |      },
576 |      "execution_count": 67,
577 |      "metadata": {},
578 |      "output_type": "execute_result"
579 |     }
580 |    ],
581 |    "source": [
582 |     "set([word for word in alice if re.search(\"[aeiou]c.+y$\",word)])"
583 |    ]
584 |   },
585 |   {
586 |    "cell_type": "code",
587 |    "execution_count": 68,
588 |    "metadata": {
589 |     "collapsed": false
590 |    },
591 |    "outputs": [
592 |     {
593 |      "data": {
594 |       "text/plain": [
595 |        "{u'Beautiful',\n",
596 |        " u'barrowful',\n",
597 |        " u'beautiful',\n",
598 |        " u'delightful',\n",
599 |        " u'doubtful',\n",
600 |        " u'dreadful',\n",
601 |        " u'graceful',\n",
602 |        " u'hopeful',\n",
603 |        " u'mournful',\n",
604 |        " u'ootiful',\n",
605 |        " u'respectful',\n",
606 |        " u'sorrowful',\n",
607 |        " u'truthful',\n",
608 |        " u'useful',\n",
609 |        " u'wonderful'}"
610 |       ]
611 |      },
612 |      "execution_count": 68,
613 |      "metadata": {},
614 |      "output_type": "execute_result"
615 |     }
616 |    ],
617 |    "source": [
618 |     "set([word for word in alice if re.search(\"ful$\",word)])"
619 |    ]
620 |   },
621 |   {
622 |    "cell_type": "code",
623 |    "execution_count": 69,
624 |    "metadata": {
625 |     "collapsed": false
626 |    },
627 |    "outputs": [
628 |     {
629 |      "data": {
630 |       "text/plain": [
631 |        "{u'cannot', u'dinner', u'fanned', u'manner', u'tunnel'}"
632 |       ]
633 |      },
634 |      "execution_count": 69,
635 |      "metadata": {},
636 |      "output_type": "execute_result"
637 |     }
638 |    ],
639 |    "source": [
640 |     "set([word for word in alice if re.search(\"^..nn..$\",word)])"
641 |    ]
642 |   },
643 |   {
644 |    "cell_type": "code",
645 |    "execution_count": 70,
646 |    "metadata": {
647 |     "collapsed": false
648 |    },
649 |    "outputs": [
650 |     {
651 |      "data": {
652 |       "text/plain": [
653 |        "{u'Dinn', u'dinn'}"
654 |       ]
655 |      },
656 |      "execution_count": 70,
657 |      "metadata": {},
658 |      "output_type": "execute_result"
659 |     }
660 |    ],
661 |    "source": [
662 |     "set([word for word in alice if re.search(\"^..nn$\",word)])"
663 |    ]
664 |   },
665 |   {
666 |    "cell_type": "code",
667 |    "execution_count": 73,
668 |    "metadata": {
669 |     "collapsed": false
670 |    },
671 |    "outputs": [
672 |     {
673 |      "data": {
674 |       "text/plain": [
675 |        "{u'cat', u'hat', u'rat'}"
676 |       ]
677 |      },
678 |      "execution_count": 73,
679 |      "metadata": {},
680 |      "output_type": "execute_result"
681 |     }
682 |    ],
683 |    "source": [
684 |     "set([word for word in alice if re.search(\"^[chr]at$\",word)])"
685 |    ]
686 |   },
687 |   {
688 |    "cell_type": "code",
689 |    "execution_count": 74,
690 |    "metadata": {
691 |     "collapsed": false
692 |    },
693 |    "outputs": [
694 |     {
695 |      "data": {
696 |       "text/plain": [
697 |        "{u'Ann',\n",
698 |        " u'Dinn',\n",
699 |        " u'Pennyworth',\n",
700 |        " u'annoy',\n",
701 |        " u'annoyed',\n",
702 |        " u'beginning',\n",
703 |        " u'cannot',\n",
704 |        " u'cunning',\n",
705 |        " u'dinn',\n",
706 |        " u'dinner',\n",
707 |        " u'fanned',\n",
708 |        " u'fanning',\n",
709 |        " u'funny',\n",
710 |        " u'grinned',\n",
711 |        " u'grinning',\n",
712 |        " u'manner',\n",
713 |        " u'manners',\n",
714 |        " u'planning',\n",
715 |        " u'running',\n",
716 |        " u'tunnel'}"
717 |       ]
718 |      },
719 |      "execution_count": 74,
720 |      "metadata": {},
721 |      "output_type": "execute_result"
722 |     }
723 |    ],
724 |    "source": [
725 |     "set([word for word in alice if re.search(\"^.*nn.*$\",word)])"
726 |    ]
727 |   },
728 |   {
729 |    "cell_type": "code",
730 |    "execution_count": null,
731 |    "metadata": {
732 |     "collapsed": true
733 |    },
734 |    "outputs": [],
735 |    "source": []
736 |   }
737 |  ],
738 |  "metadata": {
739 |   "kernelspec": {
740 |    "display_name": "Python 2",
741 |    "language": "python",
742 |    "name": "python2"
743 |   },
744 |   "language_info": {
745 |    "codemirror_mode": {
746 |     "name": "ipython",
747 |     "version": 2
748 |    },
749 |    "file_extension": ".py",
750 |    "mimetype": "text/x-python",
751 |    "name": "python",
752 |    "nbconvert_exporter": "python",
753 |    "pygments_lexer": "ipython2",
754 |    "version": "2.7.13"
755 |   }
756 |  },
757 |  "nbformat": 4,
758 |  "nbformat_minor": 2
759 | }
760 | 


--------------------------------------------------------------------------------
/NLTK_nlp/basic_sentiment.py:
--------------------------------------------------------------------------------
 1 | import nltk
 2 | import csv
 3 | import numpy as np 
 4 | negative=[]
 5 | with open("negetive.csv","rb") as file:
 6 | 	reader=csv.reader(file)
 7 | 	for row in reader:
 8 | 		negative.append(row)
 9 | positive=[]
10 | with open("positive.csv","rb") as file:
11 | 	reader=csv.reader(file)
12 | 	for row in reader:
13 | 		positive.append(row)
14 | #print positive[:10]
15 | 
16 | def sentiment(text):
17 | 	temp=[]
18 | 	text_sent=nltk.sent_tokenize(text)
19 | 	for sentence in text_sent:
20 | 		n_count=0
21 | 		p_count=0
22 | 		sent_words=nltk.word_tokenize(sentence)
23 | 		for word in sent_words:
24 | 			for item in positive:
25 | 				if(word==item[0]):
26 | 					p_count+=1
27 | 			for item in negative:
28 | 				if (word==item[0]):
29 | 					n_count+=1
30 | 					temp.append(-1)
31 | 		if (p_count>0 and n_count==0):
32 | 			temp.append(1)
33 | 
34 | 			print "+ : " +sentence
35 | 		elif n_count%2>0:
36 | 			temp.append(-1)
37 | 
38 | 			print "- : " +sentence
39 | 		elif n_count%2==0 and n_count>0:
40 | 			temp.append(-1)
41 | 
42 | 			print "+ : " +sentence
43 | 		else:
44 | 			temp.append(0)
45 | 
46 | 			print "----"+sentence
47 | 	return temp
48 | sentiment("It was not that good!")
49 | sentiment("HI how are you")
50 | sentiment("La la land is worst")
51 | sentiment("I am good")
52 | 
53 | comments=[]
54 | with open("movie-pang02.csv",'rb') as file:
55 | 	reader=csv.reader(file)
56 | 	for row in reader:
57 | 		comments.append(row)
58 | print comments[0]
59 | for review in comments:
60 | 	print "\n"
61 | 	print np.average(sentiment(str(review)))
62 | 	print review
63 | 


--------------------------------------------------------------------------------
/NLTK_nlp/negetive.csv:
--------------------------------------------------------------------------------
   1 | 2-faced
   2 | 2-faces
   3 | abnormal
   4 | abolish
   5 | abominable
   6 | abominably
   7 | abominate
   8 | abomination
   9 | abort
  10 | aborted
  11 | aborts
  12 | abrade
  13 | abrasive
  14 | abrupt
  15 | abruptly
  16 | abscond
  17 | absence
  18 | absent-minded
  19 | absentee
  20 | absurd
  21 | absurdity
  22 | absurdly
  23 | absurdness
  24 | abuse
  25 | abused
  26 | abuses
  27 | abusive
  28 | abysmal
  29 | abysmally
  30 | abyss
  31 | accidental
  32 | accost
  33 | accursed
  34 | accusation
  35 | accusations
  36 | accuse
  37 | accuses
  38 | accusing
  39 | accusingly
  40 | acerbate
  41 | acerbic
  42 | acerbically
  43 | ache
  44 | ached
  45 | aches
  46 | achey
  47 | aching
  48 | acrid
  49 | acridly
  50 | acridness
  51 | acrimonious
  52 | acrimoniously
  53 | acrimony
  54 | adamant
  55 | adamantly
  56 | addict
  57 | addicted
  58 | addicting
  59 | addicts
  60 | admonish
  61 | admonisher
  62 | admonishingly
  63 | admonishment
  64 | admonition
  65 | adulterate
  66 | adulterated
  67 | adulteration
  68 | adulterier
  69 | adversarial
  70 | adversary
  71 | adverse
  72 | adversity
  73 | afflict
  74 | affliction
  75 | afflictive
  76 | affront
  77 | afraid
  78 | aggravate
  79 | aggravating
  80 | aggravation
  81 | aggression
  82 | aggressive
  83 | aggressiveness
  84 | aggressor
  85 | aggrieve
  86 | aggrieved
  87 | aggrivation
  88 | aghast
  89 | agonies
  90 | agonize
  91 | agonizing
  92 | agonizingly
  93 | agony
  94 | aground
  95 | ail
  96 | ailing
  97 | ailment
  98 | aimless
  99 | alarm
 100 | alarmed
 101 | alarming
 102 | alarmingly
 103 | alienate
 104 | alienated
 105 | alienation
 106 | allegation
 107 | allegations
 108 | allege
 109 | allergic
 110 | allergies
 111 | allergy
 112 | aloof
 113 | altercation
 114 | ambiguity
 115 | ambiguous
 116 | ambivalence
 117 | ambivalent
 118 | ambush
 119 | amiss
 120 | amputate
 121 | anarchism
 122 | anarchist
 123 | anarchistic
 124 | anarchy
 125 | anemic
 126 | anger
 127 | angrily
 128 | angriness
 129 | angry
 130 | anguish
 131 | animosity
 132 | annihilate
 133 | annihilation
 134 | annoy
 135 | annoyance
 136 | annoyances
 137 | annoyed
 138 | annoying
 139 | annoyingly
 140 | annoys
 141 | anomalous
 142 | anomaly
 143 | antagonism
 144 | antagonist
 145 | antagonistic
 146 | antagonize
 147 | anti-
 148 | anti-american
 149 | anti-israeli
 150 | anti-occupation
 151 | anti-proliferation
 152 | anti-semites
 153 | anti-social
 154 | anti-us
 155 | anti-white
 156 | antipathy
 157 | antiquated
 158 | antithetical
 159 | anxieties
 160 | anxiety
 161 | anxious
 162 | anxiously
 163 | anxiousness
 164 | apathetic
 165 | apathetically
 166 | apathy
 167 | apocalypse
 168 | apocalyptic
 169 | apologist
 170 | apologists
 171 | appal
 172 | appall
 173 | appalled
 174 | appalling
 175 | appallingly
 176 | apprehension
 177 | apprehensions
 178 | apprehensive
 179 | apprehensively
 180 | arbitrary
 181 | arcane
 182 | archaic
 183 | arduous
 184 | arduously
 185 | argumentative
 186 | arrogance
 187 | arrogant
 188 | arrogantly
 189 | ashamed
 190 | asinine
 191 | asininely
 192 | asinininity
 193 | askance
 194 | asperse
 195 | aspersion
 196 | aspersions
 197 | assail
 198 | assassin
 199 | assassinate
 200 | assault
 201 | assult
 202 | astray
 203 | asunder
 204 | atrocious
 205 | atrocities
 206 | atrocity
 207 | atrophy
 208 | attack
 209 | attacks
 210 | audacious
 211 | audaciously
 212 | audaciousness
 213 | audacity
 214 | audiciously
 215 | austere
 216 | authoritarian
 217 | autocrat
 218 | autocratic
 219 | avalanche
 220 | avarice
 221 | avaricious
 222 | avariciously
 223 | avenge
 224 | averse
 225 | aversion
 226 | aweful
 227 | awful
 228 | awfully
 229 | awfulness
 230 | awkward
 231 | awkwardness
 232 | ax
 233 | babble
 234 | back-logged
 235 | back-wood
 236 | back-woods
 237 | backache
 238 | backaches
 239 | backaching
 240 | backbite
 241 | backbiting
 242 | backward
 243 | backwardness
 244 | backwood
 245 | backwoods
 246 | bad
 247 | badly
 248 | baffle
 249 | baffled
 250 | bafflement
 251 | baffling
 252 | bait
 253 | balk
 254 | banal
 255 | banalize
 256 | bane
 257 | banish
 258 | banishment
 259 | bankrupt
 260 | barbarian
 261 | barbaric
 262 | barbarically
 263 | barbarity
 264 | barbarous
 265 | barbarously
 266 | barren
 267 | baseless
 268 | bash
 269 | bashed
 270 | bashful
 271 | bashing
 272 | bastard
 273 | bastards
 274 | battered
 275 | battering
 276 | batty
 277 | bearish
 278 | beastly
 279 | bedlam
 280 | bedlamite
 281 | befoul
 282 | beg
 283 | beggar
 284 | beggarly
 285 | begging
 286 | beguile
 287 | belabor
 288 | belated
 289 | beleaguer
 290 | belie
 291 | belittle
 292 | belittled
 293 | belittling
 294 | bellicose
 295 | belligerence
 296 | belligerent
 297 | belligerently
 298 | bemoan
 299 | bemoaning
 300 | bemused
 301 | bent
 302 | berate
 303 | bereave
 304 | bereavement
 305 | bereft
 306 | berserk
 307 | beseech
 308 | beset
 309 | besiege
 310 | besmirch
 311 | bestial
 312 | betray
 313 | betrayal
 314 | betrayals
 315 | betrayer
 316 | betraying
 317 | betrays
 318 | bewail
 319 | beware
 320 | bewilder
 321 | bewildered
 322 | bewildering
 323 | bewilderingly
 324 | bewilderment
 325 | bewitch
 326 | bias
 327 | biased
 328 | biases
 329 | bicker
 330 | bickering
 331 | bid-rigging
 332 | bigotries
 333 | bigotry
 334 | bitch
 335 | bitchy
 336 | biting
 337 | bitingly
 338 | bitter
 339 | bitterly
 340 | bitterness
 341 | bizarre
 342 | blab
 343 | blabber
 344 | blackmail
 345 | blah
 346 | blame
 347 | blameworthy
 348 | bland
 349 | blandish
 350 | blaspheme
 351 | blasphemous
 352 | blasphemy
 353 | blasted
 354 | blatant
 355 | blatantly
 356 | blather
 357 | bleak
 358 | bleakly
 359 | bleakness
 360 | bleed
 361 | bleeding
 362 | bleeds
 363 | blemish
 364 | blind
 365 | blinding
 366 | blindingly
 367 | blindside
 368 | blister
 369 | blistering
 370 | bloated
 371 | blockage
 372 | blockhead
 373 | bloodshed
 374 | bloodthirsty
 375 | bloody
 376 | blotchy
 377 | blow
 378 | blunder
 379 | blundering
 380 | blunders
 381 | blunt
 382 | blur
 383 | bluring
 384 | blurred
 385 | blurring
 386 | blurry
 387 | blurs
 388 | blurt
 389 | boastful
 390 | boggle
 391 | bogus
 392 | boil
 393 | boiling
 394 | boisterous
 395 | bomb
 396 | bombard
 397 | bombardment
 398 | bombastic
 399 | bondage
 400 | bonkers
 401 | bore
 402 | bored
 403 | boredom
 404 | bores
 405 | boring
 406 | botch
 407 | bother
 408 | bothered
 409 | bothering
 410 | bothers
 411 | bothersome
 412 | bowdlerize
 413 | boycott
 414 | braggart
 415 | bragger
 416 | brainless
 417 | brainwash
 418 | brash
 419 | brashly
 420 | brashness
 421 | brat
 422 | bravado
 423 | brazen
 424 | brazenly
 425 | brazenness
 426 | breach
 427 | break
 428 | break-up
 429 | break-ups
 430 | breakdown
 431 | breaking
 432 | breaks
 433 | breakup
 434 | breakups
 435 | bribery
 436 | brimstone
 437 | bristle
 438 | brittle
 439 | broke
 440 | broken
 441 | broken-hearted
 442 | brood
 443 | browbeat
 444 | bruise
 445 | bruised
 446 | bruises
 447 | bruising
 448 | brusque
 449 | brutal
 450 | brutalising
 451 | brutalities
 452 | brutality
 453 | brutalize
 454 | brutalizing
 455 | brutally
 456 | brute
 457 | brutish
 458 | bs
 459 | buckle
 460 | bug
 461 | bugging
 462 | buggy
 463 | bugs
 464 | bulkier
 465 | bulkiness
 466 | bulky
 467 | bulkyness
 468 | bull****
 469 | bull----
 470 | bullies
 471 | bullshit
 472 | bullshyt
 473 | bully
 474 | bullying
 475 | bullyingly
 476 | bum
 477 | bump
 478 | bumped
 479 | bumping
 480 | bumpping
 481 | bumps
 482 | bumpy
 483 | bungle
 484 | bungler
 485 | bungling
 486 | bunk
 487 | burden
 488 | burdensome
 489 | burdensomely
 490 | burn
 491 | burned
 492 | burning
 493 | burns
 494 | bust
 495 | busts
 496 | busybody
 497 | butcher
 498 | butchery
 499 | buzzing
 500 | byzantine
 501 | cackle
 502 | calamities
 503 | calamitous
 504 | calamitously
 505 | calamity
 506 | callous
 507 | calumniate
 508 | calumniation
 509 | calumnies
 510 | calumnious
 511 | calumniously
 512 | calumny
 513 | cancer
 514 | cancerous
 515 | cannibal
 516 | cannibalize
 517 | capitulate
 518 | capricious
 519 | capriciously
 520 | capriciousness
 521 | capsize
 522 | careless
 523 | carelessness
 524 | caricature
 525 | carnage
 526 | carp
 527 | cartoonish
 528 | cash-strapped
 529 | castigate
 530 | castrated
 531 | casualty
 532 | cataclysm
 533 | cataclysmal
 534 | cataclysmic
 535 | cataclysmically
 536 | catastrophe
 537 | catastrophes
 538 | catastrophic
 539 | catastrophically
 540 | catastrophies
 541 | caustic
 542 | caustically
 543 | cautionary
 544 | cave
 545 | censure
 546 | chafe
 547 | chaff
 548 | chagrin
 549 | challenging
 550 | chaos
 551 | chaotic
 552 | chasten
 553 | chastise
 554 | chastisement
 555 | chatter
 556 | chatterbox
 557 | cheap
 558 | cheapen
 559 | cheaply
 560 | cheat
 561 | cheated
 562 | cheater
 563 | cheating
 564 | cheats
 565 | checkered
 566 | cheerless
 567 | cheesy
 568 | chide
 569 | childish
 570 | chill
 571 | chilly
 572 | chintzy
 573 | choke
 574 | choleric
 575 | choppy
 576 | chore
 577 | chronic
 578 | chunky
 579 | clamor
 580 | clamorous
 581 | clash
 582 | cliche
 583 | cliched
 584 | clique
 585 | clog
 586 | clogged
 587 | clogs
 588 | cloud
 589 | clouding
 590 | cloudy
 591 | clueless
 592 | clumsy
 593 | clunky
 594 | coarse
 595 | cocky
 596 | coerce
 597 | coercion
 598 | coercive
 599 | cold
 600 | coldly
 601 | collapse
 602 | collude
 603 | collusion
 604 | combative
 605 | combust
 606 | comical
 607 | commiserate
 608 | commonplace
 609 | commotion
 610 | commotions
 611 | complacent
 612 | complain
 613 | complained
 614 | complaining
 615 | complains
 616 | complaint
 617 | complaints
 618 | complex
 619 | complicated
 620 | complication
 621 | complicit
 622 | compulsion
 623 | compulsive
 624 | concede
 625 | conceded
 626 | conceit
 627 | conceited
 628 | concen
 629 | concens
 630 | concern
 631 | concerned
 632 | concerns
 633 | concession
 634 | concessions
 635 | condemn
 636 | condemnable
 637 | condemnation
 638 | condemned
 639 | condemns
 640 | condescend
 641 | condescending
 642 | condescendingly
 643 | condescension
 644 | confess
 645 | confession
 646 | confessions
 647 | confined
 648 | conflict
 649 | conflicted
 650 | conflicting
 651 | conflicts
 652 | confound
 653 | confounded
 654 | confounding
 655 | confront
 656 | confrontation
 657 | confrontational
 658 | confuse
 659 | confused
 660 | confuses
 661 | confusing
 662 | confusion
 663 | confusions
 664 | congested
 665 | congestion
 666 | cons
 667 | conscons
 668 | conservative
 669 | conspicuous
 670 | conspicuously
 671 | conspiracies
 672 | conspiracy
 673 | conspirator
 674 | conspiratorial
 675 | conspire
 676 | consternation
 677 | contagious
 678 | contaminate
 679 | contaminated
 680 | contaminates
 681 | contaminating
 682 | contamination
 683 | contempt
 684 | contemptible
 685 | contemptuous
 686 | contemptuously
 687 | contend
 688 | contention
 689 | contentious
 690 | contort
 691 | contortions
 692 | contradict
 693 | contradiction
 694 | contradictory
 695 | contrariness
 696 | contravene
 697 | contrive
 698 | contrived
 699 | controversial
 700 | controversy
 701 | convoluted
 702 | corrode
 703 | corrosion
 704 | corrosions
 705 | corrosive
 706 | corrupt
 707 | corrupted
 708 | corrupting
 709 | corruption
 710 | corrupts
 711 | corruptted
 712 | costlier
 713 | costly
 714 | counter-productive
 715 | counterproductive
 716 | coupists
 717 | covetous
 718 | coward
 719 | cowardly
 720 | crabby
 721 | crack
 722 | cracked
 723 | cracks
 724 | craftily
 725 | craftly
 726 | crafty
 727 | cramp
 728 | cramped
 729 | cramping
 730 | cranky
 731 | crap
 732 | crappy
 733 | craps
 734 | crash
 735 | crashed
 736 | crashes
 737 | crashing
 738 | crass
 739 | craven
 740 | cravenly
 741 | craze
 742 | crazily
 743 | craziness
 744 | crazy
 745 | creak
 746 | creaking
 747 | creaks
 748 | credulous
 749 | creep
 750 | creeping
 751 | creeps
 752 | creepy
 753 | crept
 754 | crime
 755 | criminal
 756 | cringe
 757 | cringed
 758 | cringes
 759 | cripple
 760 | crippled
 761 | cripples
 762 | crippling
 763 | crisis
 764 | critic
 765 | critical
 766 | criticism
 767 | criticisms
 768 | criticize
 769 | criticized
 770 | criticizing
 771 | critics
 772 | cronyism
 773 | crook
 774 | crooked
 775 | crooks
 776 | crowded
 777 | crowdedness
 778 | crude
 779 | cruel
 780 | crueler
 781 | cruelest
 782 | cruelly
 783 | cruelness
 784 | cruelties
 785 | cruelty
 786 | crumble
 787 | crumbling
 788 | crummy
 789 | crumple
 790 | crumpled
 791 | crumples
 792 | crush
 793 | crushed
 794 | crushing
 795 | cry
 796 | culpable
 797 | culprit
 798 | cumbersome
 799 | cunt
 800 | cunts
 801 | cuplrit
 802 | curse
 803 | cursed
 804 | curses
 805 | curt
 806 | cuss
 807 | cussed
 808 | cutthroat
 809 | cynical
 810 | cynicism
 811 | d*mn
 812 | damage
 813 | damaged
 814 | damages
 815 | damaging
 816 | damn
 817 | damnable
 818 | damnably
 819 | damnation
 820 | damned
 821 | damning
 822 | damper
 823 | danger
 824 | dangerous
 825 | dangerousness
 826 | dark
 827 | darken
 828 | darkened
 829 | darker
 830 | darkness
 831 | dastard
 832 | dastardly
 833 | daunt
 834 | daunting
 835 | dauntingly
 836 | dawdle
 837 | daze
 838 | dazed
 839 | dead
 840 | deadbeat
 841 | deadlock
 842 | deadly
 843 | deadweight
 844 | deaf
 845 | dearth
 846 | death
 847 | debacle
 848 | debase
 849 | debasement
 850 | debaser
 851 | debatable
 852 | debauch
 853 | debaucher
 854 | debauchery
 855 | debilitate
 856 | debilitating
 857 | debility
 858 | debt
 859 | debts
 860 | decadence
 861 | decadent
 862 | decay
 863 | decayed
 864 | deceit
 865 | deceitful
 866 | deceitfully
 867 | deceitfulness
 868 | deceive
 869 | deceiver
 870 | deceivers
 871 | deceiving
 872 | deception
 873 | deceptive
 874 | deceptively
 875 | declaim
 876 | decline
 877 | declines
 878 | declining
 879 | decrement
 880 | decrepit
 881 | decrepitude
 882 | decry
 883 | defamation
 884 | defamations
 885 | defamatory
 886 | defame
 887 | defect
 888 | defective
 889 | defects
 890 | defensive
 891 | defiance
 892 | defiant
 893 | defiantly
 894 | deficiencies
 895 | deficiency
 896 | deficient
 897 | defile
 898 | defiler
 899 | deform
 900 | deformed
 901 | defrauding
 902 | defunct
 903 | defy
 904 | degenerate
 905 | degenerately
 906 | degeneration
 907 | degradation
 908 | degrade
 909 | degrading
 910 | degradingly
 911 | dehumanization
 912 | dehumanize
 913 | deign
 914 | deject
 915 | dejected
 916 | dejectedly
 917 | dejection
 918 | delay
 919 | delayed
 920 | delaying
 921 | delays
 922 | delinquency
 923 | delinquent
 924 | delirious
 925 | delirium
 926 | delude
 927 | deluded
 928 | deluge
 929 | delusion
 930 | delusional
 931 | delusions
 932 | demean
 933 | demeaning
 934 | demise
 935 | demolish
 936 | demolisher
 937 | demon
 938 | demonic
 939 | demonize
 940 | demonized
 941 | demonizes
 942 | demonizing
 943 | demoralize
 944 | demoralizing
 945 | demoralizingly
 946 | denial
 947 | denied
 948 | denies
 949 | denigrate
 950 | denounce
 951 | dense
 952 | dent
 953 | dented
 954 | dents
 955 | denunciate
 956 | denunciation
 957 | denunciations
 958 | deny
 959 | denying
 960 | deplete
 961 | deplorable
 962 | deplorably
 963 | deplore
 964 | deploring
 965 | deploringly
 966 | deprave
 967 | depraved
 968 | depravedly
 969 | deprecate
 970 | depress
 971 | depressed
 972 | depressing
 973 | depressingly
 974 | depression
 975 | depressions
 976 | deprive
 977 | deprived
 978 | deride
 979 | derision
 980 | derisive
 981 | derisively
 982 | derisiveness
 983 | derogatory
 984 | desecrate
 985 | desert
 986 | desertion
 987 | desiccate
 988 | desiccated
 989 | desititute
 990 | desolate
 991 | desolately
 992 | desolation
 993 | despair
 994 | despairing
 995 | despairingly
 996 | desperate
 997 | desperately
 998 | desperation
 999 | despicable
1000 | despicably
1001 | despise
1002 | despised
1003 | despoil
1004 | despoiler
1005 | despondence
1006 | despondency
1007 | despondent
1008 | despondently
1009 | despot
1010 | despotic
1011 | despotism
1012 | destabilisation
1013 | destains
1014 | destitute
1015 | destitution
1016 | destroy
1017 | destroyer
1018 | destruction
1019 | destructive
1020 | desultory
1021 | deter
1022 | deteriorate
1023 | deteriorating
1024 | deterioration
1025 | deterrent
1026 | detest
1027 | detestable
1028 | detestably
1029 | detested
1030 | detesting
1031 | detests
1032 | detract
1033 | detracted
1034 | detracting
1035 | detraction
1036 | detracts
1037 | detriment
1038 | detrimental
1039 | devastate
1040 | devastated
1041 | devastates
1042 | devastating
1043 | devastatingly
1044 | devastation
1045 | deviate
1046 | deviation
1047 | devil
1048 | devilish
1049 | devilishly
1050 | devilment
1051 | devilry
1052 | devious
1053 | deviously
1054 | deviousness
1055 | devoid
1056 | diabolic
1057 | diabolical
1058 | diabolically
1059 | diametrically
1060 | diappointed
1061 | diatribe
1062 | diatribes
1063 | dick
1064 | dictator
1065 | dictatorial
1066 | die
1067 | die-hard
1068 | died
1069 | dies
1070 | difficult
1071 | difficulties
1072 | difficulty
1073 | diffidence
1074 | dilapidated
1075 | dilemma
1076 | dilly-dally
1077 | dim
1078 | dimmer
1079 | din
1080 | ding
1081 | dings
1082 | dinky
1083 | dire
1084 | direly
1085 | direness
1086 | dirt
1087 | dirtbag
1088 | dirtbags
1089 | dirts
1090 | dirty
1091 | disable
1092 | disabled
1093 | disaccord
1094 | disadvantage
1095 | disadvantaged
1096 | disadvantageous
1097 | disadvantages
1098 | disaffect
1099 | disaffected
1100 | disaffirm
1101 | disagree
1102 | disagreeable
1103 | disagreeably
1104 | disagreed
1105 | disagreeing
1106 | disagreement
1107 | disagrees
1108 | disallow
1109 | disapointed
1110 | disapointing
1111 | disapointment
1112 | disappoint
1113 | disappointed
1114 | disappointing
1115 | disappointingly
1116 | disappointment
1117 | disappointments
1118 | disappoints
1119 | disapprobation
1120 | disapproval
1121 | disapprove
1122 | disapproving
1123 | disarm
1124 | disarray
1125 | disaster
1126 | disasterous
1127 | disastrous
1128 | disastrously
1129 | disavow
1130 | disavowal
1131 | disbelief
1132 | disbelieve
1133 | disbeliever
1134 | disclaim
1135 | discombobulate
1136 | discomfit
1137 | discomfititure
1138 | discomfort
1139 | discompose
1140 | disconcert
1141 | disconcerted
1142 | disconcerting
1143 | disconcertingly
1144 | disconsolate
1145 | disconsolately
1146 | disconsolation
1147 | discontent
1148 | discontented
1149 | discontentedly
1150 | discontinued
1151 | discontinuity
1152 | discontinuous
1153 | discord
1154 | discordance
1155 | discordant
1156 | discountenance
1157 | discourage
1158 | discouragement
1159 | discouraging
1160 | discouragingly
1161 | discourteous
1162 | discourteously
1163 | discoutinous
1164 | discredit
1165 | discrepant
1166 | discriminate
1167 | discrimination
1168 | discriminatory
1169 | disdain
1170 | disdained
1171 | disdainful
1172 | disdainfully
1173 | disfavor
1174 | disgrace
1175 | disgraced
1176 | disgraceful
1177 | disgracefully
1178 | disgruntle
1179 | disgruntled
1180 | disgust
1181 | disgusted
1182 | disgustedly
1183 | disgustful
1184 | disgustfully
1185 | disgusting
1186 | disgustingly
1187 | dishearten
1188 | disheartening
1189 | dishearteningly
1190 | dishonest
1191 | dishonestly
1192 | dishonesty
1193 | dishonor
1194 | dishonorable
1195 | dishonorablely
1196 | disillusion
1197 | disillusioned
1198 | disillusionment
1199 | disillusions
1200 | disinclination
1201 | disinclined
1202 | disingenuous
1203 | disingenuously
1204 | disintegrate
1205 | disintegrated
1206 | disintegrates
1207 | disintegration
1208 | disinterest
1209 | disinterested
1210 | dislike
1211 | disliked
1212 | dislikes
1213 | disliking
1214 | dislocated
1215 | disloyal
1216 | disloyalty
1217 | dismal
1218 | dismally
1219 | dismalness
1220 | dismay
1221 | dismayed
1222 | dismaying
1223 | dismayingly
1224 | dismissive
1225 | dismissively
1226 | disobedience
1227 | disobedient
1228 | disobey
1229 | disoobedient
1230 | disorder
1231 | disordered
1232 | disorderly
1233 | disorganized
1234 | disorient
1235 | disoriented
1236 | disown
1237 | disparage
1238 | disparaging
1239 | disparagingly
1240 | dispensable
1241 | dispirit
1242 | dispirited
1243 | dispiritedly
1244 | dispiriting
1245 | displace
1246 | displaced
1247 | displease
1248 | displeased
1249 | displeasing
1250 | displeasure
1251 | disproportionate
1252 | disprove
1253 | disputable
1254 | dispute
1255 | disputed
1256 | disquiet
1257 | disquieting
1258 | disquietingly
1259 | disquietude
1260 | disregard
1261 | disregardful
1262 | disreputable
1263 | disrepute
1264 | disrespect
1265 | disrespectable
1266 | disrespectablity
1267 | disrespectful
1268 | disrespectfully
1269 | disrespectfulness
1270 | disrespecting
1271 | disrupt
1272 | disruption
1273 | disruptive
1274 | diss
1275 | dissapointed
1276 | dissappointed
1277 | dissappointing
1278 | dissatisfaction
1279 | dissatisfactory
1280 | dissatisfied
1281 | dissatisfies
1282 | dissatisfy
1283 | dissatisfying
1284 | dissed
1285 | dissemble
1286 | dissembler
1287 | dissension
1288 | dissent
1289 | dissenter
1290 | dissention
1291 | disservice
1292 | disses
1293 | dissidence
1294 | dissident
1295 | dissidents
1296 | dissing
1297 | dissocial
1298 | dissolute
1299 | dissolution
1300 | dissonance
1301 | dissonant
1302 | dissonantly
1303 | dissuade
1304 | dissuasive
1305 | distains
1306 | distaste
1307 | distasteful
1308 | distastefully
1309 | distort
1310 | distorted
1311 | distortion
1312 | distorts
1313 | distract
1314 | distracting
1315 | distraction
1316 | distraught
1317 | distraughtly
1318 | distraughtness
1319 | distress
1320 | distressed
1321 | distressing
1322 | distressingly
1323 | distrust
1324 | distrustful
1325 | distrusting
1326 | disturb
1327 | disturbance
1328 | disturbed
1329 | disturbing
1330 | disturbingly
1331 | disunity
1332 | disvalue
1333 | divergent
1334 | divisive
1335 | divisively
1336 | divisiveness
1337 | dizzing
1338 | dizzingly
1339 | dizzy
1340 | doddering
1341 | dodgey
1342 | dogged
1343 | doggedly
1344 | dogmatic
1345 | doldrums
1346 | domineer
1347 | domineering
1348 | donside
1349 | doom
1350 | doomed
1351 | doomsday
1352 | dope
1353 | doubt
1354 | doubtful
1355 | doubtfully
1356 | doubts
1357 | douchbag
1358 | douchebag
1359 | douchebags
1360 | downbeat
1361 | downcast
1362 | downer
1363 | downfall
1364 | downfallen
1365 | downgrade
1366 | downhearted
1367 | downheartedly
1368 | downhill
1369 | downside
1370 | downsides
1371 | downturn
1372 | downturns
1373 | drab
1374 | draconian
1375 | draconic
1376 | drag
1377 | dragged
1378 | dragging
1379 | dragoon
1380 | drags
1381 | drain
1382 | drained
1383 | draining
1384 | drains
1385 | drastic
1386 | drastically
1387 | drawback
1388 | drawbacks
1389 | dread
1390 | dreadful
1391 | dreadfully
1392 | dreadfulness
1393 | dreary
1394 | dripped
1395 | dripping
1396 | drippy
1397 | drips
1398 | drones
1399 | droop
1400 | droops
1401 | drop-out
1402 | drop-outs
1403 | dropout
1404 | dropouts
1405 | drought
1406 | drowning
1407 | drunk
1408 | drunkard
1409 | drunken
1410 | dubious
1411 | dubiously
1412 | dubitable
1413 | dud
1414 | dull
1415 | dullard
1416 | dumb
1417 | dumbfound
1418 | dump
1419 | dumped
1420 | dumping
1421 | dumps
1422 | dunce
1423 | dungeon
1424 | dungeons
1425 | dupe
1426 | dust
1427 | dusty
1428 | dwindling
1429 | dying
1430 | earsplitting
1431 | eccentric
1432 | eccentricity
1433 | effigy
1434 | effrontery
1435 | egocentric
1436 | egomania
1437 | egotism
1438 | egotistical
1439 | egotistically
1440 | egregious
1441 | egregiously
1442 | election-rigger
1443 | elimination
1444 | emaciated
1445 | emasculate
1446 | embarrass
1447 | embarrassing
1448 | embarrassingly
1449 | embarrassment
1450 | embattled
1451 | embroil
1452 | embroiled
1453 | embroilment
1454 | emergency
1455 | emphatic
1456 | emphatically
1457 | emptiness
1458 | encroach
1459 | encroachment
1460 | endanger
1461 | enemies
1462 | enemy
1463 | enervate
1464 | enfeeble
1465 | enflame
1466 | engulf
1467 | enjoin
1468 | enmity
1469 | enrage
1470 | enraged
1471 | enraging
1472 | enslave
1473 | entangle
1474 | entanglement
1475 | entrap
1476 | entrapment
1477 | envious
1478 | enviously
1479 | enviousness
1480 | epidemic
1481 | equivocal
1482 | erase
1483 | erode
1484 | erodes
1485 | erosion
1486 | err
1487 | errant
1488 | erratic
1489 | erratically
1490 | erroneous
1491 | erroneously
1492 | error
1493 | errors
1494 | eruptions
1495 | escapade
1496 | eschew
1497 | estranged
1498 | evade
1499 | evasion
1500 | evasive
1501 | evil
1502 | evildoer
1503 | evils
1504 | eviscerate
1505 | exacerbate
1506 | exagerate
1507 | exagerated
1508 | exagerates
1509 | exaggerate
1510 | exaggeration
1511 | exasperate
1512 | exasperated
1513 | exasperating
1514 | exasperatingly
1515 | exasperation
1516 | excessive
1517 | excessively
1518 | exclusion
1519 | excoriate
1520 | excruciating
1521 | excruciatingly
1522 | excuse
1523 | excuses
1524 | execrate
1525 | exhaust
1526 | exhausted
1527 | exhaustion
1528 | exhausts
1529 | exhorbitant
1530 | exhort
1531 | exile
1532 | exorbitant
1533 | exorbitantance
1534 | exorbitantly
1535 | expel
1536 | expensive
1537 | expire
1538 | expired
1539 | explode
1540 | exploit
1541 | exploitation
1542 | explosive
1543 | expropriate
1544 | expropriation
1545 | expulse
1546 | expunge
1547 | exterminate
1548 | extermination
1549 | extinguish
1550 | extort
1551 | extortion
1552 | extraneous
1553 | extravagance
1554 | extravagant
1555 | extravagantly
1556 | extremism
1557 | extremist
1558 | extremists
1559 | eyesore
1560 | f**k
1561 | fabricate
1562 | fabrication
1563 | facetious
1564 | facetiously
1565 | fail
1566 | failed
1567 | failing
1568 | fails
1569 | failure
1570 | failures
1571 | faint
1572 | fainthearted
1573 | faithless
1574 | fake
1575 | fall
1576 | fallacies
1577 | fallacious
1578 | fallaciously
1579 | fallaciousness
1580 | fallacy
1581 | fallen
1582 | falling
1583 | fallout
1584 | falls
1585 | FALSE
1586 | falsehood
1587 | falsely
1588 | falsify
1589 | falter
1590 | faltered
1591 | famine
1592 | famished
1593 | fanatic
1594 | fanatical
1595 | fanatically
1596 | fanaticism
1597 | fanatics
1598 | fanciful
1599 | far-fetched
1600 | farce
1601 | farcical
1602 | farcical-yet-provocative
1603 | farcically
1604 | farfetched
1605 | fascism
1606 | fascist
1607 | fastidious
1608 | fastidiously
1609 | fastuous
1610 | fat
1611 | fat-cat
1612 | fat-cats
1613 | fatal
1614 | fatalistic
1615 | fatalistically
1616 | fatally
1617 | fatcat
1618 | fatcats
1619 | fateful
1620 | fatefully
1621 | fathomless
1622 | fatigue
1623 | fatigued
1624 | fatique
1625 | fatty
1626 | fatuity
1627 | fatuous
1628 | fatuously
1629 | fault
1630 | faults
1631 | faulty
1632 | fawningly
1633 | faze
1634 | fear
1635 | fearful
1636 | fearfully
1637 | fears
1638 | fearsome
1639 | feckless
1640 | feeble
1641 | feeblely
1642 | feebleminded
1643 | feign
1644 | feint
1645 | fell
1646 | felon
1647 | felonious
1648 | ferociously
1649 | ferocity
1650 | fetid
1651 | fever
1652 | feverish
1653 | fevers
1654 | fiasco
1655 | fib
1656 | fibber
1657 | fickle
1658 | fiction
1659 | fictional
1660 | fictitious
1661 | fidget
1662 | fidgety
1663 | fiend
1664 | fiendish
1665 | fierce
1666 | figurehead
1667 | filth
1668 | filthy
1669 | finagle
1670 | finicky
1671 | fissures
1672 | fist
1673 | flabbergast
1674 | flabbergasted
1675 | flagging
1676 | flagrant
1677 | flagrantly
1678 | flair
1679 | flairs
1680 | flak
1681 | flake
1682 | flakey
1683 | flakieness
1684 | flaking
1685 | flaky
1686 | flare
1687 | flares
1688 | flareup
1689 | flareups
1690 | flat-out
1691 | flaunt
1692 | flaw
1693 | flawed
1694 | flaws
1695 | flee
1696 | fleed
1697 | fleeing
1698 | fleer
1699 | flees
1700 | fleeting
1701 | flicering
1702 | flicker
1703 | flickering
1704 | flickers
1705 | flighty
1706 | flimflam
1707 | flimsy
1708 | flirt
1709 | flirty
1710 | floored
1711 | flounder
1712 | floundering
1713 | flout
1714 | fluster
1715 | foe
1716 | fool
1717 | fooled
1718 | foolhardy
1719 | foolish
1720 | foolishly
1721 | foolishness
1722 | forbid
1723 | forbidden
1724 | forbidding
1725 | forceful
1726 | foreboding
1727 | forebodingly
1728 | forfeit
1729 | forged
1730 | forgetful
1731 | forgetfully
1732 | forgetfulness
1733 | forlorn
1734 | forlornly
1735 | forsake
1736 | forsaken
1737 | forswear
1738 | foul
1739 | foully
1740 | foulness
1741 | fractious
1742 | fractiously
1743 | fracture
1744 | fragile
1745 | fragmented
1746 | frail
1747 | frantic
1748 | frantically
1749 | franticly
1750 | fraud
1751 | fraudulent
1752 | fraught
1753 | frazzle
1754 | frazzled
1755 | freak
1756 | freaking
1757 | freakish
1758 | freakishly
1759 | freaks
1760 | freeze
1761 | freezes
1762 | freezing
1763 | frenetic
1764 | frenetically
1765 | frenzied
1766 | frenzy
1767 | fret
1768 | fretful
1769 | frets
1770 | friction
1771 | frictions
1772 | fried
1773 | friggin
1774 | frigging
1775 | fright
1776 | frighten
1777 | frightening
1778 | frighteningly
1779 | frightful
1780 | frightfully
1781 | frigid
1782 | frost
1783 | frown
1784 | froze
1785 | frozen
1786 | fruitless
1787 | fruitlessly
1788 | frustrate
1789 | frustrated
1790 | frustrates
1791 | frustrating
1792 | frustratingly
1793 | frustration
1794 | frustrations
1795 | fuck
1796 | fucking
1797 | fudge
1798 | fugitive
1799 | full-blown
1800 | fulminate
1801 | fumble
1802 | fume
1803 | fumes
1804 | fundamentalism
1805 | funky
1806 | funnily
1807 | funny
1808 | furious
1809 | furiously
1810 | furor
1811 | fury
1812 | fuss
1813 | fussy
1814 | fustigate
1815 | fusty
1816 | futile
1817 | futilely
1818 | futility
1819 | fuzzy
1820 | gabble
1821 | gaff
1822 | gaffe
1823 | gainsay
1824 | gainsayer
1825 | gall
1826 | galling
1827 | gallingly
1828 | galls
1829 | gangster
1830 | gape
1831 | garbage
1832 | garish
1833 | gasp
1834 | gauche
1835 | gaudy
1836 | gawk
1837 | gawky
1838 | geezer
1839 | genocide
1840 | get-rich
1841 | ghastly
1842 | ghetto
1843 | ghosting
1844 | gibber
1845 | gibberish
1846 | gibe
1847 | giddy
1848 | gimmick
1849 | gimmicked
1850 | gimmicking
1851 | gimmicks
1852 | gimmicky
1853 | glare
1854 | glaringly
1855 | glib
1856 | glibly
1857 | glitch
1858 | glitches
1859 | gloatingly
1860 | gloom
1861 | gloomy
1862 | glower
1863 | glum
1864 | glut
1865 | gnawing
1866 | goad
1867 | goading
1868 | god-awful
1869 | goof
1870 | goofy
1871 | goon
1872 | gossip
1873 | graceless
1874 | gracelessly
1875 | graft
1876 | grainy
1877 | grapple
1878 | grate
1879 | grating
1880 | gravely
1881 | greasy
1882 | greed
1883 | greedy
1884 | grief
1885 | grievance
1886 | grievances
1887 | grieve
1888 | grieving
1889 | grievous
1890 | grievously
1891 | grim
1892 | grimace
1893 | grind
1894 | gripe
1895 | gripes
1896 | grisly
1897 | gritty
1898 | gross
1899 | grossly
1900 | grotesque
1901 | grouch
1902 | grouchy
1903 | groundless
1904 | grouse
1905 | growl
1906 | grudge
1907 | grudges
1908 | grudging
1909 | grudgingly
1910 | gruesome
1911 | gruesomely
1912 | gruff
1913 | grumble
1914 | grumpier
1915 | grumpiest
1916 | grumpily
1917 | grumpish
1918 | grumpy
1919 | guile
1920 | guilt
1921 | guiltily
1922 | guilty
1923 | gullible
1924 | gutless
1925 | gutter
1926 | hack
1927 | hacks
1928 | haggard
1929 | haggle
1930 | hairloss
1931 | halfhearted
1932 | halfheartedly
1933 | hallucinate
1934 | hallucination
1935 | hamper
1936 | hampered
1937 | handicapped
1938 | hang
1939 | hangs
1940 | haphazard
1941 | hapless
1942 | harangue
1943 | harass
1944 | harassed
1945 | harasses
1946 | harassment
1947 | harboring
1948 | harbors
1949 | hard
1950 | hard-hit
1951 | hard-line
1952 | hard-liner
1953 | hardball
1954 | harden
1955 | hardened
1956 | hardheaded
1957 | hardhearted
1958 | hardliner
1959 | hardliners
1960 | hardship
1961 | hardships
1962 | harm
1963 | harmed
1964 | harmful
1965 | harms
1966 | harpy
1967 | harridan
1968 | harried
1969 | harrow
1970 | harsh
1971 | harshly
1972 | hasseling
1973 | hassle
1974 | hassled
1975 | hassles
1976 | haste
1977 | hastily
1978 | hasty
1979 | hate
1980 | hated
1981 | hateful
1982 | hatefully
1983 | hatefulness
1984 | hater
1985 | haters
1986 | hates
1987 | hating
1988 | hatred
1989 | haughtily
1990 | haughty
1991 | haunt
1992 | haunting
1993 | havoc
1994 | hawkish
1995 | haywire
1996 | hazard
1997 | hazardous
1998 | haze
1999 | hazy
2000 | head-aches
2001 | headache
2002 | headaches
2003 | heartbreaker
2004 | heartbreaking
2005 | heartbreakingly
2006 | heartless
2007 | heathen
2008 | heavy-handed
2009 | heavyhearted
2010 | heck
2011 | heckle
2012 | heckled
2013 | heckles
2014 | hectic
2015 | hedge
2016 | hedonistic
2017 | heedless
2018 | hefty
2019 | hegemonism
2020 | hegemonistic
2021 | hegemony
2022 | heinous
2023 | hell
2024 | hell-bent
2025 | hellion
2026 | hells
2027 | helpless
2028 | helplessly
2029 | helplessness
2030 | heresy
2031 | heretic
2032 | heretical
2033 | hesitant
2034 | hestitant
2035 | hideous
2036 | hideously
2037 | hideousness
2038 | high-priced
2039 | hiliarious
2040 | hinder
2041 | hindrance
2042 | hiss
2043 | hissed
2044 | hissing
2045 | ho-hum
2046 | hoard
2047 | hoax
2048 | hobble
2049 | hogs
2050 | hollow
2051 | hoodium
2052 | hoodwink
2053 | hooligan
2054 | hopeless
2055 | hopelessly
2056 | hopelessness
2057 | horde
2058 | horrendous
2059 | horrendously
2060 | horrible
2061 | horrid
2062 | horrific
2063 | horrified
2064 | horrifies
2065 | horrify
2066 | horrifying
2067 | horrifys
2068 | hostage
2069 | hostile
2070 | hostilities
2071 | hostility
2072 | hotbeds
2073 | hothead
2074 | hotheaded
2075 | hothouse
2076 | hubris
2077 | huckster
2078 | hum
2079 | humid
2080 | humiliate
2081 | humiliating
2082 | humiliation
2083 | humming
2084 | hung
2085 | hurt
2086 | hurted
2087 | hurtful
2088 | hurting
2089 | hurts
2090 | hustler
2091 | hype
2092 | hypocricy
2093 | hypocrisy
2094 | hypocrite
2095 | hypocrites
2096 | hypocritical
2097 | hypocritically
2098 | hysteria
2099 | hysteric
2100 | hysterical
2101 | hysterically
2102 | hysterics
2103 | idiocies
2104 | idiocy
2105 | idiot
2106 | idiotic
2107 | idiotically
2108 | idiots
2109 | idle
2110 | ignoble
2111 | ignominious
2112 | ignominiously
2113 | ignominy
2114 | ignorance
2115 | ignorant
2116 | ignore
2117 | ill-advised
2118 | ill-conceived
2119 | ill-defined
2120 | ill-designed
2121 | ill-fated
2122 | ill-favored
2123 | ill-formed
2124 | ill-mannered
2125 | ill-natured
2126 | ill-sorted
2127 | ill-tempered
2128 | ill-treated
2129 | ill-treatment
2130 | ill-usage
2131 | ill-used
2132 | illegal
2133 | illegally
2134 | illegitimate
2135 | illicit
2136 | illiterate
2137 | illness
2138 | illogic
2139 | illogical
2140 | illogically
2141 | illusion
2142 | illusions
2143 | illusory
2144 | imaginary
2145 | imbalance
2146 | imbecile
2147 | imbroglio
2148 | immaterial
2149 | immature
2150 | imminence
2151 | imminently
2152 | immobilized
2153 | immoderate
2154 | immoderately
2155 | immodest
2156 | immoral
2157 | immorality
2158 | immorally
2159 | immovable
2160 | impair
2161 | impaired
2162 | impasse
2163 | impatience
2164 | impatient
2165 | impatiently
2166 | impeach
2167 | impedance
2168 | impede
2169 | impediment
2170 | impending
2171 | impenitent
2172 | imperfect
2173 | imperfection
2174 | imperfections
2175 | imperfectly
2176 | imperialist
2177 | imperil
2178 | imperious
2179 | imperiously
2180 | impermissible
2181 | impersonal
2182 | impertinent
2183 | impetuous
2184 | impetuously
2185 | impiety
2186 | impinge
2187 | impious
2188 | implacable
2189 | implausible
2190 | implausibly
2191 | implicate
2192 | implication
2193 | implode
2194 | impolite
2195 | impolitely
2196 | impolitic
2197 | importunate
2198 | importune
2199 | impose
2200 | imposers
2201 | imposing
2202 | imposition
2203 | impossible
2204 | impossiblity
2205 | impossibly
2206 | impotent
2207 | impoverish
2208 | impoverished
2209 | impractical
2210 | imprecate
2211 | imprecise
2212 | imprecisely
2213 | imprecision
2214 | imprison
2215 | imprisonment
2216 | improbability
2217 | improbable
2218 | improbably
2219 | improper
2220 | improperly
2221 | impropriety
2222 | imprudence
2223 | imprudent
2224 | impudence
2225 | impudent
2226 | impudently
2227 | impugn
2228 | impulsive
2229 | impulsively
2230 | impunity
2231 | impure
2232 | impurity
2233 | inability
2234 | inaccuracies
2235 | inaccuracy
2236 | inaccurate
2237 | inaccurately
2238 | inaction
2239 | inactive
2240 | inadequacy
2241 | inadequate
2242 | inadequately
2243 | inadverent
2244 | inadverently
2245 | inadvisable
2246 | inadvisably
2247 | inane
2248 | inanely
2249 | inappropriate
2250 | inappropriately
2251 | inapt
2252 | inaptitude
2253 | inarticulate
2254 | inattentive
2255 | inaudible
2256 | incapable
2257 | incapably
2258 | incautious
2259 | incendiary
2260 | incense
2261 | incessant
2262 | incessantly
2263 | incite
2264 | incitement
2265 | incivility
2266 | inclement
2267 | incognizant
2268 | incoherence
2269 | incoherent
2270 | incoherently
2271 | incommensurate
2272 | incomparable
2273 | incomparably
2274 | incompatability
2275 | incompatibility
2276 | incompatible
2277 | incompetence
2278 | incompetent
2279 | incompetently
2280 | incomplete
2281 | incompliant
2282 | incomprehensible
2283 | incomprehension
2284 | inconceivable
2285 | inconceivably
2286 | incongruous
2287 | incongruously
2288 | inconsequent
2289 | inconsequential
2290 | inconsequentially
2291 | inconsequently
2292 | inconsiderate
2293 | inconsiderately
2294 | inconsistence
2295 | inconsistencies
2296 | inconsistency
2297 | inconsistent
2298 | inconsolable
2299 | inconsolably
2300 | inconstant
2301 | inconvenience
2302 | inconveniently
2303 | incorrect
2304 | incorrectly
2305 | incorrigible
2306 | incorrigibly
2307 | incredulous
2308 | incredulously
2309 | inculcate
2310 | indecency
2311 | indecent
2312 | indecently
2313 | indecision
2314 | indecisive
2315 | indecisively
2316 | indecorum
2317 | indefensible
2318 | indelicate
2319 | indeterminable
2320 | indeterminably
2321 | indeterminate
2322 | indifference
2323 | indifferent
2324 | indigent
2325 | indignant
2326 | indignantly
2327 | indignation
2328 | indignity
2329 | indiscernible
2330 | indiscreet
2331 | indiscreetly
2332 | indiscretion
2333 | indiscriminate
2334 | indiscriminately
2335 | indiscriminating
2336 | indistinguishable
2337 | indoctrinate
2338 | indoctrination
2339 | indolent
2340 | indulge
2341 | ineffective
2342 | ineffectively
2343 | ineffectiveness
2344 | ineffectual
2345 | ineffectually
2346 | ineffectualness
2347 | inefficacious
2348 | inefficacy
2349 | inefficiency
2350 | inefficient
2351 | inefficiently
2352 | inelegance
2353 | inelegant
2354 | ineligible
2355 | ineloquent
2356 | ineloquently
2357 | inept
2358 | ineptitude
2359 | ineptly
2360 | inequalities
2361 | inequality
2362 | inequitable
2363 | inequitably
2364 | inequities
2365 | inescapable
2366 | inescapably
2367 | inessential
2368 | inevitable
2369 | inevitably
2370 | inexcusable
2371 | inexcusably
2372 | inexorable
2373 | inexorably
2374 | inexperience
2375 | inexperienced
2376 | inexpert
2377 | inexpertly
2378 | inexpiable
2379 | inexplainable
2380 | inextricable
2381 | inextricably
2382 | infamous
2383 | infamously
2384 | infamy
2385 | infected
2386 | infection
2387 | infections
2388 | inferior
2389 | inferiority
2390 | infernal
2391 | infest
2392 | infested
2393 | infidel
2394 | infidels
2395 | infiltrator
2396 | infiltrators
2397 | infirm
2398 | inflame
2399 | inflammation
2400 | inflammatory
2401 | inflammed
2402 | inflated
2403 | inflationary
2404 | inflexible
2405 | inflict
2406 | infraction
2407 | infringe
2408 | infringement
2409 | infringements
2410 | infuriate
2411 | infuriated
2412 | infuriating
2413 | infuriatingly
2414 | inglorious
2415 | ingrate
2416 | ingratitude
2417 | inhibit
2418 | inhibition
2419 | inhospitable
2420 | inhospitality
2421 | inhuman
2422 | inhumane
2423 | inhumanity
2424 | inimical
2425 | inimically
2426 | iniquitous
2427 | iniquity
2428 | injudicious
2429 | injure
2430 | injurious
2431 | injury
2432 | injustice
2433 | injustices
2434 | innuendo
2435 | inoperable
2436 | inopportune
2437 | inordinate
2438 | inordinately
2439 | insane
2440 | insanely
2441 | insanity
2442 | insatiable
2443 | insecure
2444 | insecurity
2445 | insensible
2446 | insensitive
2447 | insensitively
2448 | insensitivity
2449 | insidious
2450 | insidiously
2451 | insignificance
2452 | insignificant
2453 | insignificantly
2454 | insincere
2455 | insincerely
2456 | insincerity
2457 | insinuate
2458 | insinuating
2459 | insinuation
2460 | insociable
2461 | insolence
2462 | insolent
2463 | insolently
2464 | insolvent
2465 | insouciance
2466 | instability
2467 | instable
2468 | instigate
2469 | instigator
2470 | instigators
2471 | insubordinate
2472 | insubstantial
2473 | insubstantially
2474 | insufferable
2475 | insufferably
2476 | insufficiency
2477 | insufficient
2478 | insufficiently
2479 | insular
2480 | insult
2481 | insulted
2482 | insulting
2483 | insultingly
2484 | insults
2485 | insupportable
2486 | insupportably
2487 | insurmountable
2488 | insurmountably
2489 | insurrection
2490 | intefere
2491 | inteferes
2492 | intense
2493 | interfere
2494 | interference
2495 | interferes
2496 | intermittent
2497 | interrupt
2498 | interruption
2499 | interruptions
2500 | intimidate
2501 | intimidating
2502 | intimidatingly
2503 | intimidation
2504 | intolerable
2505 | intolerablely
2506 | intolerance
2507 | intoxicate
2508 | intractable
2509 | intransigence
2510 | intransigent
2511 | intrude
2512 | intrusion
2513 | intrusive
2514 | inundate
2515 | inundated
2516 | invader
2517 | invalid
2518 | invalidate
2519 | invalidity
2520 | invasive
2521 | invective
2522 | inveigle
2523 | invidious
2524 | invidiously
2525 | invidiousness
2526 | invisible
2527 | involuntarily
2528 | involuntary
2529 | irascible
2530 | irate
2531 | irately
2532 | ire
2533 | irk
2534 | irked
2535 | irking
2536 | irks
2537 | irksome
2538 | irksomely
2539 | irksomeness
2540 | irksomenesses
2541 | ironic
2542 | ironical
2543 | ironically
2544 | ironies
2545 | irony
2546 | irragularity
2547 | irrational
2548 | irrationalities
2549 | irrationality
2550 | irrationally
2551 | irrationals
2552 | irreconcilable
2553 | irrecoverable
2554 | irrecoverableness
2555 | irrecoverablenesses
2556 | irrecoverably
2557 | irredeemable
2558 | irredeemably
2559 | irreformable
2560 | irregular
2561 | irregularity
2562 | irrelevance
2563 | irrelevant
2564 | irreparable
2565 | irreplacible
2566 | irrepressible
2567 | irresolute
2568 | irresolvable
2569 | irresponsible
2570 | irresponsibly
2571 | irretating
2572 | irretrievable
2573 | irreversible
2574 | irritable
2575 | irritably
2576 | irritant
2577 | irritate
2578 | irritated
2579 | irritating
2580 | irritation
2581 | irritations
2582 | isolate
2583 | isolated
2584 | isolation
2585 | issue
2586 | issues
2587 | itch
2588 | itching
2589 | itchy
2590 | jabber
2591 | jaded
2592 | jagged
2593 | jam
2594 | jarring
2595 | jaundiced
2596 | jealous
2597 | jealously
2598 | jealousness
2599 | jealousy
2600 | jeer
2601 | jeering
2602 | jeeringly
2603 | jeers
2604 | jeopardize
2605 | jeopardy
2606 | jerk
2607 | jerky
2608 | jitter
2609 | jitters
2610 | jittery
2611 | job-killing
2612 | jobless
2613 | joke
2614 | joker
2615 | jolt
2616 | judder
2617 | juddering
2618 | judders
2619 | jumpy
2620 | junk
2621 | junky
2622 | junkyard
2623 | jutter
2624 | jutters
2625 | kaput
2626 | kill
2627 | killed
2628 | killer
2629 | killing
2630 | killjoy
2631 | kills
2632 | knave
2633 | knife
2634 | knock
2635 | knotted
2636 | kook
2637 | kooky
2638 | lack
2639 | lackadaisical
2640 | lacked
2641 | lackey
2642 | lackeys
2643 | lacking
2644 | lackluster
2645 | lacks
2646 | laconic
2647 | lag
2648 | lagged
2649 | lagging
2650 | laggy
2651 | lags
2652 | laid-off
2653 | lambast
2654 | lambaste
2655 | lame
2656 | lame-duck
2657 | lament
2658 | lamentable
2659 | lamentably
2660 | languid
2661 | languish
2662 | languor
2663 | languorous
2664 | languorously
2665 | lanky
2666 | lapse
2667 | lapsed
2668 | lapses
2669 | lascivious
2670 | last-ditch
2671 | latency
2672 | laughable
2673 | laughably
2674 | laughingstock
2675 | lawbreaker
2676 | lawbreaking
2677 | lawless
2678 | lawlessness
2679 | layoff
2680 | layoff-happy
2681 | lazy
2682 | leak
2683 | leakage
2684 | leakages
2685 | leaking
2686 | leaks
2687 | leaky
2688 | lech
2689 | lecher
2690 | lecherous
2691 | lechery
2692 | leech
2693 | leer
2694 | leery
2695 | left-leaning
2696 | lemon
2697 | lengthy
2698 | less-developed
2699 | lesser-known
2700 | letch
2701 | lethal
2702 | lethargic
2703 | lethargy
2704 | lewd
2705 | lewdly
2706 | lewdness
2707 | liability
2708 | liable
2709 | liar
2710 | liars
2711 | licentious
2712 | licentiously
2713 | licentiousness
2714 | lie
2715 | lied
2716 | lier
2717 | lies
2718 | life-threatening
2719 | lifeless
2720 | limit
2721 | limitation
2722 | limitations
2723 | limited
2724 | limits
2725 | limp
2726 | listless
2727 | litigious
2728 | little-known
2729 | livid
2730 | lividly
2731 | loath
2732 | loathe
2733 | loathing
2734 | loathly
2735 | loathsome
2736 | loathsomely
2737 | lone
2738 | loneliness
2739 | lonely
2740 | loner
2741 | lonesome
2742 | long-time
2743 | long-winded
2744 | longing
2745 | longingly
2746 | loophole
2747 | loopholes
2748 | loose
2749 | loot
2750 | lorn
2751 | lose
2752 | loser
2753 | losers
2754 | loses
2755 | losing
2756 | loss
2757 | losses
2758 | lost
2759 | loud
2760 | louder
2761 | lousy
2762 | loveless
2763 | lovelorn
2764 | low-rated
2765 | lowly
2766 | ludicrous
2767 | ludicrously
2768 | lugubrious
2769 | lukewarm
2770 | lull
2771 | lumpy
2772 | lunatic
2773 | lunaticism
2774 | lurch
2775 | lure
2776 | lurid
2777 | lurk
2778 | lurking
2779 | lying
2780 | macabre
2781 | mad
2782 | madden
2783 | maddening
2784 | maddeningly
2785 | madder
2786 | madly
2787 | madman
2788 | madness
2789 | maladjusted
2790 | maladjustment
2791 | malady
2792 | malaise
2793 | malcontent
2794 | malcontented
2795 | maledict
2796 | malevolence
2797 | malevolent
2798 | malevolently
2799 | malice
2800 | malicious
2801 | maliciously
2802 | maliciousness
2803 | malign
2804 | malignant
2805 | malodorous
2806 | maltreatment
2807 | mangle
2808 | mangled
2809 | mangles
2810 | mangling
2811 | mania
2812 | maniac
2813 | maniacal
2814 | manic
2815 | manipulate
2816 | manipulation
2817 | manipulative
2818 | manipulators
2819 | mar
2820 | marginal
2821 | marginally
2822 | martyrdom
2823 | martyrdom-seeking
2824 | mashed
2825 | massacre
2826 | massacres
2827 | matte
2828 | mawkish
2829 | mawkishly
2830 | mawkishness
2831 | meager
2832 | meaningless
2833 | meanness
2834 | measly
2835 | meddle
2836 | meddlesome
2837 | mediocre
2838 | mediocrity
2839 | melancholy
2840 | melodramatic
2841 | melodramatically
2842 | meltdown
2843 | menace
2844 | menacing
2845 | menacingly
2846 | mendacious
2847 | mendacity
2848 | menial
2849 | merciless
2850 | mercilessly
2851 | mess
2852 | messed
2853 | messes
2854 | messing
2855 | messy
2856 | midget
2857 | miff
2858 | militancy
2859 | mindless
2860 | mindlessly
2861 | mirage
2862 | mire
2863 | misalign
2864 | misaligned
2865 | misaligns
2866 | misapprehend
2867 | misbecome
2868 | misbecoming
2869 | misbegotten
2870 | misbehave
2871 | misbehavior
2872 | miscalculate
2873 | miscalculation
2874 | miscellaneous
2875 | mischief
2876 | mischievous
2877 | mischievously
2878 | misconception
2879 | misconceptions
2880 | miscreant
2881 | miscreants
2882 | misdirection
2883 | miser
2884 | miserable
2885 | miserableness
2886 | miserably
2887 | miseries
2888 | miserly
2889 | misery
2890 | misfit
2891 | misfortune
2892 | misgiving
2893 | misgivings
2894 | misguidance
2895 | misguide
2896 | misguided
2897 | mishandle
2898 | mishap
2899 | misinform
2900 | misinformed
2901 | misinterpret
2902 | misjudge
2903 | misjudgment
2904 | mislead
2905 | misleading
2906 | misleadingly
2907 | mislike
2908 | mismanage
2909 | mispronounce
2910 | mispronounced
2911 | mispronounces
2912 | misread
2913 | misreading
2914 | misrepresent
2915 | misrepresentation
2916 | miss
2917 | missed
2918 | misses
2919 | misstatement
2920 | mist
2921 | mistake
2922 | mistaken
2923 | mistakenly
2924 | mistakes
2925 | mistified
2926 | mistress
2927 | mistrust
2928 | mistrustful
2929 | mistrustfully
2930 | mists
2931 | misunderstand
2932 | misunderstanding
2933 | misunderstandings
2934 | misunderstood
2935 | misuse
2936 | moan
2937 | mobster
2938 | mock
2939 | mocked
2940 | mockeries
2941 | mockery
2942 | mocking
2943 | mockingly
2944 | mocks
2945 | molest
2946 | molestation
2947 | monotonous
2948 | monotony
2949 | monster
2950 | monstrosities
2951 | monstrosity
2952 | monstrous
2953 | monstrously
2954 | moody
2955 | moot
2956 | mope
2957 | morbid
2958 | morbidly
2959 | mordant
2960 | mordantly
2961 | moribund
2962 | moron
2963 | moronic
2964 | morons
2965 | mortification
2966 | mortified
2967 | mortify
2968 | mortifying
2969 | motionless
2970 | motley
2971 | mourn
2972 | mourner
2973 | mournful
2974 | mournfully
2975 | muddle
2976 | muddy
2977 | mudslinger
2978 | mudslinging
2979 | mulish
2980 | multi-polarization
2981 | mundane
2982 | murder
2983 | murderer
2984 | murderous
2985 | murderously
2986 | murky
2987 | muscle-flexing
2988 | mushy
2989 | musty
2990 | mysterious
2991 | mysteriously
2992 | mystery
2993 | mystify
2994 | myth
2995 | nag
2996 | nagging
2997 | naive
2998 | naively
2999 | narrower
3000 | nastily
3001 | nastiness
3002 | nasty
3003 | naughty
3004 | nauseate
3005 | nauseates
3006 | nauseating
3007 | nauseatingly
3008 | na?ve
3009 | nebulous
3010 | nebulously
3011 | needless
3012 | needlessly
3013 | needy
3014 | nefarious
3015 | nefariously
3016 | negate
3017 | negation
3018 | negative
3019 | negatives
3020 | negativity
3021 | neglect
3022 | neglected
3023 | negligence
3024 | negligent
3025 | nemesis
3026 | nepotism
3027 | nervous
3028 | nervously
3029 | nervousness
3030 | nettle
3031 | nettlesome
3032 | neurotic
3033 | neurotically
3034 | niggle
3035 | niggles
3036 | nightmare
3037 | nightmarish
3038 | nightmarishly
3039 | nitpick
3040 | nitpicking
3041 | noise
3042 | noises
3043 | noisier
3044 | noisy
3045 | non-confidence
3046 | nonexistent
3047 | nonresponsive
3048 | nonsense
3049 | nosey
3050 | notoriety
3051 | notorious
3052 | notoriously
3053 | noxious
3054 | nuisance
3055 | numb
3056 | obese
3057 | object
3058 | objection
3059 | objectionable
3060 | objections
3061 | oblique
3062 | obliterate
3063 | obliterated
3064 | oblivious
3065 | obnoxious
3066 | obnoxiously
3067 | obscene
3068 | obscenely
3069 | obscenity
3070 | obscure
3071 | obscured
3072 | obscures
3073 | obscurity
3074 | obsess
3075 | obsessive
3076 | obsessively
3077 | obsessiveness
3078 | obsolete
3079 | obstacle
3080 | obstinate
3081 | obstinately
3082 | obstruct
3083 | obstructed
3084 | obstructing
3085 | obstruction
3086 | obstructs
3087 | obtrusive
3088 | obtuse
3089 | occlude
3090 | occluded
3091 | occludes
3092 | occluding
3093 | odd
3094 | odder
3095 | oddest
3096 | oddities
3097 | oddity
3098 | oddly
3099 | odor
3100 | offence
3101 | offend
3102 | offender
3103 | offending
3104 | offenses
3105 | offensive
3106 | offensively
3107 | offensiveness
3108 | officious
3109 | ominous
3110 | ominously
3111 | omission
3112 | omit
3113 | one-sided
3114 | onerous
3115 | onerously
3116 | onslaught
3117 | opinionated
3118 | opponent
3119 | opportunistic
3120 | oppose
3121 | opposition
3122 | oppositions
3123 | oppress
3124 | oppression
3125 | oppressive
3126 | oppressively
3127 | oppressiveness
3128 | oppressors
3129 | ordeal
3130 | orphan
3131 | ostracize
3132 | outbreak
3133 | outburst
3134 | outbursts
3135 | outcast
3136 | outcry
3137 | outlaw
3138 | outmoded
3139 | outrage
3140 | outraged
3141 | outrageous
3142 | outrageously
3143 | outrageousness
3144 | outrages
3145 | outsider
3146 | over-acted
3147 | over-awe
3148 | over-balanced
3149 | over-hyped
3150 | over-priced
3151 | over-valuation
3152 | overact
3153 | overacted
3154 | overawe
3155 | overbalance
3156 | overbalanced
3157 | overbearing
3158 | overbearingly
3159 | overblown
3160 | overdo
3161 | overdone
3162 | overdue
3163 | overemphasize
3164 | overheat
3165 | overkill
3166 | overloaded
3167 | overlook
3168 | overpaid
3169 | overpayed
3170 | overplay
3171 | overpower
3172 | overpriced
3173 | overrated
3174 | overreach
3175 | overrun
3176 | overshadow
3177 | oversight
3178 | oversights
3179 | oversimplification
3180 | oversimplified
3181 | oversimplify
3182 | oversize
3183 | overstate
3184 | overstated
3185 | overstatement
3186 | overstatements
3187 | overstates
3188 | overtaxed
3189 | overthrow
3190 | overthrows
3191 | overturn
3192 | overweight
3193 | overwhelm
3194 | overwhelmed
3195 | overwhelming
3196 | overwhelmingly
3197 | overwhelms
3198 | overzealous
3199 | overzealously
3200 | overzelous
3201 | pain
3202 | painful
3203 | painfull
3204 | painfully
3205 | pains
3206 | pale
3207 | pales
3208 | paltry
3209 | pan
3210 | pandemonium
3211 | pander
3212 | pandering
3213 | panders
3214 | panic
3215 | panick
3216 | panicked
3217 | panicking
3218 | panicky
3219 | paradoxical
3220 | paradoxically
3221 | paralize
3222 | paralyzed
3223 | paranoia
3224 | paranoid
3225 | parasite
3226 | pariah
3227 | parody
3228 | partiality
3229 | partisan
3230 | partisans
3231 | passe
3232 | passive
3233 | passiveness
3234 | pathetic
3235 | pathetically
3236 | patronize
3237 | paucity
3238 | pauper
3239 | paupers
3240 | payback
3241 | peculiar
3242 | peculiarly
3243 | pedantic
3244 | peeled
3245 | peeve
3246 | peeved
3247 | peevish
3248 | peevishly
3249 | penalize
3250 | penalty
3251 | perfidious
3252 | perfidity
3253 | perfunctory
3254 | peril
3255 | perilous
3256 | perilously
3257 | perish
3258 | pernicious
3259 | perplex
3260 | perplexed
3261 | perplexing
3262 | perplexity
3263 | persecute
3264 | persecution
3265 | pertinacious
3266 | pertinaciously
3267 | pertinacity
3268 | perturb
3269 | perturbed
3270 | pervasive
3271 | perverse
3272 | perversely
3273 | perversion
3274 | perversity
3275 | pervert
3276 | perverted
3277 | perverts
3278 | pessimism
3279 | pessimistic
3280 | pessimistically
3281 | pest
3282 | pestilent
3283 | petrified
3284 | petrify
3285 | pettifog
3286 | petty
3287 | phobia
3288 | phobic
3289 | phony
3290 | picket
3291 | picketed
3292 | picketing
3293 | pickets
3294 | picky
3295 | pig
3296 | pigs
3297 | pillage
3298 | pillory
3299 | pimple
3300 | pinch
3301 | pique
3302 | pitiable
3303 | pitiful
3304 | pitifully
3305 | pitiless
3306 | pitilessly
3307 | pittance
3308 | pity
3309 | plagiarize
3310 | plague
3311 | plasticky
3312 | plaything
3313 | plea
3314 | pleas
3315 | plebeian
3316 | plight
3317 | plot
3318 | plotters
3319 | ploy
3320 | plunder
3321 | plunderer
3322 | pointless
3323 | pointlessly
3324 | poison
3325 | poisonous
3326 | poisonously
3327 | pokey
3328 | poky
3329 | polarisation
3330 | polemize
3331 | pollute
3332 | polluter
3333 | polluters
3334 | polution
3335 | pompous
3336 | poor
3337 | poorer
3338 | poorest
3339 | poorly
3340 | posturing
3341 | pout
3342 | poverty
3343 | powerless
3344 | prate
3345 | pratfall
3346 | prattle
3347 | precarious
3348 | precariously
3349 | precipitate
3350 | precipitous
3351 | predatory
3352 | predicament
3353 | prejudge
3354 | prejudice
3355 | prejudices
3356 | prejudicial
3357 | premeditated
3358 | preoccupy
3359 | preposterous
3360 | preposterously
3361 | presumptuous
3362 | presumptuously
3363 | pretence
3364 | pretend
3365 | pretense
3366 | pretentious
3367 | pretentiously
3368 | prevaricate
3369 | pricey
3370 | pricier
3371 | prick
3372 | prickle
3373 | prickles
3374 | prideful
3375 | prik
3376 | primitive
3377 | prison
3378 | prisoner
3379 | problem
3380 | problematic
3381 | problems
3382 | procrastinate
3383 | procrastinates
3384 | procrastination
3385 | profane
3386 | profanity
3387 | prohibit
3388 | prohibitive
3389 | prohibitively
3390 | propaganda
3391 | propagandize
3392 | proprietary
3393 | prosecute
3394 | protest
3395 | protested
3396 | protesting
3397 | protests
3398 | protracted
3399 | provocation
3400 | provocative
3401 | provoke
3402 | pry
3403 | pugnacious
3404 | pugnaciously
3405 | pugnacity
3406 | punch
3407 | punish
3408 | punishable
3409 | punitive
3410 | punk
3411 | puny
3412 | puppet
3413 | puppets
3414 | puzzled
3415 | puzzlement
3416 | puzzling
3417 | quack
3418 | qualm
3419 | qualms
3420 | quandary
3421 | quarrel
3422 | quarrellous
3423 | quarrellously
3424 | quarrels
3425 | quarrelsome
3426 | quash
3427 | queer
3428 | questionable
3429 | quibble
3430 | quibbles
3431 | quitter
3432 | rabid
3433 | racism
3434 | racist
3435 | racists
3436 | racy
3437 | radical
3438 | radicalization
3439 | radically
3440 | radicals
3441 | rage
3442 | ragged
3443 | raging
3444 | rail
3445 | raked
3446 | rampage
3447 | rampant
3448 | ramshackle
3449 | rancor
3450 | randomly
3451 | rankle
3452 | rant
3453 | ranted
3454 | ranting
3455 | rantingly
3456 | rants
3457 | rape
3458 | raped
3459 | raping
3460 | rascal
3461 | rascals
3462 | rash
3463 | rattle
3464 | rattled
3465 | rattles
3466 | ravage
3467 | raving
3468 | reactionary
3469 | rebellious
3470 | rebuff
3471 | rebuke
3472 | recalcitrant
3473 | recant
3474 | recession
3475 | recessionary
3476 | reckless
3477 | recklessly
3478 | recklessness
3479 | recoil
3480 | recourses
3481 | redundancy
3482 | redundant
3483 | refusal
3484 | refuse
3485 | refused
3486 | refuses
3487 | refusing
3488 | refutation
3489 | refute
3490 | refuted
3491 | refutes
3492 | refuting
3493 | regress
3494 | regression
3495 | regressive
3496 | regret
3497 | regreted
3498 | regretful
3499 | regretfully
3500 | regrets
3501 | regrettable
3502 | regrettably
3503 | regretted
3504 | reject
3505 | rejected
3506 | rejecting
3507 | rejection
3508 | rejects
3509 | relapse
3510 | relentless
3511 | relentlessly
3512 | relentlessness
3513 | reluctance
3514 | reluctant
3515 | reluctantly
3516 | remorse
3517 | remorseful
3518 | remorsefully
3519 | remorseless
3520 | remorselessly
3521 | remorselessness
3522 | renounce
3523 | renunciation
3524 | repel
3525 | repetitive
3526 | reprehensible
3527 | reprehensibly
3528 | reprehension
3529 | reprehensive
3530 | repress
3531 | repression
3532 | repressive
3533 | reprimand
3534 | reproach
3535 | reproachful
3536 | reprove
3537 | reprovingly
3538 | repudiate
3539 | repudiation
3540 | repugn
3541 | repugnance
3542 | repugnant
3543 | repugnantly
3544 | repulse
3545 | repulsed
3546 | repulsing
3547 | repulsive
3548 | repulsively
3549 | repulsiveness
3550 | resent
3551 | resentful
3552 | resentment
3553 | resignation
3554 | resigned
3555 | resistance
3556 | restless
3557 | restlessness
3558 | restrict
3559 | restricted
3560 | restriction
3561 | restrictive
3562 | resurgent
3563 | retaliate
3564 | retaliatory
3565 | retard
3566 | retarded
3567 | retardedness
3568 | retards
3569 | reticent
3570 | retract
3571 | retreat
3572 | retreated
3573 | revenge
3574 | revengeful
3575 | revengefully
3576 | revert
3577 | revile
3578 | reviled
3579 | revoke
3580 | revolt
3581 | revolting
3582 | revoltingly
3583 | revulsion
3584 | revulsive
3585 | rhapsodize
3586 | rhetoric
3587 | rhetorical
3588 | ricer
3589 | ridicule
3590 | ridicules
3591 | ridiculous
3592 | ridiculously
3593 | rife
3594 | rift
3595 | rifts
3596 | rigid
3597 | rigidity
3598 | rigidness
3599 | rile
3600 | riled
3601 | rip
3602 | rip-off
3603 | ripoff
3604 | ripped
3605 | risk
3606 | risks
3607 | risky
3608 | rival
3609 | rivalry
3610 | roadblocks
3611 | rocky
3612 | rogue
3613 | rollercoaster
3614 | rot
3615 | rotten
3616 | rough
3617 | rremediable
3618 | rubbish
3619 | rude
3620 | rue
3621 | ruffian
3622 | ruffle
3623 | ruin
3624 | ruined
3625 | ruining
3626 | ruinous
3627 | ruins
3628 | rumbling
3629 | rumor
3630 | rumors
3631 | rumours
3632 | rumple
3633 | run-down
3634 | runaway
3635 | rupture
3636 | rust
3637 | rusts
3638 | rusty
3639 | rut
3640 | ruthless
3641 | ruthlessly
3642 | ruthlessness
3643 | ruts
3644 | sabotage
3645 | sack
3646 | sacrificed
3647 | sad
3648 | sadden
3649 | sadly
3650 | sadness
3651 | sag
3652 | sagged
3653 | sagging
3654 | saggy
3655 | sags
3656 | salacious
3657 | sanctimonious
3658 | sap
3659 | sarcasm
3660 | sarcastic
3661 | sarcastically
3662 | sardonic
3663 | sardonically
3664 | sass
3665 | satirical
3666 | satirize
3667 | savage
3668 | savaged
3669 | savagery
3670 | savages
3671 | scaly
3672 | scam
3673 | scams
3674 | scandal
3675 | scandalize
3676 | scandalized
3677 | scandalous
3678 | scandalously
3679 | scandals
3680 | scandel
3681 | scandels
3682 | scant
3683 | scapegoat
3684 | scar
3685 | scarce
3686 | scarcely
3687 | scarcity
3688 | scare
3689 | scared
3690 | scarier
3691 | scariest
3692 | scarily
3693 | scarred
3694 | scars
3695 | scary
3696 | scathing
3697 | scathingly
3698 | sceptical
3699 | scoff
3700 | scoffingly
3701 | scold
3702 | scolded
3703 | scolding
3704 | scoldingly
3705 | scorching
3706 | scorchingly
3707 | scorn
3708 | scornful
3709 | scornfully
3710 | scoundrel
3711 | scourge
3712 | scowl
3713 | scramble
3714 | scrambled
3715 | scrambles
3716 | scrambling
3717 | scrap
3718 | scratch
3719 | scratched
3720 | scratches
3721 | scratchy
3722 | scream
3723 | screech
3724 | screw-up
3725 | screwed
3726 | screwed-up
3727 | screwy
3728 | scuff
3729 | scuffs
3730 | scum
3731 | scummy
3732 | second-class
3733 | second-tier
3734 | secretive
3735 | sedentary
3736 | seedy
3737 | seethe
3738 | seething
3739 | self-coup
3740 | self-criticism
3741 | self-defeating
3742 | self-destructive
3743 | self-humiliation
3744 | self-interest
3745 | self-interested
3746 | self-serving
3747 | selfinterested
3748 | selfish
3749 | selfishly
3750 | selfishness
3751 | semi-retarded
3752 | senile
3753 | sensationalize
3754 | senseless
3755 | senselessly
3756 | seriousness
3757 | sermonize
3758 | servitude
3759 | set-up
3760 | setback
3761 | setbacks
3762 | sever
3763 | severe
3764 | severity
3765 | sh*t
3766 | shabby
3767 | shadowy
3768 | shady
3769 | shake
3770 | shaky
3771 | shallow
3772 | sham
3773 | shambles
3774 | shame
3775 | shameful
3776 | shamefully
3777 | shamefulness
3778 | shameless
3779 | shamelessly
3780 | shamelessness
3781 | shark
3782 | sharply
3783 | shatter
3784 | shemale
3785 | shimmer
3786 | shimmy
3787 | shipwreck
3788 | shirk
3789 | shirker
3790 | shit
3791 | shiver
3792 | shock
3793 | shocked
3794 | shocking
3795 | shockingly
3796 | shoddy
3797 | short-lived
3798 | shortage
3799 | shortchange
3800 | shortcoming
3801 | shortcomings
3802 | shortness
3803 | shortsighted
3804 | shortsightedness
3805 | showdown
3806 | shrew
3807 | shriek
3808 | shrill
3809 | shrilly
3810 | shrivel
3811 | shroud
3812 | shrouded
3813 | shrug
3814 | shun
3815 | shunned
3816 | sick
3817 | sicken
3818 | sickening
3819 | sickeningly
3820 | sickly
3821 | sickness
3822 | sidetrack
3823 | sidetracked
3824 | siege
3825 | sillily
3826 | silly
3827 | simplistic
3828 | simplistically
3829 | sin
3830 | sinful
3831 | sinfully
3832 | sinister
3833 | sinisterly
3834 | sink
3835 | sinking
3836 | skeletons
3837 | skeptic
3838 | skeptical
3839 | skeptically
3840 | skepticism
3841 | sketchy
3842 | skimpy
3843 | skinny
3844 | skittish
3845 | skittishly
3846 | skulk
3847 | slack
3848 | slander
3849 | slanderer
3850 | slanderous
3851 | slanderously
3852 | slanders
3853 | slap
3854 | slashing
3855 | slaughter
3856 | slaughtered
3857 | slave
3858 | slaves
3859 | sleazy
3860 | slime
3861 | slog
3862 | slogged
3863 | slogging
3864 | slogs
3865 | sloooooooooooooow
3866 | sloooow
3867 | slooow
3868 | sloow
3869 | sloppily
3870 | sloppy
3871 | sloth
3872 | slothful
3873 | slow
3874 | slow-moving
3875 | slowed
3876 | slower
3877 | slowest
3878 | slowly
3879 | sloww
3880 | slowww
3881 | slowwww
3882 | slug
3883 | sluggish
3884 | slump
3885 | slumping
3886 | slumpping
3887 | slur
3888 | slut
3889 | sluts
3890 | sly
3891 | smack
3892 | smallish
3893 | smash
3894 | smear
3895 | smell
3896 | smelled
3897 | smelling
3898 | smells
3899 | smelly
3900 | smelt
3901 | smoke
3902 | smokescreen
3903 | smolder
3904 | smoldering
3905 | smother
3906 | smoulder
3907 | smouldering
3908 | smudge
3909 | smudged
3910 | smudges
3911 | smudging
3912 | smug
3913 | smugly
3914 | smut
3915 | smuttier
3916 | smuttiest
3917 | smutty
3918 | snag
3919 | snagged
3920 | snagging
3921 | snags
3922 | snappish
3923 | snappishly
3924 | snare
3925 | snarky
3926 | snarl
3927 | sneak
3928 | sneakily
3929 | sneaky
3930 | sneer
3931 | sneering
3932 | sneeringly
3933 | snob
3934 | snobbish
3935 | snobby
3936 | snobish
3937 | snobs
3938 | snub
3939 | so-cal
3940 | soapy
3941 | sob
3942 | sober
3943 | sobering
3944 | solemn
3945 | solicitude
3946 | somber
3947 | sore
3948 | sorely
3949 | soreness
3950 | sorrow
3951 | sorrowful
3952 | sorrowfully
3953 | sorry
3954 | sour
3955 | sourly
3956 | spade
3957 | spank
3958 | spendy
3959 | spew
3960 | spewed
3961 | spewing
3962 | spews
3963 | spilling
3964 | spinster
3965 | spiritless
3966 | spite
3967 | spiteful
3968 | spitefully
3969 | spitefulness
3970 | splatter
3971 | split
3972 | splitting
3973 | spoil
3974 | spoilage
3975 | spoilages
3976 | spoiled
3977 | spoilled
3978 | spoils
3979 | spook
3980 | spookier
3981 | spookiest
3982 | spookily
3983 | spooky
3984 | spoon-fed
3985 | spoon-feed
3986 | spoonfed
3987 | sporadic
3988 | spotty
3989 | spurious
3990 | spurn
3991 | sputter
3992 | squabble
3993 | squabbling
3994 | squander
3995 | squash
3996 | squeak
3997 | squeaks
3998 | squeaky
3999 | squeal
4000 | squealing
4001 | squeals
4002 | squirm
4003 | stab
4004 | stagnant
4005 | stagnate
4006 | stagnation
4007 | staid
4008 | stain
4009 | stains
4010 | stale
4011 | stalemate
4012 | stall
4013 | stalls
4014 | stammer
4015 | stampede
4016 | standstill
4017 | stark
4018 | starkly
4019 | startle
4020 | startling
4021 | startlingly
4022 | starvation
4023 | starve
4024 | static
4025 | steal
4026 | stealing
4027 | steals
4028 | steep
4029 | steeply
4030 | stench
4031 | stereotype
4032 | stereotypical
4033 | stereotypically
4034 | stern
4035 | stew
4036 | sticky
4037 | stiff
4038 | stiffness
4039 | stifle
4040 | stifling
4041 | stiflingly
4042 | stigma
4043 | stigmatize
4044 | sting
4045 | stinging
4046 | stingingly
4047 | stingy
4048 | stink
4049 | stinks
4050 | stodgy
4051 | stole
4052 | stolen
4053 | stooge
4054 | stooges
4055 | stormy
4056 | straggle
4057 | straggler
4058 | strain
4059 | strained
4060 | straining
4061 | strange
4062 | strangely
4063 | stranger
4064 | strangest
4065 | strangle
4066 | streaky
4067 | strenuous
4068 | stress
4069 | stresses
4070 | stressful
4071 | stressfully
4072 | stricken
4073 | strict
4074 | strictly
4075 | strident
4076 | stridently
4077 | strife
4078 | strike
4079 | stringent
4080 | stringently
4081 | struck
4082 | struggle
4083 | struggled
4084 | struggles
4085 | struggling
4086 | strut
4087 | stubborn
4088 | stubbornly
4089 | stubbornness
4090 | stuck
4091 | stuffy
4092 | stumble
4093 | stumbled
4094 | stumbles
4095 | stump
4096 | stumped
4097 | stumps
4098 | stun
4099 | stunt
4100 | stunted
4101 | stupid
4102 | stupidest
4103 | stupidity
4104 | stupidly
4105 | stupified
4106 | stupify
4107 | stupor
4108 | stutter
4109 | stuttered
4110 | stuttering
4111 | stutters
4112 | sty
4113 | stymied
4114 | sub-par
4115 | subdued
4116 | subjected
4117 | subjection
4118 | subjugate
4119 | subjugation
4120 | submissive
4121 | subordinate
4122 | subpoena
4123 | subpoenas
4124 | subservience
4125 | subservient
4126 | substandard
4127 | subtract
4128 | subversion
4129 | subversive
4130 | subversively
4131 | subvert
4132 | succumb
4133 | suck
4134 | sucked
4135 | sucker
4136 | sucks
4137 | sucky
4138 | sue
4139 | sued
4140 | sueing
4141 | sues
4142 | suffer
4143 | suffered
4144 | sufferer
4145 | sufferers
4146 | suffering
4147 | suffers
4148 | suffocate
4149 | sugar-coat
4150 | sugar-coated
4151 | sugarcoated
4152 | suicidal
4153 | suicide
4154 | sulk
4155 | sullen
4156 | sully
4157 | sunder
4158 | sunk
4159 | sunken
4160 | superficial
4161 | superficiality
4162 | superficially
4163 | superfluous
4164 | superstition
4165 | superstitious
4166 | suppress
4167 | suppression
4168 | surrender
4169 | susceptible
4170 | suspect
4171 | suspicion
4172 | suspicions
4173 | suspicious
4174 | suspiciously
4175 | swagger
4176 | swamped
4177 | sweaty
4178 | swelled
4179 | swelling
4180 | swindle
4181 | swipe
4182 | swollen
4183 | symptom
4184 | symptoms
4185 | syndrome
4186 | taboo
4187 | tacky
4188 | taint
4189 | tainted
4190 | tamper
4191 | tangle
4192 | tangled
4193 | tangles
4194 | tank
4195 | tanked
4196 | tanks
4197 | tantrum
4198 | tardy
4199 | tarnish
4200 | tarnished
4201 | tarnishes
4202 | tarnishing
4203 | tattered
4204 | taunt
4205 | taunting
4206 | tauntingly
4207 | taunts
4208 | taut
4209 | tawdry
4210 | taxing
4211 | tease
4212 | teasingly
4213 | tedious
4214 | tediously
4215 | temerity
4216 | temper
4217 | tempest
4218 | temptation
4219 | tenderness
4220 | tense
4221 | tension
4222 | tentative
4223 | tentatively
4224 | tenuous
4225 | tenuously
4226 | tepid
4227 | terrible
4228 | terribleness
4229 | terribly
4230 | terror
4231 | terror-genic
4232 | terrorism
4233 | terrorize
4234 | testily
4235 | testy
4236 | tetchily
4237 | tetchy
4238 | thankless
4239 | thicker
4240 | thirst
4241 | thorny
4242 | thoughtless
4243 | thoughtlessly
4244 | thoughtlessness
4245 | thrash
4246 | threat
4247 | threaten
4248 | threatening
4249 | threats
4250 | threesome
4251 | throb
4252 | throbbed
4253 | throbbing
4254 | throbs
4255 | throttle
4256 | thug
4257 | thumb-down
4258 | thumbs-down
4259 | thwart
4260 | time-consuming
4261 | timid
4262 | timidity
4263 | timidly
4264 | timidness
4265 | tin-y
4266 | tingled
4267 | tingling
4268 | tired
4269 | tiresome
4270 | tiring
4271 | tiringly
4272 | toil
4273 | toll
4274 | top-heavy
4275 | topple
4276 | torment
4277 | tormented
4278 | torrent
4279 | tortuous
4280 | torture
4281 | tortured
4282 | tortures
4283 | torturing
4284 | torturous
4285 | torturously
4286 | totalitarian
4287 | touchy
4288 | toughness
4289 | tout
4290 | touted
4291 | touts
4292 | toxic
4293 | traduce
4294 | tragedy
4295 | tragic
4296 | tragically
4297 | traitor
4298 | traitorous
4299 | traitorously
4300 | tramp
4301 | trample
4302 | transgress
4303 | transgression
4304 | trap
4305 | traped
4306 | trapped
4307 | trash
4308 | trashed
4309 | trashy
4310 | trauma
4311 | traumatic
4312 | traumatically
4313 | traumatize
4314 | traumatized
4315 | travesties
4316 | travesty
4317 | treacherous
4318 | treacherously
4319 | treachery
4320 | treason
4321 | treasonous
4322 | trick
4323 | tricked
4324 | trickery
4325 | tricky
4326 | trivial
4327 | trivialize
4328 | trouble
4329 | troubled
4330 | troublemaker
4331 | troubles
4332 | troublesome
4333 | troublesomely
4334 | troubling
4335 | troublingly
4336 | truant
4337 | tumble
4338 | tumbled
4339 | tumbles
4340 | tumultuous
4341 | turbulent
4342 | turmoil
4343 | twist
4344 | twisted
4345 | twists
4346 | two-faced
4347 | two-faces
4348 | tyrannical
4349 | tyrannically
4350 | tyranny
4351 | tyrant
4352 | ugh
4353 | uglier
4354 | ugliest
4355 | ugliness
4356 | ugly
4357 | ulterior
4358 | ultimatum
4359 | ultimatums
4360 | ultra-hardline
4361 | un-viewable
4362 | unable
4363 | unacceptable
4364 | unacceptablely
4365 | unacceptably
4366 | unaccessible
4367 | unaccustomed
4368 | unachievable
4369 | unaffordable
4370 | unappealing
4371 | unattractive
4372 | unauthentic
4373 | unavailable
4374 | unavoidably
4375 | unbearable
4376 | unbearablely
4377 | unbelievable
4378 | unbelievably
4379 | uncaring
4380 | uncertain
4381 | uncivil
4382 | uncivilized
4383 | unclean
4384 | unclear
4385 | uncollectible
4386 | uncomfortable
4387 | uncomfortably
4388 | uncomfy
4389 | uncompetitive
4390 | uncompromising
4391 | uncompromisingly
4392 | unconfirmed
4393 | unconstitutional
4394 | uncontrolled
4395 | unconvincing
4396 | unconvincingly
4397 | uncooperative
4398 | uncouth
4399 | uncreative
4400 | undecided
4401 | undefined
4402 | undependability
4403 | undependable
4404 | undercut
4405 | undercuts
4406 | undercutting
4407 | underdog
4408 | underestimate
4409 | underlings
4410 | undermine
4411 | undermined
4412 | undermines
4413 | undermining
4414 | underpaid
4415 | underpowered
4416 | undersized
4417 | undesirable
4418 | undetermined
4419 | undid
4420 | undignified
4421 | undissolved
4422 | undocumented
4423 | undone
4424 | undue
4425 | unease
4426 | uneasily
4427 | uneasiness
4428 | uneasy
4429 | uneconomical
4430 | unemployed
4431 | unequal
4432 | unethical
4433 | uneven
4434 | uneventful
4435 | unexpected
4436 | unexpectedly
4437 | unexplained
4438 | unfairly
4439 | unfaithful
4440 | unfaithfully
4441 | unfamiliar
4442 | unfavorable
4443 | unfeeling
4444 | unfinished
4445 | unfit
4446 | unforeseen
4447 | unforgiving
4448 | unfortunate
4449 | unfortunately
4450 | unfounded
4451 | unfriendly
4452 | unfulfilled
4453 | unfunded
4454 | ungovernable
4455 | ungrateful
4456 | unhappily
4457 | unhappiness
4458 | unhappy
4459 | unhealthy
4460 | unhelpful
4461 | unilateralism
4462 | unimaginable
4463 | unimaginably
4464 | unimportant
4465 | uninformed
4466 | uninsured
4467 | unintelligible
4468 | unintelligile
4469 | unipolar
4470 | unjust
4471 | unjustifiable
4472 | unjustifiably
4473 | unjustified
4474 | unjustly
4475 | unkind
4476 | unkindly
4477 | unknown
4478 | unlamentable
4479 | unlamentably
4480 | unlawful
4481 | unlawfully
4482 | unlawfulness
4483 | unleash
4484 | unlicensed
4485 | unlikely
4486 | unlucky
4487 | unmoved
4488 | unnatural
4489 | unnaturally
4490 | unnecessary
4491 | unneeded
4492 | unnerve
4493 | unnerved
4494 | unnerving
4495 | unnervingly
4496 | unnoticed
4497 | unobserved
4498 | unorthodox
4499 | unorthodoxy
4500 | unpleasant
4501 | unpleasantries
4502 | unpopular
4503 | unpredictable
4504 | unprepared
4505 | unproductive
4506 | unprofitable
4507 | unprove
4508 | unproved
4509 | unproven
4510 | unproves
4511 | unproving
4512 | unqualified
4513 | unravel
4514 | unraveled
4515 | unreachable
4516 | unreadable
4517 | unrealistic
4518 | unreasonable
4519 | unreasonably
4520 | unrelenting
4521 | unrelentingly
4522 | unreliability
4523 | unreliable
4524 | unresolved
4525 | unresponsive
4526 | unrest
4527 | unruly
4528 | unsafe
4529 | unsatisfactory
4530 | unsavory
4531 | unscrupulous
4532 | unscrupulously
4533 | unsecure
4534 | unseemly
4535 | unsettle
4536 | unsettled
4537 | unsettling
4538 | unsettlingly
4539 | unskilled
4540 | unsophisticated
4541 | unsound
4542 | unspeakable
4543 | unspeakablely
4544 | unspecified
4545 | unstable
4546 | unsteadily
4547 | unsteadiness
4548 | unsteady
4549 | unsuccessful
4550 | unsuccessfully
4551 | unsupported
4552 | unsupportive
4553 | unsure
4554 | unsuspecting
4555 | unsustainable
4556 | untenable
4557 | untested
4558 | unthinkable
4559 | unthinkably
4560 | untimely
4561 | untouched
4562 | untrue
4563 | untrustworthy
4564 | untruthful
4565 | unusable
4566 | unusably
4567 | unuseable
4568 | unuseably
4569 | unusual
4570 | unusually
4571 | unviewable
4572 | unwanted
4573 | unwarranted
4574 | unwatchable
4575 | unwelcome
4576 | unwell
4577 | unwieldy
4578 | unwilling
4579 | unwillingly
4580 | unwillingness
4581 | unwise
4582 | unwisely
4583 | unworkable
4584 | unworthy
4585 | unyielding
4586 | upbraid
4587 | upheaval
4588 | uprising
4589 | uproar
4590 | uproarious
4591 | uproariously
4592 | uproarous
4593 | uproarously
4594 | uproot
4595 | upset
4596 | upseting
4597 | upsets
4598 | upsetting
4599 | upsettingly
4600 | urgent
4601 | useless
4602 | usurp
4603 | usurper
4604 | utterly
4605 | vagrant
4606 | vague
4607 | vagueness
4608 | vain
4609 | vainly
4610 | vanity
4611 | vehement
4612 | vehemently
4613 | vengeance
4614 | vengeful
4615 | vengefully
4616 | vengefulness
4617 | venom
4618 | venomous
4619 | venomously
4620 | vent
4621 | vestiges
4622 | vex
4623 | vexation
4624 | vexing
4625 | vexingly
4626 | vibrate
4627 | vibrated
4628 | vibrates
4629 | vibrating
4630 | vibration
4631 | vice
4632 | vicious
4633 | viciously
4634 | viciousness
4635 | victimize
4636 | vile
4637 | vileness
4638 | vilify
4639 | villainous
4640 | villainously
4641 | villains
4642 | villian
4643 | villianous
4644 | villianously
4645 | villify
4646 | vindictive
4647 | vindictively
4648 | vindictiveness
4649 | violate
4650 | violation
4651 | violator
4652 | violators
4653 | violent
4654 | violently
4655 | viper
4656 | virulence
4657 | virulent
4658 | virulently
4659 | virus
4660 | vociferous
4661 | vociferously
4662 | volatile
4663 | volatility
4664 | vomit
4665 | vomited
4666 | vomiting
4667 | vomits
4668 | vulgar
4669 | vulnerable
4670 | wack
4671 | wail
4672 | wallow
4673 | wane
4674 | waning
4675 | wanton
4676 | war-like
4677 | warily
4678 | wariness
4679 | warlike
4680 | warned
4681 | warning
4682 | warp
4683 | warped
4684 | wary
4685 | washed-out
4686 | waste
4687 | wasted
4688 | wasteful
4689 | wastefulness
4690 | wasting
4691 | water-down
4692 | watered-down
4693 | wayward
4694 | weak
4695 | weaken
4696 | weakening
4697 | weaker
4698 | weakness
4699 | weaknesses
4700 | weariness
4701 | wearisome
4702 | weary
4703 | wedge
4704 | weed
4705 | weep
4706 | weird
4707 | weirdly
4708 | wheedle
4709 | whimper
4710 | whine
4711 | whining
4712 | whiny
4713 | whips
4714 | whore
4715 | whores
4716 | wicked
4717 | wickedly
4718 | wickedness
4719 | wild
4720 | wildly
4721 | wiles
4722 | wilt
4723 | wily
4724 | wimpy
4725 | wince
4726 | wobble
4727 | wobbled
4728 | wobbles
4729 | woe
4730 | woebegone
4731 | woeful
4732 | woefully
4733 | womanizer
4734 | womanizing
4735 | worn
4736 | worried
4737 | worriedly
4738 | worrier
4739 | worries
4740 | worrisome
4741 | worry
4742 | worrying
4743 | worryingly
4744 | worse
4745 | worsen
4746 | worsening
4747 | worst
4748 | worthless
4749 | worthlessly
4750 | worthlessness
4751 | wound
4752 | wounds
4753 | wrangle
4754 | wrath
4755 | wreak
4756 | wreaked
4757 | wreaks
4758 | wreck
4759 | wrest
4760 | wrestle
4761 | wretch
4762 | wretched
4763 | wretchedly
4764 | wretchedness
4765 | wrinkle
4766 | wrinkled
4767 | wrinkles
4768 | wrip
4769 | wripped
4770 | wripping
4771 | writhe
4772 | wrong
4773 | wrongful
4774 | wrongly
4775 | wrought
4776 | yawn
4777 | zap
4778 | zapped
4779 | zaps
4780 | zealot
4781 | zealous
4782 | zealously
4783 | zombie
4784 | 


--------------------------------------------------------------------------------
/NLTK_nlp/nlp_ner.py:
--------------------------------------------------------------------------------
 1 | import nltk 
 2 | 
 3 | sample="I want to create an mobile application"
 4 | 
 5 | sentences = nltk.sent_tokenize(sample)
 6 | tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]
 7 | print tokenized_sentences
 8 | tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]
 9 | print tagged_sentences
10 | chunked_sentences = nltk.ne_chunk_sents(tagged_sentences, binary=True)
11 | print chunked_sentences
12 | 
13 | entity_names = []
14 | 
15 | if hasattr(chunked_sentences, 'label') and chunked_sentences.label:
16 |         if t.label() == 'NE':
17 |             entity_names.append(' '.join([child[0] for child in t]))
18 |         else:
19 |             for child in t:
20 |                 entity_names.append(extract_entity_names(child))
21 | 
22 | print entity_names
23 | 
24 | 
25 |     #entity_names.append(extract_entity_names(tree))
26 | 
27 | # Print all entity names
28 | print entity_names
29 | 
30 | # Print unique entity names
31 | print entity_names


--------------------------------------------------------------------------------
/NLTK_nlp/nltk_tf_idf.py:
--------------------------------------------------------------------------------
 1 | from sklearn.feature_extraction.text import TfidfVectorizer
 2 | import numpy as np
 3 | import pandas as pd
 4 | phrases=["I am going to have a pizza   delivered for tonight","I am going to try that new French restaurant for dinner"
 5 |          ,"I prefer reading over going to the movie","I have friends in New York city","she loves going to the movies"]
 6 | vect=TfidfVectorizer(min_df=1)
 7 | tfidf=vect.fit_transform(phrases)
 8 | print tfidf
 9 | cosine=(tfidf*tfidf.T).A
10 | print cosine
11 | df=pd.DataFrame(cosine,index=phrases,columns=phrases)
12 | print df.head(20)
13 | 


--------------------------------------------------------------------------------
/NLTK_nlp/positive.csv:
--------------------------------------------------------------------------------
   1 | a+
   2 | abound
   3 | abounds
   4 | abundance
   5 | abundant
   6 | accessable
   7 | accessible
   8 | acclaim
   9 | acclaimed
  10 | acclamation
  11 | accolade
  12 | accolades
  13 | accommodative
  14 | accomodative
  15 | accomplish
  16 | accomplished
  17 | accomplishment
  18 | accomplishments
  19 | accurate
  20 | accurately
  21 | achievable
  22 | achievement
  23 | achievements
  24 | achievible
  25 | acumen
  26 | adaptable
  27 | adaptive
  28 | adequate
  29 | adjustable
  30 | admirable
  31 | admirably
  32 | admiration
  33 | admire
  34 | admirer
  35 | admiring
  36 | admiringly
  37 | adorable
  38 | adore
  39 | adored
  40 | adorer
  41 | adoring
  42 | adoringly
  43 | adroit
  44 | adroitly
  45 | adulate
  46 | adulation
  47 | adulatory
  48 | advanced
  49 | advantage
  50 | advantageous
  51 | advantageously
  52 | advantages
  53 | adventuresome
  54 | adventurous
  55 | advocate
  56 | advocated
  57 | advocates
  58 | affability
  59 | affable
  60 | affably
  61 | affectation
  62 | affection
  63 | affectionate
  64 | affinity
  65 | affirm
  66 | affirmation
  67 | affirmative
  68 | affluence
  69 | affluent
  70 | afford
  71 | affordable
  72 | affordably
  73 | afordable
  74 | agile
  75 | agilely
  76 | agility
  77 | agreeable
  78 | agreeableness
  79 | agreeably
  80 | all-around
  81 | alluring
  82 | alluringly
  83 | altruistic
  84 | altruistically
  85 | amaze
  86 | amazed
  87 | amazement
  88 | amazes
  89 | amazing
  90 | amazingly
  91 | ambitious
  92 | ambitiously
  93 | ameliorate
  94 | amenable
  95 | amenity
  96 | amiability
  97 | amiabily
  98 | amiable
  99 | amicability
 100 | amicable
 101 | amicably
 102 | amity
 103 | ample
 104 | amply
 105 | amuse
 106 | amusing
 107 | amusingly
 108 | angel
 109 | angelic
 110 | apotheosis
 111 | appeal
 112 | appealing
 113 | applaud
 114 | appreciable
 115 | appreciate
 116 | appreciated
 117 | appreciates
 118 | appreciative
 119 | appreciatively
 120 | appropriate
 121 | approval
 122 | approve
 123 | ardent
 124 | ardently
 125 | ardor
 126 | articulate
 127 | aspiration
 128 | aspirations
 129 | aspire
 130 | assurance
 131 | assurances
 132 | assure
 133 | assuredly
 134 | assuring
 135 | astonish
 136 | astonished
 137 | astonishing
 138 | astonishingly
 139 | astonishment
 140 | astound
 141 | astounded
 142 | astounding
 143 | astoundingly
 144 | astutely
 145 | attentive
 146 | attraction
 147 | attractive
 148 | attractively
 149 | attune
 150 | audible
 151 | audibly
 152 | auspicious
 153 | authentic
 154 | authoritative
 155 | autonomous
 156 | available
 157 | aver
 158 | avid
 159 | avidly
 160 | award
 161 | awarded
 162 | awards
 163 | awe
 164 | awed
 165 | awesome
 166 | awesomely
 167 | awesomeness
 168 | awestruck
 169 | awsome
 170 | backbone
 171 | balanced
 172 | bargain
 173 | beauteous
 174 | beautiful
 175 | beautifullly
 176 | beautifully
 177 | beautify
 178 | beauty
 179 | beckon
 180 | beckoned
 181 | beckoning
 182 | beckons
 183 | believable
 184 | believeable
 185 | beloved
 186 | benefactor
 187 | beneficent
 188 | beneficial
 189 | beneficially
 190 | beneficiary
 191 | benefit
 192 | benefits
 193 | benevolence
 194 | benevolent
 195 | benifits
 196 | best
 197 | best-known
 198 | best-performing
 199 | best-selling
 200 | better
 201 | better-known
 202 | better-than-expected
 203 | beutifully
 204 | blameless
 205 | bless
 206 | blessing
 207 | bliss
 208 | blissful
 209 | blissfully
 210 | blithe
 211 | blockbuster
 212 | bloom
 213 | blossom
 214 | bolster
 215 | bonny
 216 | bonus
 217 | bonuses
 218 | boom
 219 | booming
 220 | boost
 221 | boundless
 222 | bountiful
 223 | brainiest
 224 | brainy
 225 | brand-new
 226 | brave
 227 | bravery
 228 | bravo
 229 | breakthrough
 230 | breakthroughs
 231 | breathlessness
 232 | breathtaking
 233 | breathtakingly
 234 | breeze
 235 | bright
 236 | brighten
 237 | brighter
 238 | brightest
 239 | brilliance
 240 | brilliances
 241 | brilliant
 242 | brilliantly
 243 | brisk
 244 | brotherly
 245 | bullish
 246 | buoyant
 247 | cajole
 248 | calm
 249 | calming
 250 | calmness
 251 | capability
 252 | capable
 253 | capably
 254 | captivate
 255 | captivating
 256 | carefree
 257 | cashback
 258 | cashbacks
 259 | catchy
 260 | celebrate
 261 | celebrated
 262 | celebration
 263 | celebratory
 264 | champ
 265 | champion
 266 | charisma
 267 | charismatic
 268 | charitable
 269 | charm
 270 | charming
 271 | charmingly
 272 | chaste
 273 | cheaper
 274 | cheapest
 275 | cheer
 276 | cheerful
 277 | cheery
 278 | cherish
 279 | cherished
 280 | cherub
 281 | chic
 282 | chivalrous
 283 | chivalry
 284 | civility
 285 | civilize
 286 | clarity
 287 | classic
 288 | classy
 289 | clean
 290 | cleaner
 291 | cleanest
 292 | cleanliness
 293 | cleanly
 294 | clear
 295 | clear-cut
 296 | cleared
 297 | clearer
 298 | clearly
 299 | clears
 300 | clever
 301 | cleverly
 302 | cohere
 303 | coherence
 304 | coherent
 305 | cohesive
 306 | colorful
 307 | comely
 308 | comfort
 309 | comfortable
 310 | comfortably
 311 | comforting
 312 | comfy
 313 | commend
 314 | commendable
 315 | commendably
 316 | commitment
 317 | commodious
 318 | compact
 319 | compactly
 320 | compassion
 321 | compassionate
 322 | compatible
 323 | competitive
 324 | complement
 325 | complementary
 326 | complemented
 327 | complements
 328 | compliant
 329 | compliment
 330 | complimentary
 331 | comprehensive
 332 | conciliate
 333 | conciliatory
 334 | concise
 335 | confidence
 336 | confident
 337 | congenial
 338 | congratulate
 339 | congratulation
 340 | congratulations
 341 | congratulatory
 342 | conscientious
 343 | considerate
 344 | consistent
 345 | consistently
 346 | constructive
 347 | consummate
 348 | contentment
 349 | continuity
 350 | contrasty
 351 | contribution
 352 | convenience
 353 | convenient
 354 | conveniently
 355 | convience
 356 | convienient
 357 | convient
 358 | convincing
 359 | convincingly
 360 | cool
 361 | coolest
 362 | cooperative
 363 | cooperatively
 364 | cornerstone
 365 | correct
 366 | correctly
 367 | cost-effective
 368 | cost-saving
 369 | counter-attack
 370 | counter-attacks
 371 | courage
 372 | courageous
 373 | courageously
 374 | courageousness
 375 | courteous
 376 | courtly
 377 | covenant
 378 | cozy
 379 | creative
 380 | credence
 381 | credible
 382 | crisp
 383 | crisper
 384 | cure
 385 | cure-all
 386 | cushy
 387 | cute
 388 | cuteness
 389 | danke
 390 | danken
 391 | daring
 392 | daringly
 393 | darling
 394 | dashing
 395 | dauntless
 396 | dawn
 397 | dazzle
 398 | dazzled
 399 | dazzling
 400 | dead-cheap
 401 | dead-on
 402 | decency
 403 | decent
 404 | decisive
 405 | decisiveness
 406 | dedicated
 407 | defeat
 408 | defeated
 409 | defeating
 410 | defeats
 411 | defender
 412 | deference
 413 | deft
 414 | deginified
 415 | delectable
 416 | delicacy
 417 | delicate
 418 | delicious
 419 | delight
 420 | delighted
 421 | delightful
 422 | delightfully
 423 | delightfulness
 424 | dependable
 425 | dependably
 426 | deservedly
 427 | deserving
 428 | desirable
 429 | desiring
 430 | desirous
 431 | destiny
 432 | detachable
 433 | devout
 434 | dexterous
 435 | dexterously
 436 | dextrous
 437 | dignified
 438 | dignify
 439 | dignity
 440 | diligence
 441 | diligent
 442 | diligently
 443 | diplomatic
 444 | dirt-cheap
 445 | distinction
 446 | distinctive
 447 | distinguished
 448 | diversified
 449 | divine
 450 | divinely
 451 | dominate
 452 | dominated
 453 | dominates
 454 | dote
 455 | dotingly
 456 | doubtless
 457 | dreamland
 458 | dumbfounded
 459 | dumbfounding
 460 | dummy-proof
 461 | durable
 462 | dynamic
 463 | eager
 464 | eagerly
 465 | eagerness
 466 | earnest
 467 | earnestly
 468 | earnestness
 469 | ease
 470 | eased
 471 | eases
 472 | easier
 473 | easiest
 474 | easiness
 475 | easing
 476 | easy
 477 | easy-to-use
 478 | easygoing
 479 | ebullience
 480 | ebullient
 481 | ebulliently
 482 | ecenomical
 483 | economical
 484 | ecstasies
 485 | ecstasy
 486 | ecstatic
 487 | ecstatically
 488 | edify
 489 | educated
 490 | effective
 491 | effectively
 492 | effectiveness
 493 | effectual
 494 | efficacious
 495 | efficient
 496 | efficiently
 497 | effortless
 498 | effortlessly
 499 | effusion
 500 | effusive
 501 | effusively
 502 | effusiveness
 503 | elan
 504 | elate
 505 | elated
 506 | elatedly
 507 | elation
 508 | electrify
 509 | elegance
 510 | elegant
 511 | elegantly
 512 | elevate
 513 | elite
 514 | eloquence
 515 | eloquent
 516 | eloquently
 517 | embolden
 518 | eminence
 519 | eminent
 520 | empathize
 521 | empathy
 522 | empower
 523 | empowerment
 524 | enchant
 525 | enchanted
 526 | enchanting
 527 | enchantingly
 528 | encourage
 529 | encouragement
 530 | encouraging
 531 | encouragingly
 532 | endear
 533 | endearing
 534 | endorse
 535 | endorsed
 536 | endorsement
 537 | endorses
 538 | endorsing
 539 | energetic
 540 | energize
 541 | energy-efficient
 542 | energy-saving
 543 | engaging
 544 | engrossing
 545 | enhance
 546 | enhanced
 547 | enhancement
 548 | enhances
 549 | enjoy
 550 | enjoyable
 551 | enjoyably
 552 | enjoyed
 553 | enjoying
 554 | enjoyment
 555 | enjoys
 556 | enlighten
 557 | enlightenment
 558 | enliven
 559 | ennoble
 560 | enough
 561 | enrapt
 562 | enrapture
 563 | enraptured
 564 | enrich
 565 | enrichment
 566 | enterprising
 567 | entertain
 568 | entertaining
 569 | entertains
 570 | enthral
 571 | enthrall
 572 | enthralled
 573 | enthuse
 574 | enthusiasm
 575 | enthusiast
 576 | enthusiastic
 577 | enthusiastically
 578 | entice
 579 | enticed
 580 | enticing
 581 | enticingly
 582 | entranced
 583 | entrancing
 584 | entrust
 585 | enviable
 586 | enviably
 587 | envious
 588 | enviously
 589 | enviousness
 590 | envy
 591 | equitable
 592 | ergonomical
 593 | err-free
 594 | erudite
 595 | ethical
 596 | eulogize
 597 | euphoria
 598 | euphoric
 599 | euphorically
 600 | evaluative
 601 | evenly
 602 | eventful
 603 | everlasting
 604 | evocative
 605 | exalt
 606 | exaltation
 607 | exalted
 608 | exaltedly
 609 | exalting
 610 | exaltingly
 611 | examplar
 612 | examplary
 613 | excallent
 614 | exceed
 615 | exceeded
 616 | exceeding
 617 | exceedingly
 618 | exceeds
 619 | excel
 620 | exceled
 621 | excelent
 622 | excellant
 623 | excelled
 624 | excellence
 625 | excellency
 626 | excellent
 627 | excellently
 628 | excels
 629 | exceptional
 630 | exceptionally
 631 | excite
 632 | excited
 633 | excitedly
 634 | excitedness
 635 | excitement
 636 | excites
 637 | exciting
 638 | excitingly
 639 | exellent
 640 | exemplar
 641 | exemplary
 642 | exhilarate
 643 | exhilarating
 644 | exhilaratingly
 645 | exhilaration
 646 | exonerate
 647 | expansive
 648 | expeditiously
 649 | expertly
 650 | exquisite
 651 | exquisitely
 652 | extol
 653 | extoll
 654 | extraordinarily
 655 | extraordinary
 656 | exuberance
 657 | exuberant
 658 | exuberantly
 659 | exult
 660 | exultant
 661 | exultation
 662 | exultingly
 663 | eye-catch
 664 | eye-catching
 665 | eyecatch
 666 | eyecatching
 667 | fabulous
 668 | fabulously
 669 | facilitate
 670 | fair
 671 | fairly
 672 | fairness
 673 | faith
 674 | faithful
 675 | faithfully
 676 | faithfulness
 677 | fame
 678 | famed
 679 | famous
 680 | famously
 681 | fancier
 682 | fancinating
 683 | fancy
 684 | fanfare
 685 | fans
 686 | fantastic
 687 | fantastically
 688 | fascinate
 689 | fascinating
 690 | fascinatingly
 691 | fascination
 692 | fashionable
 693 | fashionably
 694 | fast
 695 | fast-growing
 696 | fast-paced
 697 | faster
 698 | fastest
 699 | fastest-growing
 700 | faultless
 701 | fav
 702 | fave
 703 | favor
 704 | favorable
 705 | favored
 706 | favorite
 707 | favorited
 708 | favour
 709 | fearless
 710 | fearlessly
 711 | feasible
 712 | feasibly
 713 | feat
 714 | feature-rich
 715 | fecilitous
 716 | feisty
 717 | felicitate
 718 | felicitous
 719 | felicity
 720 | fertile
 721 | fervent
 722 | fervently
 723 | fervid
 724 | fervidly
 725 | fervor
 726 | festive
 727 | fidelity
 728 | fiery
 729 | fine
 730 | fine-looking
 731 | finely
 732 | finer
 733 | finest
 734 | firmer
 735 | first-class
 736 | first-in-class
 737 | first-rate
 738 | flashy
 739 | flatter
 740 | flattering
 741 | flatteringly
 742 | flawless
 743 | flawlessly
 744 | flexibility
 745 | flexible
 746 | flourish
 747 | flourishing
 748 | fluent
 749 | flutter
 750 | fond
 751 | fondly
 752 | fondness
 753 | foolproof
 754 | foremost
 755 | foresight
 756 | formidable
 757 | fortitude
 758 | fortuitous
 759 | fortuitously
 760 | fortunate
 761 | fortunately
 762 | fortune
 763 | fragrant
 764 | free
 765 | freed
 766 | freedom
 767 | freedoms
 768 | fresh
 769 | fresher
 770 | freshest
 771 | friendliness
 772 | friendly
 773 | frolic
 774 | frugal
 775 | fruitful
 776 | ftw
 777 | fulfillment
 778 | fun
 779 | futurestic
 780 | futuristic
 781 | gaiety
 782 | gaily
 783 | gain
 784 | gained
 785 | gainful
 786 | gainfully
 787 | gaining
 788 | gains
 789 | gallant
 790 | gallantly
 791 | galore
 792 | geekier
 793 | geeky
 794 | gem
 795 | gems
 796 | generosity
 797 | generous
 798 | generously
 799 | genial
 800 | genius
 801 | gentle
 802 | gentlest
 803 | genuine
 804 | gifted
 805 | glad
 806 | gladden
 807 | gladly
 808 | gladness
 809 | glamorous
 810 | glee
 811 | gleeful
 812 | gleefully
 813 | glimmer
 814 | glimmering
 815 | glisten
 816 | glistening
 817 | glitter
 818 | glitz
 819 | glorify
 820 | glorious
 821 | gloriously
 822 | glory
 823 | glow
 824 | glowing
 825 | glowingly
 826 | god-given
 827 | god-send
 828 | godlike
 829 | godsend
 830 | gold
 831 | golden
 832 | good
 833 | goodly
 834 | goodness
 835 | goodwill
 836 | goood
 837 | gooood
 838 | gorgeous
 839 | gorgeously
 840 | grace
 841 | graceful
 842 | gracefully
 843 | gracious
 844 | graciously
 845 | graciousness
 846 | grand
 847 | grandeur
 848 | grateful
 849 | gratefully
 850 | gratification
 851 | gratified
 852 | gratifies
 853 | gratify
 854 | gratifying
 855 | gratifyingly
 856 | gratitude
 857 | great
 858 | greatest
 859 | greatness
 860 | grin
 861 | groundbreaking
 862 | guarantee
 863 | guidance
 864 | guiltless
 865 | gumption
 866 | gush
 867 | gusto
 868 | gutsy
 869 | hail
 870 | halcyon
 871 | hale
 872 | hallmark
 873 | hallmarks
 874 | hallowed
 875 | handier
 876 | handily
 877 | hands-down
 878 | handsome
 879 | handsomely
 880 | handy
 881 | happier
 882 | happily
 883 | happiness
 884 | happy
 885 | hard-working
 886 | hardier
 887 | hardy
 888 | harmless
 889 | harmonious
 890 | harmoniously
 891 | harmonize
 892 | harmony
 893 | headway
 894 | heal
 895 | healthful
 896 | healthy
 897 | hearten
 898 | heartening
 899 | heartfelt
 900 | heartily
 901 | heartwarming
 902 | heaven
 903 | heavenly
 904 | helped
 905 | helpful
 906 | helping
 907 | hero
 908 | heroic
 909 | heroically
 910 | heroine
 911 | heroize
 912 | heros
 913 | high-quality
 914 | high-spirited
 915 | hilarious
 916 | holy
 917 | homage
 918 | honest
 919 | honesty
 920 | honor
 921 | honorable
 922 | honored
 923 | honoring
 924 | hooray
 925 | hopeful
 926 | hospitable
 927 | hot
 928 | hotcake
 929 | hotcakes
 930 | hottest
 931 | hug
 932 | humane
 933 | humble
 934 | humility
 935 | humor
 936 | humorous
 937 | humorously
 938 | humour
 939 | humourous
 940 | ideal
 941 | idealize
 942 | ideally
 943 | idol
 944 | idolize
 945 | idolized
 946 | idyllic
 947 | illuminate
 948 | illuminati
 949 | illuminating
 950 | illumine
 951 | illustrious
 952 | ilu
 953 | imaculate
 954 | imaginative
 955 | immaculate
 956 | immaculately
 957 | immense
 958 | impartial
 959 | impartiality
 960 | impartially
 961 | impassioned
 962 | impeccable
 963 | impeccably
 964 | important
 965 | impress
 966 | impressed
 967 | impresses
 968 | impressive
 969 | impressively
 970 | impressiveness
 971 | improve
 972 | improved
 973 | improvement
 974 | improvements
 975 | improves
 976 | improving
 977 | incredible
 978 | incredibly
 979 | indebted
 980 | individualized
 981 | indulgence
 982 | indulgent
 983 | industrious
 984 | inestimable
 985 | inestimably
 986 | inexpensive
 987 | infallibility
 988 | infallible
 989 | infallibly
 990 | influential
 991 | ingenious
 992 | ingeniously
 993 | ingenuity
 994 | ingenuous
 995 | ingenuously
 996 | innocuous
 997 | innovation
 998 | innovative
 999 | inpressed
1000 | insightful
1001 | insightfully
1002 | inspiration
1003 | inspirational
1004 | inspire
1005 | inspiring
1006 | instantly
1007 | instructive
1008 | instrumental
1009 | integral
1010 | integrated
1011 | intelligence
1012 | intelligent
1013 | intelligible
1014 | interesting
1015 | interests
1016 | intimacy
1017 | intimate
1018 | intricate
1019 | intrigue
1020 | intriguing
1021 | intriguingly
1022 | intuitive
1023 | invaluable
1024 | invaluablely
1025 | inventive
1026 | invigorate
1027 | invigorating
1028 | invincibility
1029 | invincible
1030 | inviolable
1031 | inviolate
1032 | invulnerable
1033 | irreplaceable
1034 | irreproachable
1035 | irresistible
1036 | irresistibly
1037 | issue-free
1038 | jaw-droping
1039 | jaw-dropping
1040 | jollify
1041 | jolly
1042 | jovial
1043 | joy
1044 | joyful
1045 | joyfully
1046 | joyous
1047 | joyously
1048 | jubilant
1049 | jubilantly
1050 | jubilate
1051 | jubilation
1052 | jubiliant
1053 | judicious
1054 | justly
1055 | keen
1056 | keenly
1057 | keenness
1058 | kid-friendly
1059 | kindliness
1060 | kindly
1061 | kindness
1062 | knowledgeable
1063 | kudos
1064 | large-capacity
1065 | laud
1066 | laudable
1067 | laudably
1068 | lavish
1069 | lavishly
1070 | law-abiding
1071 | lawful
1072 | lawfully
1073 | lead
1074 | leading
1075 | leads
1076 | lean
1077 | led
1078 | legendary
1079 | leverage
1080 | levity
1081 | liberate
1082 | liberation
1083 | liberty
1084 | lifesaver
1085 | light-hearted
1086 | lighter
1087 | likable
1088 | like
1089 | liked
1090 | likes
1091 | liking
1092 | lionhearted
1093 | lively
1094 | logical
1095 | long-lasting
1096 | lovable
1097 | lovably
1098 | love
1099 | loved
1100 | loveliness
1101 | lovely
1102 | lover
1103 | loves
1104 | loving
1105 | low-cost
1106 | low-price
1107 | low-priced
1108 | low-risk
1109 | lower-priced
1110 | loyal
1111 | loyalty
1112 | lucid
1113 | lucidly
1114 | luck
1115 | luckier
1116 | luckiest
1117 | luckiness
1118 | lucky
1119 | lucrative
1120 | luminous
1121 | lush
1122 | luster
1123 | lustrous
1124 | luxuriant
1125 | luxuriate
1126 | luxurious
1127 | luxuriously
1128 | luxury
1129 | lyrical
1130 | magic
1131 | magical
1132 | magnanimous
1133 | magnanimously
1134 | magnificence
1135 | magnificent
1136 | magnificently
1137 | majestic
1138 | majesty
1139 | manageable
1140 | maneuverable
1141 | marvel
1142 | marveled
1143 | marvelled
1144 | marvellous
1145 | marvelous
1146 | marvelously
1147 | marvelousness
1148 | marvels
1149 | master
1150 | masterful
1151 | masterfully
1152 | masterpiece
1153 | masterpieces
1154 | masters
1155 | mastery
1156 | matchless
1157 | mature
1158 | maturely
1159 | maturity
1160 | meaningful
1161 | memorable
1162 | merciful
1163 | mercifully
1164 | mercy
1165 | merit
1166 | meritorious
1167 | merrily
1168 | merriment
1169 | merriness
1170 | merry
1171 | mesmerize
1172 | mesmerized
1173 | mesmerizes
1174 | mesmerizing
1175 | mesmerizingly
1176 | meticulous
1177 | meticulously
1178 | mightily
1179 | mighty
1180 | mind-blowing
1181 | miracle
1182 | miracles
1183 | miraculous
1184 | miraculously
1185 | miraculousness
1186 | modern
1187 | modest
1188 | modesty
1189 | momentous
1190 | monumental
1191 | monumentally
1192 | morality
1193 | motivated
1194 | multi-purpose
1195 | navigable
1196 | neat
1197 | neatest
1198 | neatly
1199 | nice
1200 | nicely
1201 | nicer
1202 | nicest
1203 | nifty
1204 | nimble
1205 | noble
1206 | nobly
1207 | noiseless
1208 | non-violence
1209 | non-violent
1210 | notably
1211 | noteworthy
1212 | nourish
1213 | nourishing
1214 | nourishment
1215 | novelty
1216 | nurturing
1217 | oasis
1218 | obsession
1219 | obsessions
1220 | obtainable
1221 | openly
1222 | openness
1223 | optimal
1224 | optimism
1225 | optimistic
1226 | opulent
1227 | orderly
1228 | originality
1229 | outdo
1230 | outdone
1231 | outperform
1232 | outperformed
1233 | outperforming
1234 | outperforms
1235 | outshine
1236 | outshone
1237 | outsmart
1238 | outstanding
1239 | outstandingly
1240 | outstrip
1241 | outwit
1242 | ovation
1243 | overjoyed
1244 | overtake
1245 | overtaken
1246 | overtakes
1247 | overtaking
1248 | overtook
1249 | overture
1250 | pain-free
1251 | painless
1252 | painlessly
1253 | palatial
1254 | pamper
1255 | pampered
1256 | pamperedly
1257 | pamperedness
1258 | pampers
1259 | panoramic
1260 | paradise
1261 | paramount
1262 | pardon
1263 | passion
1264 | passionate
1265 | passionately
1266 | patience
1267 | patient
1268 | patiently
1269 | patriot
1270 | patriotic
1271 | peace
1272 | peaceable
1273 | peaceful
1274 | peacefully
1275 | peacekeepers
1276 | peach
1277 | peerless
1278 | pep
1279 | pepped
1280 | pepping
1281 | peppy
1282 | peps
1283 | perfect
1284 | perfection
1285 | perfectly
1286 | permissible
1287 | perseverance
1288 | persevere
1289 | personages
1290 | personalized
1291 | phenomenal
1292 | phenomenally
1293 | picturesque
1294 | piety
1295 | pinnacle
1296 | playful
1297 | playfully
1298 | pleasant
1299 | pleasantly
1300 | pleased
1301 | pleases
1302 | pleasing
1303 | pleasingly
1304 | pleasurable
1305 | pleasurably
1306 | pleasure
1307 | plentiful
1308 | pluses
1309 | plush
1310 | plusses
1311 | poetic
1312 | poeticize
1313 | poignant
1314 | poise
1315 | poised
1316 | polished
1317 | polite
1318 | politeness
1319 | popular
1320 | portable
1321 | posh
1322 | positive
1323 | positively
1324 | positives
1325 | powerful
1326 | powerfully
1327 | praise
1328 | praiseworthy
1329 | praising
1330 | pre-eminent
1331 | precious
1332 | precise
1333 | precisely
1334 | preeminent
1335 | prefer
1336 | preferable
1337 | preferably
1338 | prefered
1339 | preferes
1340 | preferring
1341 | prefers
1342 | premier
1343 | prestige
1344 | prestigious
1345 | prettily
1346 | pretty
1347 | priceless
1348 | pride
1349 | principled
1350 | privilege
1351 | privileged
1352 | prize
1353 | proactive
1354 | problem-free
1355 | problem-solver
1356 | prodigious
1357 | prodigiously
1358 | prodigy
1359 | productive
1360 | productively
1361 | proficient
1362 | proficiently
1363 | profound
1364 | profoundly
1365 | profuse
1366 | profusion
1367 | progress
1368 | progressive
1369 | prolific
1370 | prominence
1371 | prominent
1372 | promise
1373 | promised
1374 | promises
1375 | promising
1376 | promoter
1377 | prompt
1378 | promptly
1379 | proper
1380 | properly
1381 | propitious
1382 | propitiously
1383 | pros
1384 | prosper
1385 | prosperity
1386 | prosperous
1387 | prospros
1388 | protect
1389 | protection
1390 | protective
1391 | proud
1392 | proven
1393 | proves
1394 | providence
1395 | proving
1396 | prowess
1397 | prudence
1398 | prudent
1399 | prudently
1400 | punctual
1401 | pure
1402 | purify
1403 | purposeful
1404 | quaint
1405 | qualified
1406 | qualify
1407 | quicker
1408 | quiet
1409 | quieter
1410 | radiance
1411 | radiant
1412 | rapid
1413 | rapport
1414 | rapt
1415 | rapture
1416 | raptureous
1417 | raptureously
1418 | rapturous
1419 | rapturously
1420 | rational
1421 | razor-sharp
1422 | reachable
1423 | readable
1424 | readily
1425 | ready
1426 | reaffirm
1427 | reaffirmation
1428 | realistic
1429 | realizable
1430 | reasonable
1431 | reasonably
1432 | reasoned
1433 | reassurance
1434 | reassure
1435 | receptive
1436 | reclaim
1437 | recomend
1438 | recommend
1439 | recommendation
1440 | recommendations
1441 | recommended
1442 | reconcile
1443 | reconciliation
1444 | record-setting
1445 | recover
1446 | recovery
1447 | rectification
1448 | rectify
1449 | rectifying
1450 | redeem
1451 | redeeming
1452 | redemption
1453 | refine
1454 | refined
1455 | refinement
1456 | reform
1457 | reformed
1458 | reforming
1459 | reforms
1460 | refresh
1461 | refreshed
1462 | refreshing
1463 | refund
1464 | refunded
1465 | regal
1466 | regally
1467 | regard
1468 | rejoice
1469 | rejoicing
1470 | rejoicingly
1471 | rejuvenate
1472 | rejuvenated
1473 | rejuvenating
1474 | relaxed
1475 | relent
1476 | reliable
1477 | reliably
1478 | relief
1479 | relish
1480 | remarkable
1481 | remarkably
1482 | remedy
1483 | remission
1484 | remunerate
1485 | renaissance
1486 | renewed
1487 | renown
1488 | renowned
1489 | replaceable
1490 | reputable
1491 | reputation
1492 | resilient
1493 | resolute
1494 | resound
1495 | resounding
1496 | resourceful
1497 | resourcefulness
1498 | respect
1499 | respectable
1500 | respectful
1501 | respectfully
1502 | respite
1503 | resplendent
1504 | responsibly
1505 | responsive
1506 | restful
1507 | restored
1508 | restructure
1509 | restructured
1510 | restructuring
1511 | retractable
1512 | revel
1513 | revelation
1514 | revere
1515 | reverence
1516 | reverent
1517 | reverently
1518 | revitalize
1519 | revival
1520 | revive
1521 | revives
1522 | revolutionary
1523 | revolutionize
1524 | revolutionized
1525 | revolutionizes
1526 | reward
1527 | rewarding
1528 | rewardingly
1529 | rich
1530 | richer
1531 | richly
1532 | richness
1533 | right
1534 | righten
1535 | righteous
1536 | righteously
1537 | righteousness
1538 | rightful
1539 | rightfully
1540 | rightly
1541 | rightness
1542 | risk-free
1543 | robust
1544 | rock-star
1545 | rock-stars
1546 | rockstar
1547 | rockstars
1548 | romantic
1549 | romantically
1550 | romanticize
1551 | roomier
1552 | roomy
1553 | rosy
1554 | safe
1555 | safely
1556 | sagacity
1557 | sagely
1558 | saint
1559 | saintliness
1560 | saintly
1561 | salutary
1562 | salute
1563 | sane
1564 | satisfactorily
1565 | satisfactory
1566 | satisfied
1567 | satisfies
1568 | satisfy
1569 | satisfying
1570 | satisified
1571 | saver
1572 | savings
1573 | savior
1574 | savvy
1575 | scenic
1576 | seamless
1577 | seasoned
1578 | secure
1579 | securely
1580 | selective
1581 | self-determination
1582 | self-respect
1583 | self-satisfaction
1584 | self-sufficiency
1585 | self-sufficient
1586 | sensation
1587 | sensational
1588 | sensationally
1589 | sensations
1590 | sensible
1591 | sensibly
1592 | sensitive
1593 | serene
1594 | serenity
1595 | sexy
1596 | sharp
1597 | sharper
1598 | sharpest
1599 | shimmering
1600 | shimmeringly
1601 | shine
1602 | shiny
1603 | significant
1604 | silent
1605 | simpler
1606 | simplest
1607 | simplified
1608 | simplifies
1609 | simplify
1610 | simplifying
1611 | sincere
1612 | sincerely
1613 | sincerity
1614 | skill
1615 | skilled
1616 | skillful
1617 | skillfully
1618 | slammin
1619 | sleek
1620 | slick
1621 | smart
1622 | smarter
1623 | smartest
1624 | smartly
1625 | smile
1626 | smiles
1627 | smiling
1628 | smilingly
1629 | smitten
1630 | smooth
1631 | smoother
1632 | smoothes
1633 | smoothest
1634 | smoothly
1635 | snappy
1636 | snazzy
1637 | sociable
1638 | soft
1639 | softer
1640 | solace
1641 | solicitous
1642 | solicitously
1643 | solid
1644 | solidarity
1645 | soothe
1646 | soothingly
1647 | sophisticated
1648 | soulful
1649 | soundly
1650 | soundness
1651 | spacious
1652 | sparkle
1653 | sparkling
1654 | spectacular
1655 | spectacularly
1656 | speedily
1657 | speedy
1658 | spellbind
1659 | spellbinding
1660 | spellbindingly
1661 | spellbound
1662 | spirited
1663 | spiritual
1664 | splendid
1665 | splendidly
1666 | splendor
1667 | spontaneous
1668 | sporty
1669 | spotless
1670 | sprightly
1671 | stability
1672 | stabilize
1673 | stable
1674 | stainless
1675 | standout
1676 | state-of-the-art
1677 | stately
1678 | statuesque
1679 | staunch
1680 | staunchly
1681 | staunchness
1682 | steadfast
1683 | steadfastly
1684 | steadfastness
1685 | steadiest
1686 | steadiness
1687 | steady
1688 | stellar
1689 | stellarly
1690 | stimulate
1691 | stimulates
1692 | stimulating
1693 | stimulative
1694 | stirringly
1695 | straighten
1696 | straightforward
1697 | streamlined
1698 | striking
1699 | strikingly
1700 | striving
1701 | strong
1702 | stronger
1703 | strongest
1704 | stunned
1705 | stunning
1706 | stunningly
1707 | stupendous
1708 | stupendously
1709 | sturdier
1710 | sturdy
1711 | stylish
1712 | stylishly
1713 | stylized
1714 | suave
1715 | suavely
1716 | sublime
1717 | subsidize
1718 | subsidized
1719 | subsidizes
1720 | subsidizing
1721 | substantive
1722 | succeed
1723 | succeeded
1724 | succeeding
1725 | succeeds
1726 | succes
1727 | success
1728 | successes
1729 | successful
1730 | successfully
1731 | suffice
1732 | sufficed
1733 | suffices
1734 | sufficient
1735 | sufficiently
1736 | suitable
1737 | sumptuous
1738 | sumptuously
1739 | sumptuousness
1740 | super
1741 | superb
1742 | superbly
1743 | superior
1744 | superiority
1745 | supple
1746 | support
1747 | supported
1748 | supporter
1749 | supporting
1750 | supportive
1751 | supports
1752 | supremacy
1753 | supreme
1754 | supremely
1755 | supurb
1756 | supurbly
1757 | surmount
1758 | surpass
1759 | surreal
1760 | survival
1761 | survivor
1762 | sustainability
1763 | sustainable
1764 | swank
1765 | swankier
1766 | swankiest
1767 | swanky
1768 | sweeping
1769 | sweet
1770 | sweeten
1771 | sweetheart
1772 | sweetly
1773 | sweetness
1774 | swift
1775 | swiftness
1776 | talent
1777 | talented
1778 | talents
1779 | tantalize
1780 | tantalizing
1781 | tantalizingly
1782 | tempt
1783 | tempting
1784 | temptingly
1785 | tenacious
1786 | tenaciously
1787 | tenacity
1788 | tender
1789 | tenderly
1790 | terrific
1791 | terrifically
1792 | thank
1793 | thankful
1794 | thinner
1795 | thoughtful
1796 | thoughtfully
1797 | thoughtfulness
1798 | thrift
1799 | thrifty
1800 | thrill
1801 | thrilled
1802 | thrilling
1803 | thrillingly
1804 | thrills
1805 | thrive
1806 | thriving
1807 | thumb-up
1808 | thumbs-up
1809 | tickle
1810 | tidy
1811 | time-honored
1812 | timely
1813 | tingle
1814 | titillate
1815 | titillating
1816 | titillatingly
1817 | togetherness
1818 | tolerable
1819 | toll-free
1820 | top
1821 | top-notch
1822 | top-quality
1823 | topnotch
1824 | tops
1825 | tough
1826 | tougher
1827 | toughest
1828 | traction
1829 | tranquil
1830 | tranquility
1831 | transparent
1832 | treasure
1833 | tremendously
1834 | trendy
1835 | triumph
1836 | triumphal
1837 | triumphant
1838 | triumphantly
1839 | trivially
1840 | trophy
1841 | trouble-free
1842 | trump
1843 | trumpet
1844 | trust
1845 | trusted
1846 | trusting
1847 | trustingly
1848 | trustworthiness
1849 | trustworthy
1850 | trusty
1851 | truthful
1852 | truthfully
1853 | truthfulness
1854 | twinkly
1855 | ultra-crisp
1856 | unabashed
1857 | unabashedly
1858 | unaffected
1859 | unassailable
1860 | unbeatable
1861 | unbiased
1862 | unbound
1863 | uncomplicated
1864 | unconditional
1865 | undamaged
1866 | undaunted
1867 | understandable
1868 | undisputable
1869 | undisputably
1870 | undisputed
1871 | unencumbered
1872 | unequivocal
1873 | unequivocally
1874 | unfazed
1875 | unfettered
1876 | unforgettable
1877 | unity
1878 | unlimited
1879 | unmatched
1880 | unparalleled
1881 | unquestionable
1882 | unquestionably
1883 | unreal
1884 | unrestricted
1885 | unrivaled
1886 | unselfish
1887 | unwavering
1888 | upbeat
1889 | upgradable
1890 | upgradeable
1891 | upgraded
1892 | upheld
1893 | uphold
1894 | uplift
1895 | uplifting
1896 | upliftingly
1897 | upliftment
1898 | upscale
1899 | usable
1900 | useable
1901 | useful
1902 | user-friendly
1903 | user-replaceable
1904 | valiant
1905 | valiantly
1906 | valor
1907 | valuable
1908 | variety
1909 | venerate
1910 | verifiable
1911 | veritable
1912 | versatile
1913 | versatility
1914 | vibrant
1915 | vibrantly
1916 | victorious
1917 | victory
1918 | viewable
1919 | vigilance
1920 | vigilant
1921 | virtue
1922 | virtuous
1923 | virtuously
1924 | visionary
1925 | vivacious
1926 | vivid
1927 | vouch
1928 | vouchsafe
1929 | warm
1930 | warmer
1931 | warmhearted
1932 | warmly
1933 | warmth
1934 | wealthy
1935 | welcome
1936 | well
1937 | well-backlit
1938 | well-balanced
1939 | well-behaved
1940 | well-being
1941 | well-bred
1942 | well-connected
1943 | well-educated
1944 | well-established
1945 | well-informed
1946 | well-intentioned
1947 | well-known
1948 | well-made
1949 | well-managed
1950 | well-mannered
1951 | well-positioned
1952 | well-received
1953 | well-regarded
1954 | well-rounded
1955 | well-run
1956 | well-wishers
1957 | wellbeing
1958 | whoa
1959 | wholeheartedly
1960 | wholesome
1961 | whooa
1962 | whoooa
1963 | wieldy
1964 | willing
1965 | willingly
1966 | willingness
1967 | win
1968 | windfall
1969 | winnable
1970 | winner
1971 | winners
1972 | winning
1973 | wins
1974 | wisdom
1975 | wise
1976 | wisely
1977 | witty
1978 | won
1979 | wonder
1980 | wonderful
1981 | wonderfully
1982 | wonderous
1983 | wonderously
1984 | wonders
1985 | wondrous
1986 | woo
1987 | work
1988 | workable
1989 | worked
1990 | works
1991 | world-famous
1992 | worth
1993 | worth-while
1994 | worthiness
1995 | worthwhile
1996 | worthy
1997 | wow
1998 | wowed
1999 | wowing
2000 | wows
2001 | yay
2002 | youthful
2003 | zeal
2004 | zenith
2005 | zest
2006 | zippy
2007 | 


--------------------------------------------------------------------------------
/NLTK_nlp/readme.md:
--------------------------------------------------------------------------------
 1 | ## NLTK
 2 | NLTK is intended to support research and teaching in NLP or closely related areas, including empirical linguistics, cognitive science, artificial intelligence, information retrieval, and machine learning.NLTK is the most famous Python Natural Language Processing Toolkit,
 3 | 
 4 | 
 5 | Install Setuptools: http://pypi.python.org/pypi/setuptools
 6 | 
 7 | Install Pip: run sudo easy_install pip
 8 | 
 9 | Install Numpy (optional): run sudo pip install -U numpy
10 | 
11 | Install PyYAML and NLTK: run sudo pip install -U pyyaml nltk
12 | 
13 | Test installation: run python then type import nltk
14 | 
15 | 
16 | The simplest way to install NLTK Data is run the Python interpreter and type the commands, following example is running on Mac Os:
17 | [GCC 4.2.1 Compatible Apple Clang 4.0 (tags/Apple/clang-418.0.60)] on darwin
18 | Type “help”, “copyright”, “credits” or “license” for more information.
19 | >>> import nltk
20 | 
21 | >>> nltk.download()
22 | 
23 | 
24 | 
25 | 
26 | 
27 | 
28 | 
29 | 


--------------------------------------------------------------------------------
/Other/Part-of-speech tagging.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 3,
  6 |    "metadata": {
  7 |     "collapsed": false,
  8 |     "deletable": true,
  9 |     "editable": true
 10 |    },
 11 |    "outputs": [
 12 |     {
 13 |      "data": {
 14 |       "text/plain": [
 15 |        "'arma virumque cano, Troiae qui primus ab oris Italiam, fato profugus, Laviniaque venit litora, multum ille et terris iactatus et alto vi superum saevae memorem Iunonis ob iram; multa quoque et bello passus, dum conderet urbem,               5 inferretque deos Latio, genus unde Latinum, Albanique patres, atque altae moenia Romae.'"
 16 |       ]
 17 |      },
 18 |      "execution_count": 3,
 19 |      "metadata": {},
 20 |      "output_type": "execute_result"
 21 |     }
 22 |    ],
 23 |    "source": [
 24 |     "aen = \"\"\"arma virumque cano, Troiae qui primus ab oris\n",
 25 |     "Italiam, fato profugus, Laviniaque venit\n",
 26 |     "litora, multum ille et terris iactatus et alto\n",
 27 |     "vi superum saevae memorem Iunonis ob iram;\n",
 28 |     "multa quoque et bello passus, dum conderet urbem,               5\n",
 29 |     "inferretque deos Latio, genus unde Latinum,\n",
 30 |     "Albanique patres, atque altae moenia Romae.\"\"\"\n",
 31 |     "\n",
 32 |     "aen = aen.replace('\\n', ' ')"
 33 |    ]
 34 |   },
 35 |   {
 36 |    "cell_type": "code",
 37 |    "execution_count": 4,
 38 |    "metadata": {
 39 |     "collapsed": true,
 40 |     "deletable": true,
 41 |     "editable": true
 42 |    },
 43 |    "outputs": [],
 44 |    "source": [
 45 |     "from cltk.tag.pos import POSTag"
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "code",
 50 |    "execution_count": 5,
 51 |    "metadata": {
 52 |     "collapsed": true,
 53 |     "deletable": true,
 54 |     "editable": true
 55 |    },
 56 |    "outputs": [],
 57 |    "source": [
 58 |     "tagger = POSTag('latin')\n",
 59 |     "\n",
 60 |     "aen_tagged = tagger.tag_ngram_123_backoff(aen)"
 61 |    ]
 62 |   },
 63 |   {
 64 |    "cell_type": "code",
 65 |    "execution_count": 6,
 66 |    "metadata": {
 67 |     "collapsed": false,
 68 |     "deletable": true,
 69 |     "editable": true
 70 |    },
 71 |    "outputs": [
 72 |     {
 73 |      "name": "stdout",
 74 |      "output_type": "stream",
 75 |      "text": [
 76 |       "[('arma', 'N-P---NA-'), ('virumque', None), ('cano', None), (',', 'U--------'), ('Troiae', None), ('qui', 'P-S---MN-'), ('primus', 'A-S---MN-'), ('ab', 'R--------'), ('oris', 'N-P---NB-'), ('Italiam', None), (',', 'U--------'), ('fato', None), ('profugus', None), (',', 'U--------'), ('Laviniaque', None), ('venit', 'V3SPIA---'), ('litora', 'N-P---NA-'), (',', 'U--------'), ('multum', 'A-S---MA-'), ('ille', 'P-S---MN-'), ('et', 'C--------'), ('terris', 'N-P---FB-'), ('iactatus', None), ('et', 'C--------'), ('alto', 'A-S---MB-'), ('vi', 'N-S---FB-'), ('superum', 'N-P---MG-'), ('saevae', None), ('memorem', 'V1SPSA---'), ('Iunonis', None), ('ob', 'R--------'), ('iram', 'N-S---FA-'), (';', None), ('multa', 'A-P---NA-'), ('quoque', 'D--------'), ('et', 'C--------'), ('bello', 'N-S---NB-'), ('passus', 'N-P---MA-'), (',', 'U--------'), ('dum', 'C--------'), ('conderet', None), ('urbem', 'N-S---FA-'), (',', 'U--------'), ('5', None), ('inferretque', None), ('deos', 'N-P---MA-'), ('Latio', None), (',', 'U--------'), ('genus', 'N-S---NN-'), ('unde', 'D--------'), ('Latinum', None), (',', 'U--------'), ('Albanique', None), ('patres', 'N-P---MV-'), (',', 'U--------'), ('atque', 'C--------'), ('altae', None), ('moenia', 'N-P---NA-'), ('Romae', None), ('.', 'U--------')]\n"
 77 |      ]
 78 |     }
 79 |    ],
 80 |    "source": [
 81 |     "print(aen_tagged)"
 82 |    ]
 83 |   },
 84 |  
 85 |    "source": [
 86 |     "# a few options\n",
 87 |     "aen_tagged = tagger.tag_crf('Gallia est omnis divisa in partes tres')"
 88 |    ]
 89 |   },
 90 |   {
 91 |    "cell_type": "code",
 92 |    "execution_count": null,
 93 |    "metadata": {
 94 |     "collapsed": true,
 95 |     "deletable": true,
 96 |     "editable": true
 97 |    },
 98 |    "outputs": [],
 99 |    "source": [
100 |     " "
101 |    ]
102 |   }
103 |  ],
104 |  "metadata": {
105 |   "kernelspec": {
106 |    "display_name": "Python 3",
107 |    "language": "python",
108 |    "name": "python3"
109 |   },
110 |   "language_info": {
111 |    "codemirror_mode": {
112 |     "name": "ipython",
113 |     "version": 3
114 |    },
115 |    "file_extension": ".py",
116 |    "mimetype": "text/x-python",
117 |    "name": "python",
118 |    "nbconvert_exporter": "python",
119 |    "pygments_lexer": "ipython3",
120 |    "version": "3.6.0"
121 |   }
122 |  },
123 |  "nbformat": 4,
124 |  "nbformat_minor": 2
125 | }
126 | 


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/Other/gensim_similar.py:
--------------------------------------------------------------------------------
 1 | import logging, sys, pprint
 2 | 
 3 | logging.basicConfig(stream=sys.stdout, level=logging.INFO)
 4 | 
 5 | from gensim.corpora import TextCorpus, MmCorpus, Dictionary
 6 | 
 7 | background_corpus = TextCorpus(input=YOUR_CORPUS)
 8 | 
 9 | background_corpus.dictionary.save(
10 |     "my_dict.dict")
11 | 
12 | MmCorpus.serialize("background_corpus.mm",
13 |     background_corpus) 
14 | 
15 | from gensim.corpora import WikiCorpus, wikicorpus
16 | 
17 | articles = "enwiki-latest-pages-articles.xml.bz2" 
18 | 
19 | wiki_corpus = WikiCorpus(articles)
20 | wiki_corpus.dictionary.save("wiki_dict.dict")
21 | 
22 | MmCorpus.serialize("wiki_corpus.mm", wiki_corpus) 
23 | 
24 | bow_corpus = MmCorpus("wiki_corpus.mm") 
25 | 
26 | dictionary = Dictionary.load("wiki_dict.dict")  
27 | 
28 | from gensim.models import LsiModel, LogEntropyModel
29 | 
30 | logent_transformation = LogEntropyModel(wiki_corpus,
31 |     id2word=dictionary) 
32 | 
33 | tokenize_func = wikicorpus.tokenize  
34 | document = "Some text to be transformed."
35 | 
36 | bow_document = dictionary.doc2bow(tokenize_func(
37 |     document))
38 | logent_document = logent_transformation[[
39 |     bow_document]]
40 | 
41 | documents = ["Some iterable", "containing multiple", "documents", "..."]
42 | bow_documents = (dictionary.doc2bow(
43 |     tokenize_func(document)) for document in documents) 
44 | logent_documents = logent_transformation[
45 |                    bow_documents] 
46 | logent_corpus = MmCorpus(corpus=logent_transformation[bow_corpus])
47 | 
48 | lsi_transformation = LsiModel(corpus=logent_corpus, id2word=dictionary,
49 |     num_features=400)
50 | 
51 | 
52 | 
53 | logent_transformation.save("logent.model")
54 | lsi_transformation.save("lsi.model")
55 | 
56 | from gensim.similarities import Similarity
57 | 
58 | index_documents = ["A bear walked in the dark forest.",
59 |              "Tall trees have many more leaves than short bushes.",
60 |              "A starship may someday travel across vast reaches of space to other stars.",
61 |              "Difference is the concept of how two or more entities are not the same."]
62 | corpus = (dictionary.doc2bow(tokenize_func(document)) for document in index_documents)
63 | 
64 | index = Similarity(corpus=lsi_transformation[logent_transformation[corpus]], num_features=400, output_prefix="shard")
65 | 
66 | print "Index corpus:"
67 | pprint.pprint(documents)
68 | 
69 | print "Similarities of index corpus documents to one another:"
70 | pprint.pprint([s for s in index])
71 | 
72 | query = "In the face of ambiguity, refuse the temptation to guess."
73 | sims_to_query = index[lsi_transformation[logent_transformation[dictionary.doc2bow(tokenize_func(query))]]]
74 | print "Similarities of index corpus documents to '%s'" % query
75 | pprint.pprint(sims_to_query)
76 | 
77 | best_score = max(sims_to_query)
78 | index = sims_to_query.tolist().index(best_score)
79 | most_similar_doc = documents[index]
80 | print "The document most similar to the query is '%s' with a score of %.2f." % (most_similar_doc, best_score)
81 | 


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/Other/readme.md:
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1 | ## Coming Soon
2 | 


--------------------------------------------------------------------------------
/Parser/readme.md:
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1 | ## Coming Soon
2 | 


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/README.md:
--------------------------------------------------------------------------------
 1 | <h2>What is NLP(Natural Language Processing?)</h2>
 2 | 
 3 | The Natural Language Processing is application of computational techniques to the analysis and synthesis of natural language and speech.
 4 | 
 5 | <li>
 6 | Summarize blocks of text using Summarizer to extract the most important and central ideas while ignoring irrelevant</li> information. 
 7 | <li>Create a chat bot using Parsey McParseface, a language parsing deep learning model made by Google that uses Point-of-Speech tagging.</li>
 8 | <li>Automatically generate keyword tags from content using AutoTag, which leverages LDA, a technique that discovers topics contained within a body of text.</li>
 9 | <li>Identify the type of entity extracted, such as it being a person, place, or organization using Named Entity Recognition.</li>
10 | <li>Use Sentiment Analysis to identify the sentiment of a string of text, from very negative to neutral to very positive.</li>
11 | <li>Reduce words to their root, or stem, using PorterStemmer, or break up text into tokens using Tokenizer.
12 | </li>
13 | 
14 | <h1>Common NLP tasks in software programs today include:</h1>
15 | 
16 | <ul>
17 | <li>Sentence segmentation, part-of-speech tagging and parsing.</li>
18 | <li>Deep analytics.</li>
19 | <li>Named entity extraction.</li>
20 | <li>Co-reference resolution.</li>
21 | </ul>
22 | 
23 | 
24 | ## These are the some Examples of some basics to Advance Natural language Processing!!
25 | ## Coming soon for all modules!! will update daily (1 day commit challange :P)
26 | 
27 | Require: Python
28 | 
29 | <!DOCTYPE html> <html> <head>  </head> <body>  <div id="myDIV" class="header">   <h2 style="margin:5px">My To Do List</h2>   </div>  <ul id="myUL">   <li>Python libraries comparison</li>   <li class="checked">python 3 support</li>   
30 | <li>Deep Learning</li>   <li>WEB </li>   <li>Custom trainer</li>   </ul>    </body> </html>
31 | 


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/spacy_nlp/.DS_Store:
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https://raw.githubusercontent.com/kashyap32/NLP-Examples/8e55f5490187b2ffb54c6f4aac96d1fa51880610/spacy_nlp/.DS_Store


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/spacy_nlp/.ipynb_checkpoints/Intro_spaCy_NLP-checkpoint.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": false,
  8 |     "deletable": true,
  9 |     "editable": true
 10 |    },
 11 |    "outputs": [],
 12 |    "source": [
 13 |     "from spacy.en import English\n",
 14 |     "parser = English()\n",
 15 |     "\n",
 16 |     "multiSentence = \"There is an art, it says, or rather, a knack to flying.\" \\\n",
 17 |     "                 \"The knack lies in learning how to throw yourself at the ground and miss.\" \\\n",
 18 |     "                 \"In the beginning the Universe was created. This has made a lot of people \"\\\n",
 19 |     "                 \"very angry and been widely regarded as a bad move.\""
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "markdown",
 24 |    "metadata": {
 25 |     "deletable": true,
 26 |     "editable": true
 27 |    },
 28 |    "source": [
 29 |     "# TOKENS,POS"
 30 |    ]
 31 |   },
 32 |   {
 33 |    "cell_type": "code",
 34 |    "execution_count": 2,
 35 |    "metadata": {
 36 |     "collapsed": false,
 37 |     "deletable": true,
 38 |     "editable": true
 39 |    },
 40 |    "outputs": [
 41 |     {
 42 |      "data": {
 43 |       "text/plain": [
 44 |        "There is an art, it says, or rather, a knack to flying.The knack lies in learning how to throw yourself at the ground and miss.In the beginning the Universe was created. This has made a lot of people very angry and been widely regarded as a bad move."
 45 |       ]
 46 |      },
 47 |      "execution_count": 2,
 48 |      "metadata": {},
 49 |      "output_type": "execute_result"
 50 |     }
 51 |    ],
 52 |    "source": [
 53 |     "parsedData = parser(multiSentence)\n",
 54 |     "parsedData"
 55 |    ]
 56 |   },
 57 |   {
 58 |    "cell_type": "code",
 59 |    "execution_count": 3,
 60 |    "metadata": {
 61 |     "collapsed": false,
 62 |     "deletable": true,
 63 |     "editable": true
 64 |    },
 65 |    "outputs": [
 66 |     {
 67 |      "name": "stdout",
 68 |      "output_type": "stream",
 69 |      "text": [
 70 |       "original: 769 There\n",
 71 |       "lowercased: 608 there\n",
 72 |       "lemma: 608 there\n",
 73 |       "shape: 684 Xxxxx\n",
 74 |       "prefix: 568 T\n",
 75 |       "suffix: 609 ere\n",
 76 |       "log probability: -7.277902603149414\n",
 77 |       "Brown cluster id: 1918\n",
 78 |       "----------------------------------------\n",
 79 |       "original: 513 is\n",
 80 |       "lowercased: 513 is\n",
 81 |       "lemma: 536 be\n",
 82 |       "shape: 505 xx\n",
 83 |       "prefix: 509 i\n",
 84 |       "suffix: 513 is\n",
 85 |       "log probability: -4.3297648429870605\n",
 86 |       "Brown cluster id: 762\n",
 87 |       "----------------------------------------\n",
 88 |       "original: 591 an\n",
 89 |       "lowercased: 591 an\n",
 90 |       "lemma: 591 an\n",
 91 |       "shape: 505 xx\n",
 92 |       "prefix: 506 a\n",
 93 |       "suffix: 591 an\n",
 94 |       "log probability: -5.953293800354004\n",
 95 |       "Brown cluster id: 3\n",
 96 |       "----------------------------------------\n",
 97 |       "original: 879 art\n",
 98 |       "lowercased: 879 art\n",
 99 |       "lemma: 879 art\n",
100 |       "shape: 502 xxx\n",
101 |       "prefix: 506 a\n",
102 |       "suffix: 879 art\n",
103 |       "log probability: -9.778430938720703\n",
104 |       "Brown cluster id: 633\n",
105 |       "----------------------------------------\n",
106 |       "original: 450 ,\n",
107 |       "lowercased: 450 ,\n",
108 |       "lemma: 450 ,\n",
109 |       "shape: 450 ,\n",
110 |       "prefix: 450 ,\n",
111 |       "suffix: 450 ,\n",
112 |       "log probability: -3.3914804458618164\n",
113 |       "Brown cluster id: 4\n",
114 |       "----------------------------------------\n",
115 |       "original: 519 it\n",
116 |       "lowercased: 519 it\n",
117 |       "lemma: 757862 -PRON-\n",
118 |       "shape: 505 xx\n",
119 |       "prefix: 509 i\n",
120 |       "suffix: 519 it\n",
121 |       "log probability: -4.5064496994018555\n",
122 |       "Brown cluster id: 474\n",
123 |       "----------------------------------------\n",
124 |       "original: 1053 says\n",
125 |       "lowercased: 1053 says\n",
126 |       "lemma: 692 say\n",
127 |       "shape: 515 xxxx\n",
128 |       "prefix: 600 s\n",
129 |       "suffix: 842 ays\n",
130 |       "log probability: -8.204558372497559\n",
131 |       "Brown cluster id: 244\n",
132 |       "----------------------------------------\n",
133 |       "original: 450 ,\n",
134 |       "lowercased: 450 ,\n",
135 |       "lemma: 450 ,\n",
136 |       "shape: 450 ,\n",
137 |       "prefix: 450 ,\n",
138 |       "suffix: 450 ,\n",
139 |       "log probability: -3.3914804458618164\n",
140 |       "Brown cluster id: 4\n",
141 |       "----------------------------------------\n",
142 |       "original: 563 or\n",
143 |       "lowercased: 563 or\n",
144 |       "lemma: 563 or\n",
145 |       "shape: 505 xx\n",
146 |       "prefix: 511 o\n",
147 |       "suffix: 563 or\n",
148 |       "log probability: -5.715355396270752\n",
149 |       "Brown cluster id: 404\n",
150 |       "----------------------------------------\n",
151 |       "original: 1017 rather\n",
152 |       "lowercased: 1017 rather\n",
153 |       "lemma: 1017 rather\n",
154 |       "shape: 515 xxxx\n",
155 |       "prefix: 659 r\n",
156 |       "suffix: 656 her\n",
157 |       "log probability: -8.124470710754395\n",
158 |       "Brown cluster id: 6698\n",
159 |       "----------------------------------------\n",
160 |       "original: 450 ,\n",
161 |       "lowercased: 450 ,\n",
162 |       "lemma: 450 ,\n",
163 |       "shape: 450 ,\n",
164 |       "prefix: 450 ,\n",
165 |       "suffix: 450 ,\n",
166 |       "log probability: -3.3914804458618164\n",
167 |       "Brown cluster id: 4\n",
168 |       "----------------------------------------\n",
169 |       "original: 506 a\n",
170 |       "lowercased: 506 a\n",
171 |       "lemma: 506 a\n",
172 |       "shape: 507 x\n",
173 |       "prefix: 506 a\n",
174 |       "suffix: 506 a\n",
175 |       "log probability: -3.9830753803253174\n",
176 |       "Brown cluster id: 19\n",
177 |       "----------------------------------------\n"
178 |      ]
179 |     }
180 |    ],
181 |    "source": [
182 |     "\n",
183 |     "for i, token in enumerate(parsedData):\n",
184 |     "    print(\"original:\", token.orth, token.orth_)\n",
185 |     "    print(\"lowercased:\", token.lower, token.lower_)\n",
186 |     "    print(\"lemma:\", token.lemma, token.lemma_)\n",
187 |     "    print(\"shape:\", token.shape, token.shape_)\n",
188 |     "    print(\"prefix:\", token.prefix, token.prefix_)\n",
189 |     "    print(\"suffix:\", token.suffix, token.suffix_)\n",
190 |     "    print(\"log probability:\", token.prob)\n",
191 |     "    print(\"Brown cluster id:\", token.cluster)\n",
192 |     "    print(\"----------------------------------------\")\n",
193 |     "    if i > 10:\n",
194 |     "        break"
195 |    ]
196 |   },
197 |   {
198 |    "cell_type": "code",
199 |    "execution_count": 6,
200 |    "metadata": {
201 |     "collapsed": false,
202 |     "deletable": true,
203 |     "editable": true
204 |    },
205 |    "outputs": [
206 |     {
207 |      "name": "stdout",
208 |      "output_type": "stream",
209 |      "text": [
210 |       "There is an art, it says, or rather, a knack to flying.\n",
211 |       "The knack lies in learning how to throw yourself at the ground and miss.\n",
212 |       "In the beginning the Universe was created.\n",
213 |       "This has made a lot of people very angry and been widely regarded as a bad move.\n"
214 |      ]
215 |     }
216 |    ],
217 |    "source": [
218 |     "sents = []\n",
219 |     "\n",
220 |     "for span in parsedData.sents:\n",
221 |     "    \n",
222 |     "    sent = ''.join(parsedData[i].string for i in range(span.start, span.end)).strip()\n",
223 |     "    sents.append(sent)\n",
224 |     "\n",
225 |     "for sentence in sents:\n",
226 |     "    print(sentence)"
227 |    ]
228 |   },
229 |   {
230 |    "cell_type": "code",
231 |    "execution_count": 7,
232 |    "metadata": {
233 |     "collapsed": false,
234 |     "deletable": true,
235 |     "editable": true
236 |    },
237 |    "outputs": [
238 |     {
239 |      "name": "stdout",
240 |      "output_type": "stream",
241 |      "text": [
242 |       "There ADV\n",
243 |       "is VERB\n",
244 |       "an DET\n",
245 |       "art NOUN\n",
246 |       ", PUNCT\n",
247 |       "it PRON\n",
248 |       "says VERB\n",
249 |       ", PUNCT\n",
250 |       "or CCONJ\n",
251 |       "rather ADV\n",
252 |       ", PUNCT\n",
253 |       "a DET\n",
254 |       "knack NOUN\n",
255 |       "to ADP\n",
256 |       "flying NOUN\n",
257 |       ". PUNCT\n"
258 |      ]
259 |     }
260 |    ],
261 |    "source": [
262 |     "for span in parsedData.sents:\n",
263 |     "    sent = [parsedData[i] for i in range(span.start, span.end)]\n",
264 |     "    break\n",
265 |     "\n",
266 |     "for token in sent:\n",
267 |     "    print(token.orth_, token.pos_)"
268 |    ]
269 |   },
270 |   {
271 |    "cell_type": "code",
272 |    "execution_count": 8,
273 |    "metadata": {
274 |     "collapsed": false,
275 |     "deletable": true,
276 |     "editable": true
277 |    },
278 |    "outputs": [
279 |     {
280 |      "name": "stdout",
281 |      "output_type": "stream",
282 |      "text": [
283 |       "The det boy [] []\n",
284 |       "boy nsubj ran ['The'] ['with']\n",
285 |       "with prep boy [] []\n",
286 |       "the det dog [] []\n",
287 |       "spotted amod dog [] []\n",
288 |       "dog nsubj ran ['the', 'spotted'] []\n",
289 |       "quickly advmod ran [] []\n",
290 |       "ran ROOT ran ['boy', 'dog', 'quickly'] ['after', '.']\n",
291 |       "after prep ran [] ['firetruck']\n",
292 |       "the det firetruck [] []\n",
293 |       "firetruck pobj after ['the'] []\n",
294 |       ". punct ran [] []\n"
295 |      ]
296 |     }
297 |    ],
298 |    "source": [
299 |     "example = \"The boy with the spotted dog quickly ran after the firetruck.\"\n",
300 |     "parsedEx = parser(example)\n",
301 |     "for token in parsedEx:\n",
302 |     "    print(token.orth_, token.dep_, token.head.orth_, [t.orth_ for t in token.lefts], [t.orth_ for t in token.rights])"
303 |    ]
304 |   },
305 |   {
306 |    "cell_type": "code",
307 |    "execution_count": 10,
308 |    "metadata": {
309 |     "collapsed": false,
310 |     "deletable": true,
311 |     "editable": true
312 |    },
313 |    "outputs": [
314 |     {
315 |      "name": "stdout",
316 |      "output_type": "stream",
317 |      "text": [
318 |       "Apple ORG\n",
319 |       "'s (not an entity)\n",
320 |       "stocks (not an entity)\n",
321 |       "dropped (not an entity)\n",
322 |       "dramatically (not an entity)\n",
323 |       "after (not an entity)\n",
324 |       "the (not an entity)\n",
325 |       "death (not an entity)\n",
326 |       "of (not an entity)\n",
327 |       "Steve PERSON\n",
328 |       "Jobs PERSON\n",
329 |       "in (not an entity)\n",
330 |       "October DATE\n",
331 |       ". (not an entity)\n",
332 |       "380 ORG Apple\n",
333 |       "377 PERSON Steve Jobs\n",
334 |       "387 DATE October\n"
335 |      ]
336 |     }
337 |    ],
338 |    "source": [
339 |     "example = \"Apple's stocks dropped dramatically after the death of Steve Jobs in October.\"\n",
340 |     "parsedEx = parser(example)\n",
341 |     "for token in parsedEx:\n",
342 |     "    print(token.orth_, token.ent_type_ if token.ent_type_ != \"\" else \"(not an entity)\")\n",
343 |     "\n",
344 |     "ents = list(parsedEx.ents)\n",
345 |     "for entity in ents:\n",
346 |     "    print(entity.label, entity.label_, ' '.join(t.orth_ for t in entity))"
347 |    ]
348 |   },
349 |   {
350 |    "cell_type": "markdown",
351 |    "metadata": {
352 |     "deletable": true,
353 |     "editable": true
354 |    },
355 |    "source": []
356 |   },
357 |   {
358 |    "cell_type": "code",
359 |    "execution_count": 11,
360 |    "metadata": {
361 |     "collapsed": false,
362 |     "deletable": true,
363 |     "editable": true
364 |    },
365 |    "outputs": [
366 |     {
367 |      "name": "stdout",
368 |      "output_type": "stream",
369 |      "text": [
370 |       "lol NOUN lol\n",
371 |       "that ADJ that\n",
372 |       "is VERB be\n",
373 |       "rly ADV rly\n",
374 |       "funny ADJ funny\n",
375 |       ":) PUNCT :)\n",
376 |       "This DET this\n",
377 |       "is VERB be\n",
378 |       "gr8 VERB gr8\n",
379 |       "i PRON i\n",
380 |       "rate VERB rate\n",
381 |       "it PRON -PRON-\n",
382 |       "8/8 NUM 8/8\n",
383 |       "! PUNCT !\n",
384 |       "! PUNCT !\n",
385 |       "! PUNCT !\n"
386 |      ]
387 |     }
388 |    ],
389 |    "source": [
390 |     "messyData = \"lol that is rly funny :) This is gr8 i rate it 8/8!!!\"\n",
391 |     "parsedData = parser(messyData)\n",
392 |     "for token in parsedData:\n",
393 |     "    print(token.orth_, token.pos_, token.lemma_)\n",
394 |     "    \n"
395 |    ]
396 |   },
397 |   {
398 |    "cell_type": "markdown",
399 |    "metadata": {
400 |     "deletable": true,
401 |     "editable": true
402 |    },
403 |    "source": []
404 |   },
405 |   {
406 |    "cell_type": "code",
407 |    "execution_count": 17,
408 |    "metadata": {
409 |     "collapsed": false,
410 |     "deletable": true,
411 |     "editable": true
412 |    },
413 |    "outputs": [
414 |     {
415 |      "name": "stderr",
416 |      "output_type": "stream",
417 |      "text": [
418 |       "-c:6: RuntimeWarning: invalid value encountered in float_scalars\n"
419 |      ]
420 |     },
421 |     {
422 |      "name": "stdout",
423 |      "output_type": "stream",
424 |      "text": [
425 |       "Top 20 most similar words to NASA:\n",
426 |       "consistency\n",
427 |       "hotter\n",
428 |       "journey\n",
429 |       "featured\n",
430 |       "ref\n",
431 |       "puppies\n",
432 |       "artwork\n",
433 |       "crystal\n",
434 |       "defenses\n",
435 |       "helmet\n",
436 |       "slowed\n",
437 |       "promotion\n",
438 |       "similarities\n",
439 |       "boner\n",
440 |       "flex\n",
441 |       "flew\n",
442 |       "adapter\n",
443 |       "acquire\n",
444 |       "quarters\n",
445 |       "sizes\n",
446 |       "\n",
447 |       "----------------------------\n",
448 |       "Top 3 closest results for king - man + woman:\n",
449 |       "queen\n",
450 |       "kings\n",
451 |       "princess\n"
452 |      ]
453 |     }
454 |    ],
455 |    "source": [
456 |     "from numpy import dot\n",
457 |     "from numpy.linalg import norm\n",
458 |     "\n",
459 |     "nasa = parser.vocab['NASA']\n",
460 |     "\n",
461 |     "cosine = lambda v1, v2: dot(v1, v2) / (norm(v1) * norm(v2))\n",
462 |     "\n",
463 |     "allWords = list({w for w in parser.vocab if w.has_vector and w.orth_.islower() and w.lower_ != \"nasa\"})\n",
464 |     "\n",
465 |     "allWords.sort(key=lambda w: cosine(w.vector, nasa. vector))\n",
466 |     "allWords.reverse()\n",
467 |     "print(\"Top 20 most similar words to NASA:\")\n",
468 |     "for word in allWords[:20]:   \n",
469 |     "    print(word.orth_)\n",
470 |     "    \n",
471 |     "\n",
472 |     "king = parser.vocab['king']\n",
473 |     "man = parser.vocab['man']\n",
474 |     "woman = parser.vocab['woman']\n",
475 |     "\n",
476 |     "result = king.vector - man.vector + woman.vector\n",
477 |     "\n",
478 |     "allWords = list({w for w in parser.vocab if w.has_vector and w.orth_.islower() and w.lower_ != \"king\" and w.lower_ != \"man\" and w.lower_ != \"woman\"})\n",
479 |     "allWords.sort(key=lambda w: cosine(w.vector, result))\n",
480 |     "allWords.reverse()\n",
481 |     "print(\"\\n----------------------------\\nTop 3 closest results for king - man + woman:\")\n",
482 |     "for word in allWords[:3]:   \n",
483 |     "    print(word.orth_)\n",
484 |     "    \n",
485 |     "# it got it! Queen!"
486 |    ]
487 |   },
488 |   {
489 |    "cell_type": "markdown",
490 |    "metadata": {
491 |     "deletable": true,
492 |     "editable": true
493 |    },
494 |    "source": []
495 |   },
496 |   {
497 |    "cell_type": "code",
498 |    "execution_count": 20,
499 |    "metadata": {
500 |     "collapsed": false,
501 |     "deletable": true,
502 |     "editable": true
503 |    },
504 |    "outputs": [
505 |     {
506 |      "name": "stdout",
507 |      "output_type": "stream",
508 |      "text": [
509 |       "[('he', 'shot', 'me'), ('he', 'shot', 'sister'), ('brother', 'shot', 'me'), ('brother', 'shot', 'sister')]\n",
510 |       "[('watches', 'are', 'idea'), ('which', 'was', 'this'), ('it', 'was', 'pieces')]\n"
511 |      ]
512 |     }
513 |    ],
514 |    "source": []
515 |   },
516 |   {
517 |    "cell_type": "markdown",
518 |    "metadata": {
519 |     "deletable": true,
520 |     "editable": true
521 |    },
522 |    "source": [
523 |     "# machine learning example with text"
524 |    ]
525 |   },
526 |   {
527 |    "cell_type": "code",
528 |    "execution_count": 26,
529 |    "metadata": {
530 |     "collapsed": false,
531 |     "deletable": true,
532 |     "editable": true
533 |    },
534 |    "outputs": [
535 |     {
536 |      "name": "stdout",
537 |      "output_type": "stream",
538 |      "text": [
539 |       "----------------------------------------------------------------------------------------------\n",
540 |       "results:\n",
541 |       "i h8 riting comprehensibly #skoolsux : twitter\n",
542 |       "planets and stars and rockets and stuff : space\n",
543 |       "accuracy: 1.0\n",
544 |       "----------------------------------------------------------------------------------------------\n",
545 |       "Top 10 features used to predict: \n",
546 |       "Class 1 best: \n",
547 |       "(-0.5317457349252519, 'planet')\n",
548 |       "(-0.35387821917377232, 'space')\n",
549 |       "(-0.21950314690393238, 'mar')\n",
550 |       "(-0.21950314690393238, 'red')\n",
551 |       "(-0.15678762293683152, 'earth')\n",
552 |       "(-0.15678762293683152, 'launch')\n",
553 |       "(-0.15678762293683152, 'rocket')\n",
554 |       "(-0.14909864994270897, 'great')\n",
555 |       "(-0.14909864994270897, 'love')\n",
556 |       "(-0.099774045796133873, 'blue')\n",
557 |       "Class 2 best: \n",
558 |       "(0.40866490475230544, 'twitter')\n",
559 |       "(0.35268312478964314, '@mention')\n",
560 |       "(0.22672529221454771, 'lol')\n",
561 |       "(0.22672529221454771, 'gr8')\n",
562 |       "(0.20433286130934575, 'social')\n",
563 |       "(0.20433286130934575, 'medium')\n",
564 |       "(0.20433204344295969, 'reddit')\n",
565 |       "(0.20433204344295969, 'fun')\n",
566 |       "(0.12595783257509538, 'window')\n",
567 |       "(0.12595783257509538, 'u')\n",
568 |       "----------------------------------------------------------------------------------------------\n",
569 |       "The original data as it appeared to the classifier after tokenizing, lemmatizing, stoplisting, etc\n",
570 |       "Sample 0: ('great', 1)('space', 2)('love', 1)\n",
571 |       "Sample 1: ('exist', 1)('glad', 1)('cool', 1)('planet', 1)('space', 1)\n",
572 |       "Sample 2: ('gr8', 1)('@mention', 1)('lol', 1)\n",
573 |       "Sample 3: ('fun', 1)('reddit', 1)('twitter', 1)\n",
574 |       "Sample 4: ('red', 1)('mar', 1)('planet', 1)\n",
575 |       "Sample 5: ('9', 1)('window', 1)('skip', 1)('u', 1)('@mention', 1)\n",
576 |       "Sample 6: ('earth', 1)('launch', 1)('rocket', 1)('planet', 1)\n",
577 |       "Sample 7: ('medium', 1)('social', 1)('twitter', 1)\n",
578 |       "Sample 8: ('hashtag', 1)('@mention', 3)\n",
579 |       "Sample 9: ('green', 1)('blue', 1)('little', 1)('sun', 1)('orbit', 1)('planet', 1)\n"
580 |      ]
581 |     }
582 |    ],
583 |    "source": [
584 |     "from sklearn.feature_extraction.text import CountVectorizer\n",
585 |     "from sklearn.base import TransformerMixin\n",
586 |     "from sklearn.pipeline import Pipeline\n",
587 |     "from sklearn.svm import LinearSVC\n",
588 |     "from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS\n",
589 |     "from sklearn.metrics import accuracy_score\n",
590 |     "from nltk.corpus import stopwords\n",
591 |     "import string\n",
592 |     "import re\n",
593 |     "\n",
594 |     "STOPLIST = set(stopwords.words('english') + [\"n't\", \"'s\", \"'m\", \"ca\"] + list(ENGLISH_STOP_WORDS))\n",
595 |     "SYMBOLS = \" \".join(string.punctuation).split(\" \") + [\"-----\", \"---\", \"...\", \"“\", \"”\", \"'ve\"]\n",
596 |     "\n",
597 |     "class CleanTextTransformer(TransformerMixin):\n",
598 |     "  \n",
599 |     "\n",
600 |     "    def transform(self, X, **transform_params):\n",
601 |     "        return [cleanText(text) for text in X]\n",
602 |     "\n",
603 |     "    def fit(self, X, y=None, **fit_params):\n",
604 |     "        return self\n",
605 |     "\n",
606 |     "    def get_params(self, deep=True):\n",
607 |     "        return {}\n",
608 |     "    \n",
609 |     "def cleanText(text):\n",
610 |     "    text = text.strip().replace(\"\\n\", \" \").replace(\"\\r\", \" \")\n",
611 |     "    \n",
612 |     "    mentionFinder = re.compile(r\"@[a-z0-9_]{1,15}\", re.IGNORECASE)\n",
613 |     "    text = mentionFinder.sub(\"@MENTION\", text)\n",
614 |     "    \n",
615 |     "    text = text.replace(\"&amp;\", \"and\").replace(\"&gt;\", \">\").replace(\"&lt;\", \"<\")\n",
616 |     "    \n",
617 |     "    # lowercase\n",
618 |     "    text = text.lower()\n",
619 |     "\n",
620 |     "    return text\n",
621 |     "\n",
622 |     "def tokenizeText(sample):\n",
623 |     "\n",
624 |     "    tokens = parser(sample)\n",
625 |     "\n",
626 |     "    lemmas = []\n",
627 |     "    for tok in tokens:\n",
628 |     "        lemmas.append(tok.lemma_.lower().strip() if tok.lemma_ != \"-PRON-\" else tok.lower_)\n",
629 |     "    tokens = lemmas\n",
630 |     "\n",
631 |     "    tokens = [tok for tok in tokens if tok not in STOPLIST]\n",
632 |     "\n",
633 |     "    tokens = [tok for tok in tokens if tok not in SYMBOLS]\n",
634 |     "\n",
635 |     "    while \"\" in tokens:\n",
636 |     "        tokens.remove(\"\")\n",
637 |     "    while \" \" in tokens:\n",
638 |     "        tokens.remove(\" \")\n",
639 |     "    while \"\\n\" in tokens:\n",
640 |     "        tokens.remove(\"\\n\")\n",
641 |     "    while \"\\n\\n\" in tokens:\n",
642 |     "        tokens.remove(\"\\n\\n\")\n",
643 |     "\n",
644 |     "    return tokens\n",
645 |     "\n",
646 |     "# def printNMostInformative(vectorizer, clf, N):\n",
647 |     "#     feature_names = vectorizer.get_feature_names()\n",
648 |     "#     coefs_with_fns = sorted(zip(clf.coef_[0], feature_names))\n",
649 |     "#     topClass1 = coefs_with_fns[:N]\n",
650 |     "#     topClass2 = coefs_with_fns[:-(N + 1):-1]\n",
651 |     "#     print(\"Class 1 best: \")\n",
652 |     "#     for feat in topClass1:\n",
653 |     "#         print(feat)\n",
654 |     "#     print(\"Class 2 best: \")\n",
655 |     "#     for feat in topClass2:\n",
656 |     "#         print(feat)\n",
657 |     "\n",
658 |     "vectorizer = CountVectorizer(tokenizer=tokenizeText, ngram_range=(1,1))\n",
659 |     "clf = LinearSVC()\n",
660 |     "model = Pipeline([('cleanText', CleanTextTransformer()), ('vectorizer', vectorizer), ('clf', clf)])\n",
661 |     "\n",
662 |     "train = [\"I love space. Space is great.\", \"Planets are cool. I am glad they exist in space\", \"lol @twitterdude that is gr8\", \n",
663 |     "        \"twitter &amp; reddit are fun.\", \"Mars is a planet. It is red.\", \"@Microsoft: y u skip windows 9?\", \"Rockets launch from Earth and go to other planets.\",\n",
664 |     "        \"twitter social media &gt; &lt;\", \"@someguy @somegirl @twitter #hashtag\", \"Orbiting the sun is a little blue-green planet.\"]\n",
665 |     "labelsTrain = [\"space\", \"space\", \"twitter\", \"twitter\", \"space\", \"twitter\", \"space\", \"twitter\", \"twitter\", \"space\"]\n",
666 |     "\n",
667 |     "test = [\"i h8 riting comprehensibly #skoolsux\", \"planets and stars and rockets and stuff\"]\n",
668 |     "labelsTest = [\"twitter\", \"space\"]\n",
669 |     "\n",
670 |     "model.fit(train, labelsTrain)\n",
671 |     "\n",
672 |     "preds = model.predict(test)\n",
673 |     "print(\"----------------------------------------------------------------------------------------------\")\n",
674 |     "print(\"results:\")\n",
675 |     "for (sample, pred) in zip(test, preds):\n",
676 |     "    print(sample, \":\", pred)\n",
677 |     "print(\"accuracy:\", accuracy_score(labelsTest, preds))\n",
678 |     "\n",
679 |     "print(\"----------------------------------------------------------------------------------------------\")\n",
680 |     "print(\"Top 10 features used to predict: \")\n",
681 |     "# show the top features\n",
682 |     "printNMostInformative(vectorizer, clf, 10)\n",
683 |     "\n",
684 |     "print(\"----------------------------------------------------------------------------------------------\")\n",
685 |     "print(\"The original data as it appeared to the classifier after tokenizing, lemmatizing, stoplisting, etc\")\n",
686 |     "# let's see what the pipeline was transforming the data into\n",
687 |     "pipe = Pipeline([('cleanText', CleanTextTransformer()), ('vectorizer', vectorizer)])\n",
688 |     "transform = pipe.fit_transform(train, labelsTrain)\n",
689 |     "\n",
690 |     "# get the features that the vectorizer learned (its vocabulary)\n",
691 |     "vocab = vectorizer.get_feature_names()\n",
692 |     "\n",
693 |     "# the values from the vectorizer transformed data (each item is a row,column index with value as # times occuring in the sample, stored as a sparse matrix)\n",
694 |     "for i in range(len(train)):\n",
695 |     "    s = \"\"\n",
696 |     "    indexIntoVocab = transform.indices[transform.indptr[i]:transform.indptr[i+1]]\n",
697 |     "    numOccurences = transform.data[transform.indptr[i]:transform.indptr[i+1]]\n",
698 |     "    for idx, num in zip(indexIntoVocab, numOccurences):\n",
699 |     "        s += str((vocab[idx], num))\n",
700 |     "    print(\"Sample {}: {}\".format(i, s))"
701 |    ]
702 |   },
703 |   {
704 |    "cell_type": "code",
705 |    "execution_count": null,
706 |    "metadata": {
707 |     "collapsed": false,
708 |     "deletable": true,
709 |     "editable": true
710 |    },
711 |    "outputs": [],
712 |    "source": []
713 |   }
714 |  ],
715 |  "metadata": {
716 |   "kernelspec": {
717 |    "display_name": "IPython (Python 3)",
718 |    "language": "python",
719 |    "name": "python3"
720 |   },
721 |   "language_info": {
722 |    "codemirror_mode": {
723 |     "name": "ipython",
724 |     "version": 3
725 |    },
726 |    "file_extension": ".py",
727 |    "mimetype": "text/x-python",
728 |    "name": "python",
729 |    "nbconvert_exporter": "python",
730 |    "pygments_lexer": "ipython3",
731 |    "version": "3.5.3"
732 |   }
733 |  },
734 |  "nbformat": 4,
735 |  "nbformat_minor": 0
736 | }
737 | 


--------------------------------------------------------------------------------
/spacy_nlp/Intro_spaCy_NLP.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": false,
  8 |     "deletable": true,
  9 |     "editable": true
 10 |    },
 11 |    "outputs": [],
 12 |    "source": [
 13 |     "from spacy.en import English\n",
 14 |     "parser = English()\n",
 15 |     "\n",
 16 |     "multiSentence = \"There is an art, it says, or rather, a knack to flying.\" \\\n",
 17 |     "                 \"The knack lies in learning how to throw yourself at the ground and miss.\" \\\n",
 18 |     "                 \"In the beginning the Universe was created. This has made a lot of people \"\\\n",
 19 |     "                 \"very angry and been widely regarded as a bad move.\""
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "markdown",
 24 |    "metadata": {
 25 |     "deletable": true,
 26 |     "editable": true
 27 |    },
 28 |    "source": [
 29 |     "# TOKENS,POS"
 30 |    ]
 31 |   },
 32 |   {
 33 |    "cell_type": "code",
 34 |    "execution_count": 2,
 35 |    "metadata": {
 36 |     "collapsed": false,
 37 |     "deletable": true,
 38 |     "editable": true
 39 |    },
 40 |    "outputs": [
 41 |     {
 42 |      "data": {
 43 |       "text/plain": [
 44 |        "There is an art, it says, or rather, a knack to flying.The knack lies in learning how to throw yourself at the ground and miss.In the beginning the Universe was created. This has made a lot of people very angry and been widely regarded as a bad move."
 45 |       ]
 46 |      },
 47 |      "execution_count": 2,
 48 |      "metadata": {},
 49 |      "output_type": "execute_result"
 50 |     }
 51 |    ],
 52 |    "source": [
 53 |     "parsedData = parser(multiSentence)\n",
 54 |     "parsedData"
 55 |    ]
 56 |   },
 57 |   {
 58 |    "cell_type": "code",
 59 |    "execution_count": 3,
 60 |    "metadata": {
 61 |     "collapsed": false,
 62 |     "deletable": true,
 63 |     "editable": true
 64 |    },
 65 |    "outputs": [
 66 |     {
 67 |      "name": "stdout",
 68 |      "output_type": "stream",
 69 |      "text": [
 70 |       "original: 769 There\n",
 71 |       "lowercased: 608 there\n",
 72 |       "lemma: 608 there\n",
 73 |       "shape: 684 Xxxxx\n",
 74 |       "prefix: 568 T\n",
 75 |       "suffix: 609 ere\n",
 76 |       "log probability: -7.277902603149414\n",
 77 |       "Brown cluster id: 1918\n",
 78 |       "----------------------------------------\n",
 79 |       "original: 513 is\n",
 80 |       "lowercased: 513 is\n",
 81 |       "lemma: 536 be\n",
 82 |       "shape: 505 xx\n",
 83 |       "prefix: 509 i\n",
 84 |       "suffix: 513 is\n",
 85 |       "log probability: -4.3297648429870605\n",
 86 |       "Brown cluster id: 762\n",
 87 |       "----------------------------------------\n",
 88 |       "original: 591 an\n",
 89 |       "lowercased: 591 an\n",
 90 |       "lemma: 591 an\n",
 91 |       "shape: 505 xx\n",
 92 |       "prefix: 506 a\n",
 93 |       "suffix: 591 an\n",
 94 |       "log probability: -5.953293800354004\n",
 95 |       "Brown cluster id: 3\n",
 96 |       "----------------------------------------\n",
 97 |       "original: 879 art\n",
 98 |       "lowercased: 879 art\n",
 99 |       "lemma: 879 art\n",
100 |       "shape: 502 xxx\n",
101 |       "prefix: 506 a\n",
102 |       "suffix: 879 art\n",
103 |       "log probability: -9.778430938720703\n",
104 |       "Brown cluster id: 633\n",
105 |       "----------------------------------------\n",
106 |       "original: 450 ,\n",
107 |       "lowercased: 450 ,\n",
108 |       "lemma: 450 ,\n",
109 |       "shape: 450 ,\n",
110 |       "prefix: 450 ,\n",
111 |       "suffix: 450 ,\n",
112 |       "log probability: -3.3914804458618164\n",
113 |       "Brown cluster id: 4\n",
114 |       "----------------------------------------\n",
115 |       "original: 519 it\n",
116 |       "lowercased: 519 it\n",
117 |       "lemma: 757862 -PRON-\n",
118 |       "shape: 505 xx\n",
119 |       "prefix: 509 i\n",
120 |       "suffix: 519 it\n",
121 |       "log probability: -4.5064496994018555\n",
122 |       "Brown cluster id: 474\n",
123 |       "----------------------------------------\n",
124 |       "original: 1053 says\n",
125 |       "lowercased: 1053 says\n",
126 |       "lemma: 692 say\n",
127 |       "shape: 515 xxxx\n",
128 |       "prefix: 600 s\n",
129 |       "suffix: 842 ays\n",
130 |       "log probability: -8.204558372497559\n",
131 |       "Brown cluster id: 244\n",
132 |       "----------------------------------------\n",
133 |       "original: 450 ,\n",
134 |       "lowercased: 450 ,\n",
135 |       "lemma: 450 ,\n",
136 |       "shape: 450 ,\n",
137 |       "prefix: 450 ,\n",
138 |       "suffix: 450 ,\n",
139 |       "log probability: -3.3914804458618164\n",
140 |       "Brown cluster id: 4\n",
141 |       "----------------------------------------\n",
142 |       "original: 563 or\n",
143 |       "lowercased: 563 or\n",
144 |       "lemma: 563 or\n",
145 |       "shape: 505 xx\n",
146 |       "prefix: 511 o\n",
147 |       "suffix: 563 or\n",
148 |       "log probability: -5.715355396270752\n",
149 |       "Brown cluster id: 404\n",
150 |       "----------------------------------------\n",
151 |       "original: 1017 rather\n",
152 |       "lowercased: 1017 rather\n",
153 |       "lemma: 1017 rather\n",
154 |       "shape: 515 xxxx\n",
155 |       "prefix: 659 r\n",
156 |       "suffix: 656 her\n",
157 |       "log probability: -8.124470710754395\n",
158 |       "Brown cluster id: 6698\n",
159 |       "----------------------------------------\n",
160 |       "original: 450 ,\n",
161 |       "lowercased: 450 ,\n",
162 |       "lemma: 450 ,\n",
163 |       "shape: 450 ,\n",
164 |       "prefix: 450 ,\n",
165 |       "suffix: 450 ,\n",
166 |       "log probability: -3.3914804458618164\n",
167 |       "Brown cluster id: 4\n",
168 |       "----------------------------------------\n",
169 |       "original: 506 a\n",
170 |       "lowercased: 506 a\n",
171 |       "lemma: 506 a\n",
172 |       "shape: 507 x\n",
173 |       "prefix: 506 a\n",
174 |       "suffix: 506 a\n",
175 |       "log probability: -3.9830753803253174\n",
176 |       "Brown cluster id: 19\n",
177 |       "----------------------------------------\n"
178 |      ]
179 |     }
180 |    ],
181 |    "source": [
182 |     "\n",
183 |     "for i, token in enumerate(parsedData):\n",
184 |     "    print(\"original:\", token.orth, token.orth_)\n",
185 |     "    print(\"lowercased:\", token.lower, token.lower_)\n",
186 |     "    print(\"lemma:\", token.lemma, token.lemma_)\n",
187 |     "    print(\"shape:\", token.shape, token.shape_)\n",
188 |     "    print(\"prefix:\", token.prefix, token.prefix_)\n",
189 |     "    print(\"suffix:\", token.suffix, token.suffix_)\n",
190 |     "    print(\"log probability:\", token.prob)\n",
191 |     "    print(\"Brown cluster id:\", token.cluster)\n",
192 |     "    print(\"----------------------------------------\")\n",
193 |     "    if i > 10:\n",
194 |     "        break"
195 |    ]
196 |   },
197 |   {
198 |    "cell_type": "code",
199 |    "execution_count": 6,
200 |    "metadata": {
201 |     "collapsed": false,
202 |     "deletable": true,
203 |     "editable": true
204 |    },
205 |    "outputs": [
206 |     {
207 |      "name": "stdout",
208 |      "output_type": "stream",
209 |      "text": [
210 |       "There is an art, it says, or rather, a knack to flying.\n",
211 |       "The knack lies in learning how to throw yourself at the ground and miss.\n",
212 |       "In the beginning the Universe was created.\n",
213 |       "This has made a lot of people very angry and been widely regarded as a bad move.\n"
214 |      ]
215 |     }
216 |    ],
217 |    "source": [
218 |     "sents = []\n",
219 |     "\n",
220 |     "for span in parsedData.sents:\n",
221 |     "    \n",
222 |     "    sent = ''.join(parsedData[i].string for i in range(span.start, span.end)).strip()\n",
223 |     "    sents.append(sent)\n",
224 |     "\n",
225 |     "for sentence in sents:\n",
226 |     "    print(sentence)"
227 |    ]
228 |   },
229 |   {
230 |    "cell_type": "code",
231 |    "execution_count": 7,
232 |    "metadata": {
233 |     "collapsed": false,
234 |     "deletable": true,
235 |     "editable": true
236 |    },
237 |    "outputs": [
238 |     {
239 |      "name": "stdout",
240 |      "output_type": "stream",
241 |      "text": [
242 |       "There ADV\n",
243 |       "is VERB\n",
244 |       "an DET\n",
245 |       "art NOUN\n",
246 |       ", PUNCT\n",
247 |       "it PRON\n",
248 |       "says VERB\n",
249 |       ", PUNCT\n",
250 |       "or CCONJ\n",
251 |       "rather ADV\n",
252 |       ", PUNCT\n",
253 |       "a DET\n",
254 |       "knack NOUN\n",
255 |       "to ADP\n",
256 |       "flying NOUN\n",
257 |       ". PUNCT\n"
258 |      ]
259 |     }
260 |    ],
261 |    "source": [
262 |     "for span in parsedData.sents:\n",
263 |     "    sent = [parsedData[i] for i in range(span.start, span.end)]\n",
264 |     "    break\n",
265 |     "\n",
266 |     "for token in sent:\n",
267 |     "    print(token.orth_, token.pos_)"
268 |    ]
269 |   },
270 |   {
271 |    "cell_type": "code",
272 |    "execution_count": 8,
273 |    "metadata": {
274 |     "collapsed": false,
275 |     "deletable": true,
276 |     "editable": true
277 |    },
278 |    "outputs": [
279 |     {
280 |      "name": "stdout",
281 |      "output_type": "stream",
282 |      "text": [
283 |       "The det boy [] []\n",
284 |       "boy nsubj ran ['The'] ['with']\n",
285 |       "with prep boy [] []\n",
286 |       "the det dog [] []\n",
287 |       "spotted amod dog [] []\n",
288 |       "dog nsubj ran ['the', 'spotted'] []\n",
289 |       "quickly advmod ran [] []\n",
290 |       "ran ROOT ran ['boy', 'dog', 'quickly'] ['after', '.']\n",
291 |       "after prep ran [] ['firetruck']\n",
292 |       "the det firetruck [] []\n",
293 |       "firetruck pobj after ['the'] []\n",
294 |       ". punct ran [] []\n"
295 |      ]
296 |     }
297 |    ],
298 |    "source": [
299 |     "example = \"The boy with the spotted dog quickly ran after the firetruck.\"\n",
300 |     "parsedEx = parser(example)\n",
301 |     "for token in parsedEx:\n",
302 |     "    print(token.orth_, token.dep_, token.head.orth_, [t.orth_ for t in token.lefts], [t.orth_ for t in token.rights])"
303 |    ]
304 |   },
305 |   {
306 |    "cell_type": "code",
307 |    "execution_count": 10,
308 |    "metadata": {
309 |     "collapsed": false,
310 |     "deletable": true,
311 |     "editable": true
312 |    },
313 |    "outputs": [
314 |     {
315 |      "name": "stdout",
316 |      "output_type": "stream",
317 |      "text": [
318 |       "Apple ORG\n",
319 |       "'s (not an entity)\n",
320 |       "stocks (not an entity)\n",
321 |       "dropped (not an entity)\n",
322 |       "dramatically (not an entity)\n",
323 |       "after (not an entity)\n",
324 |       "the (not an entity)\n",
325 |       "death (not an entity)\n",
326 |       "of (not an entity)\n",
327 |       "Steve PERSON\n",
328 |       "Jobs PERSON\n",
329 |       "in (not an entity)\n",
330 |       "October DATE\n",
331 |       ". (not an entity)\n",
332 |       "380 ORG Apple\n",
333 |       "377 PERSON Steve Jobs\n",
334 |       "387 DATE October\n"
335 |      ]
336 |     }
337 |    ],
338 |    "source": [
339 |     "example = \"Apple's stocks dropped dramatically after the death of Steve Jobs in October.\"\n",
340 |     "parsedEx = parser(example)\n",
341 |     "for token in parsedEx:\n",
342 |     "    print(token.orth_, token.ent_type_ if token.ent_type_ != \"\" else \"(not an entity)\")\n",
343 |     "\n",
344 |     "ents = list(parsedEx.ents)\n",
345 |     "for entity in ents:\n",
346 |     "    print(entity.label, entity.label_, ' '.join(t.orth_ for t in entity))"
347 |    ]
348 |   },
349 |   {
350 |    "cell_type": "markdown",
351 |    "metadata": {
352 |     "deletable": true,
353 |     "editable": true
354 |    },
355 |    "source": []
356 |   },
357 |   {
358 |    "cell_type": "code",
359 |    "execution_count": 11,
360 |    "metadata": {
361 |     "collapsed": false,
362 |     "deletable": true,
363 |     "editable": true
364 |    },
365 |    "outputs": [
366 |     {
367 |      "name": "stdout",
368 |      "output_type": "stream",
369 |      "text": [
370 |       "lol NOUN lol\n",
371 |       "that ADJ that\n",
372 |       "is VERB be\n",
373 |       "rly ADV rly\n",
374 |       "funny ADJ funny\n",
375 |       ":) PUNCT :)\n",
376 |       "This DET this\n",
377 |       "is VERB be\n",
378 |       "gr8 VERB gr8\n",
379 |       "i PRON i\n",
380 |       "rate VERB rate\n",
381 |       "it PRON -PRON-\n",
382 |       "8/8 NUM 8/8\n",
383 |       "! PUNCT !\n",
384 |       "! PUNCT !\n",
385 |       "! PUNCT !\n"
386 |      ]
387 |     }
388 |    ],
389 |    "source": [
390 |     "messyData = \"lol that is rly funny :) This is gr8 i rate it 8/8!!!\"\n",
391 |     "parsedData = parser(messyData)\n",
392 |     "for token in parsedData:\n",
393 |     "    print(token.orth_, token.pos_, token.lemma_)\n",
394 |     "    \n"
395 |    ]
396 |   },
397 |   {
398 |    "cell_type": "markdown",
399 |    "metadata": {
400 |     "deletable": true,
401 |     "editable": true
402 |    },
403 |    "source": []
404 |   },
405 |   {
406 |    "cell_type": "code",
407 |    "execution_count": 17,
408 |    "metadata": {
409 |     "collapsed": false,
410 |     "deletable": true,
411 |     "editable": true
412 |    },
413 |    "outputs": [
414 |     {
415 |      "name": "stderr",
416 |      "output_type": "stream",
417 |      "text": [
418 |       "-c:6: RuntimeWarning: invalid value encountered in float_scalars\n"
419 |      ]
420 |     },
421 |     {
422 |      "name": "stdout",
423 |      "output_type": "stream",
424 |      "text": [
425 |       "Top 20 most similar words to NASA:\n",
426 |       "consistency\n",
427 |       "hotter\n",
428 |       "journey\n",
429 |       "featured\n",
430 |       "ref\n",
431 |       "puppies\n",
432 |       "artwork\n",
433 |       "crystal\n",
434 |       "defenses\n",
435 |       "helmet\n",
436 |       "slowed\n",
437 |       "promotion\n",
438 |       "similarities\n",
439 |       "boner\n",
440 |       "flex\n",
441 |       "flew\n",
442 |       "adapter\n",
443 |       "acquire\n",
444 |       "quarters\n",
445 |       "sizes\n",
446 |       "\n",
447 |       "----------------------------\n",
448 |       "Top 3 closest results for king - man + woman:\n",
449 |       "queen\n",
450 |       "kings\n",
451 |       "princess\n"
452 |      ]
453 |     }
454 |    ],
455 |    "source": [
456 |     "from numpy import dot\n",
457 |     "from numpy.linalg import norm\n",
458 |     "\n",
459 |     "nasa = parser.vocab['NASA']\n",
460 |     "\n",
461 |     "cosine = lambda v1, v2: dot(v1, v2) / (norm(v1) * norm(v2))\n",
462 |     "\n",
463 |     "allWords = list({w for w in parser.vocab if w.has_vector and w.orth_.islower() and w.lower_ != \"nasa\"})\n",
464 |     "\n",
465 |     "allWords.sort(key=lambda w: cosine(w.vector, nasa. vector))\n",
466 |     "allWords.reverse()\n",
467 |     "print(\"Top 20 most similar words to NASA:\")\n",
468 |     "for word in allWords[:20]:   \n",
469 |     "    print(word.orth_)\n",
470 |     "    \n",
471 |     "\n",
472 |     "king = parser.vocab['king']\n",
473 |     "man = parser.vocab['man']\n",
474 |     "woman = parser.vocab['woman']\n",
475 |     "\n",
476 |     "result = king.vector - man.vector + woman.vector\n",
477 |     "\n",
478 |     "allWords = list({w for w in parser.vocab if w.has_vector and w.orth_.islower() and w.lower_ != \"king\" and w.lower_ != \"man\" and w.lower_ != \"woman\"})\n",
479 |     "allWords.sort(key=lambda w: cosine(w.vector, result))\n",
480 |     "allWords.reverse()\n",
481 |     "print(\"\\n----------------------------\\nTop 3 closest results for king - man + woman:\")\n",
482 |     "for word in allWords[:3]:   \n",
483 |     "    print(word.orth_)\n",
484 |     "    \n",
485 |     "# it got it! Queen!"
486 |    ]
487 |   },
488 |   {
489 |    "cell_type": "markdown",
490 |    "metadata": {
491 |     "deletable": true,
492 |     "editable": true
493 |    },
494 |    "source": []
495 |   },
496 |   {
497 |    "cell_type": "code",
498 |    "execution_count": 20,
499 |    "metadata": {
500 |     "collapsed": false,
501 |     "deletable": true,
502 |     "editable": true
503 |    },
504 |    "outputs": [
505 |     {
506 |      "name": "stdout",
507 |      "output_type": "stream",
508 |      "text": [
509 |       "[('he', 'shot', 'me'), ('he', 'shot', 'sister'), ('brother', 'shot', 'me'), ('brother', 'shot', 'sister')]\n",
510 |       "[('watches', 'are', 'idea'), ('which', 'was', 'this'), ('it', 'was', 'pieces')]\n"
511 |      ]
512 |     }
513 |    ],
514 |    "source": []
515 |   },
516 |   {
517 |    "cell_type": "markdown",
518 |    "metadata": {
519 |     "deletable": true,
520 |     "editable": true
521 |    },
522 |    "source": [
523 |     "# machine learning example with text"
524 |    ]
525 |   },
526 |   {
527 |    "cell_type": "code",
528 |    "execution_count": 26,
529 |    "metadata": {
530 |     "collapsed": false,
531 |     "deletable": true,
532 |     "editable": true
533 |    },
534 |    "outputs": [
535 |     {
536 |      "name": "stdout",
537 |      "output_type": "stream",
538 |      "text": [
539 |       "----------------------------------------------------------------------------------------------\n",
540 |       "results:\n",
541 |       "i h8 riting comprehensibly #skoolsux : twitter\n",
542 |       "planets and stars and rockets and stuff : space\n",
543 |       "accuracy: 1.0\n",
544 |       "----------------------------------------------------------------------------------------------\n",
545 |       "Top 10 features used to predict: \n",
546 |       "Class 1 best: \n",
547 |       "(-0.5317457349252519, 'planet')\n",
548 |       "(-0.35387821917377232, 'space')\n",
549 |       "(-0.21950314690393238, 'mar')\n",
550 |       "(-0.21950314690393238, 'red')\n",
551 |       "(-0.15678762293683152, 'earth')\n",
552 |       "(-0.15678762293683152, 'launch')\n",
553 |       "(-0.15678762293683152, 'rocket')\n",
554 |       "(-0.14909864994270897, 'great')\n",
555 |       "(-0.14909864994270897, 'love')\n",
556 |       "(-0.099774045796133873, 'blue')\n",
557 |       "Class 2 best: \n",
558 |       "(0.40866490475230544, 'twitter')\n",
559 |       "(0.35268312478964314, '@mention')\n",
560 |       "(0.22672529221454771, 'lol')\n",
561 |       "(0.22672529221454771, 'gr8')\n",
562 |       "(0.20433286130934575, 'social')\n",
563 |       "(0.20433286130934575, 'medium')\n",
564 |       "(0.20433204344295969, 'reddit')\n",
565 |       "(0.20433204344295969, 'fun')\n",
566 |       "(0.12595783257509538, 'window')\n",
567 |       "(0.12595783257509538, 'u')\n",
568 |       "----------------------------------------------------------------------------------------------\n",
569 |       "The original data as it appeared to the classifier after tokenizing, lemmatizing, stoplisting, etc\n",
570 |       "Sample 0: ('great', 1)('space', 2)('love', 1)\n",
571 |       "Sample 1: ('exist', 1)('glad', 1)('cool', 1)('planet', 1)('space', 1)\n",
572 |       "Sample 2: ('gr8', 1)('@mention', 1)('lol', 1)\n",
573 |       "Sample 3: ('fun', 1)('reddit', 1)('twitter', 1)\n",
574 |       "Sample 4: ('red', 1)('mar', 1)('planet', 1)\n",
575 |       "Sample 5: ('9', 1)('window', 1)('skip', 1)('u', 1)('@mention', 1)\n",
576 |       "Sample 6: ('earth', 1)('launch', 1)('rocket', 1)('planet', 1)\n",
577 |       "Sample 7: ('medium', 1)('social', 1)('twitter', 1)\n",
578 |       "Sample 8: ('hashtag', 1)('@mention', 3)\n",
579 |       "Sample 9: ('green', 1)('blue', 1)('little', 1)('sun', 1)('orbit', 1)('planet', 1)\n"
580 |      ]
581 |     }
582 |    ],
583 |    "source": [
584 |     "from sklearn.feature_extraction.text import CountVectorizer\n",
585 |     "from sklearn.base import TransformerMixin\n",
586 |     "from sklearn.pipeline import Pipeline\n",
587 |     "from sklearn.svm import LinearSVC\n",
588 |     "from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS\n",
589 |     "from sklearn.metrics import accuracy_score\n",
590 |     "from nltk.corpus import stopwords\n",
591 |     "import string\n",
592 |     "import re\n",
593 |     "\n",
594 |     "STOPLIST = set(stopwords.words('english') + [\"n't\", \"'s\", \"'m\", \"ca\"] + list(ENGLISH_STOP_WORDS))\n",
595 |     "SYMBOLS = \" \".join(string.punctuation).split(\" \") + [\"-----\", \"---\", \"...\", \"“\", \"”\", \"'ve\"]\n",
596 |     "\n",
597 |     "class CleanTextTransformer(TransformerMixin):\n",
598 |     "  \n",
599 |     "\n",
600 |     "    def transform(self, X, **transform_params):\n",
601 |     "        return [cleanText(text) for text in X]\n",
602 |     "\n",
603 |     "    def fit(self, X, y=None, **fit_params):\n",
604 |     "        return self\n",
605 |     "\n",
606 |     "    def get_params(self, deep=True):\n",
607 |     "        return {}\n",
608 |     "    \n",
609 |     "def cleanText(text):\n",
610 |     "    text = text.strip().replace(\"\\n\", \" \").replace(\"\\r\", \" \")\n",
611 |     "    \n",
612 |     "    mentionFinder = re.compile(r\"@[a-z0-9_]{1,15}\", re.IGNORECASE)\n",
613 |     "    text = mentionFinder.sub(\"@MENTION\", text)\n",
614 |     "    \n",
615 |     "    text = text.replace(\"&amp;\", \"and\").replace(\"&gt;\", \">\").replace(\"&lt;\", \"<\")\n",
616 |     "    \n",
617 |     "    # lowercase\n",
618 |     "    text = text.lower()\n",
619 |     "\n",
620 |     "    return text\n",
621 |     "\n",
622 |     "def tokenizeText(sample):\n",
623 |     "\n",
624 |     "    tokens = parser(sample)\n",
625 |     "\n",
626 |     "    lemmas = []\n",
627 |     "    for tok in tokens:\n",
628 |     "        lemmas.append(tok.lemma_.lower().strip() if tok.lemma_ != \"-PRON-\" else tok.lower_)\n",
629 |     "    tokens = lemmas\n",
630 |     "\n",
631 |     "    tokens = [tok for tok in tokens if tok not in STOPLIST]\n",
632 |     "\n",
633 |     "    tokens = [tok for tok in tokens if tok not in SYMBOLS]\n",
634 |     "\n",
635 |     "    while \"\" in tokens:\n",
636 |     "        tokens.remove(\"\")\n",
637 |     "    while \" \" in tokens:\n",
638 |     "        tokens.remove(\" \")\n",
639 |     "    while \"\\n\" in tokens:\n",
640 |     "        tokens.remove(\"\\n\")\n",
641 |     "    while \"\\n\\n\" in tokens:\n",
642 |     "        tokens.remove(\"\\n\\n\")\n",
643 |     "\n",
644 |     "    return tokens\n",
645 |     "\n",
646 |     "# def printNMostInformative(vectorizer, clf, N):\n",
647 |     "#     feature_names = vectorizer.get_feature_names()\n",
648 |     "#     coefs_with_fns = sorted(zip(clf.coef_[0], feature_names))\n",
649 |     "#     topClass1 = coefs_with_fns[:N]\n",
650 |     "#     topClass2 = coefs_with_fns[:-(N + 1):-1]\n",
651 |     "#     print(\"Class 1 best: \")\n",
652 |     "#     for feat in topClass1:\n",
653 |     "#         print(feat)\n",
654 |     "#     print(\"Class 2 best: \")\n",
655 |     "#     for feat in topClass2:\n",
656 |     "#         print(feat)\n",
657 |     "\n",
658 |     "vectorizer = CountVectorizer(tokenizer=tokenizeText, ngram_range=(1,1))\n",
659 |     "clf = LinearSVC()\n",
660 |     "model = Pipeline([('cleanText', CleanTextTransformer()), ('vectorizer', vectorizer), ('clf', clf)])\n",
661 |     "\n",
662 |     "train = [\"I love space. Space is great.\", \"Planets are cool. I am glad they exist in space\", \"lol @twitterdude that is gr8\", \n",
663 |     "        \"twitter &amp; reddit are fun.\", \"Mars is a planet. It is red.\", \"@Microsoft: y u skip windows 9?\", \"Rockets launch from Earth and go to other planets.\",\n",
664 |     "        \"twitter social media &gt; &lt;\", \"@someguy @somegirl @twitter #hashtag\", \"Orbiting the sun is a little blue-green planet.\"]\n",
665 |     "labelsTrain = [\"space\", \"space\", \"twitter\", \"twitter\", \"space\", \"twitter\", \"space\", \"twitter\", \"twitter\", \"space\"]\n",
666 |     "\n",
667 |     "test = [\"i h8 riting comprehensibly #skoolsux\", \"planets and stars and rockets and stuff\"]\n",
668 |     "labelsTest = [\"twitter\", \"space\"]\n",
669 |     "\n",
670 |     "model.fit(train, labelsTrain)\n",
671 |     "\n",
672 |     "preds = model.predict(test)\n",
673 |     "print(\"----------------------------------------------------------------------------------------------\")\n",
674 |     "print(\"results:\")\n",
675 |     "for (sample, pred) in zip(test, preds):\n",
676 |     "    print(sample, \":\", pred)\n",
677 |     "print(\"accuracy:\", accuracy_score(labelsTest, preds))\n",
678 |     "\n",
679 |     "print(\"----------------------------------------------------------------------------------------------\")\n",
680 |     "print(\"Top 10 features used to predict: \")\n",
681 |     "# show the top features\n",
682 |     "printNMostInformative(vectorizer, clf, 10)\n",
683 |     "\n",
684 |     "print(\"----------------------------------------------------------------------------------------------\")\n",
685 |     "print(\"The original data as it appeared to the classifier after tokenizing, lemmatizing, stoplisting, etc\")\n",
686 |     "# let's see what the pipeline was transforming the data into\n",
687 |     "pipe = Pipeline([('cleanText', CleanTextTransformer()), ('vectorizer', vectorizer)])\n",
688 |     "transform = pipe.fit_transform(train, labelsTrain)\n",
689 |     "\n",
690 |     "# get the features that the vectorizer learned (its vocabulary)\n",
691 |     "vocab = vectorizer.get_feature_names()\n",
692 |     "\n",
693 |     "# the values from the vectorizer transformed data (each item is a row,column index with value as # times occuring in the sample, stored as a sparse matrix)\n",
694 |     "for i in range(len(train)):\n",
695 |     "    s = \"\"\n",
696 |     "    indexIntoVocab = transform.indices[transform.indptr[i]:transform.indptr[i+1]]\n",
697 |     "    numOccurences = transform.data[transform.indptr[i]:transform.indptr[i+1]]\n",
698 |     "    for idx, num in zip(indexIntoVocab, numOccurences):\n",
699 |     "        s += str((vocab[idx], num))\n",
700 |     "    print(\"Sample {}: {}\".format(i, s))"
701 |    ]
702 |   },
703 |   {
704 |    "cell_type": "code",
705 |    "execution_count": null,
706 |    "metadata": {
707 |     "collapsed": false,
708 |     "deletable": true,
709 |     "editable": true
710 |    },
711 |    "outputs": [],
712 |    "source": []
713 |   }
714 |  ],
715 |  "metadata": {
716 |   "kernelspec": {
717 |    "display_name": "IPython (Python 3)",
718 |    "language": "python",
719 |    "name": "python3"
720 |   },
721 |   "language_info": {
722 |    "codemirror_mode": {
723 |     "name": "ipython",
724 |     "version": 3
725 |    },
726 |    "file_extension": ".py",
727 |    "mimetype": "text/x-python",
728 |    "name": "python",
729 |    "nbconvert_exporter": "python",
730 |    "pygments_lexer": "ipython3",
731 |    "version": "3.5.3"
732 |   }
733 |  },
734 |  "nbformat": 4,
735 |  "nbformat_minor": 0
736 | }
737 | 


--------------------------------------------------------------------------------
/spacy_nlp/parsing.py:
--------------------------------------------------------------------------------
 1 | import spacy
 2 | import requests
 3 | url=requests.get('https://s3-ap-south-1.amazonaws.com/av-blog-media/wp-content/uploads/2017/04/04080929/Tripadvisor_hotelreviews_Shivambansal.txt').content
 4 | # print url
 5 | nlp=spacy.load('en')
 6 | document = url.decode('utf8')
 7 | document = nlp(document)
 8 | #print document.ents ## freaking awesome!! can even detect thanksgiving as date!!
 9 | labels = set([w.label_ for w in document.ents])
10 | def cleanup(token, lower = True):
11 |     if lower:
12 |        token = token.lower()
13 |     return token.strip()
14 | for label in labels:
15 |     entities = [cleanup(e.string, lower=False) for e in document.ents if label==e.label_]
16 |     entities = list(set(entities))
17 |     # print label,entities
18 | 
19 | 
20 | ###### Depenedancy Parsing!! ##########
21 | hotel = [sent for sent in document.sents if 'hotel' in sent.string.lower()]
22 | sentence = hotel[2]
23 | print sentence
24 | 
25 | sentence = hotel[2]
26 | for word in sentence:
27 |     # tree
28 |     print word, ': ', str(list(word.children))
29 | 
30 | 
31 | # Generate Noun Phrases ######
32 | 
33 | doc = nlp(u'Well, I am kind of falling love in spacy!!')
34 | for no in doc.noun_chunks:
35 |     print no
36 | for np in doc.noun_chunks:
37 |     print np.root.dep_
38 |     print np.root.head.text
39 | 
40 | 
41 | 
42 | 
43 | 
44 | def iter_products(docs):
45 |     for doc in docs:
46 |         for ent in doc.ents:
47 |             if ent.label_ == 'PRODUCT':
48 |                 yield ent ## Trying out generator
49 | 
50 | def word_is_in_entity(word):
51 |     return word.ent_type != 0
52 | 
53 | def count_parent_verb_by_person(docs):
54 |     counts = defaultdict(defaultdict(int))
55 |     for doc in docs:
56 |         for ent in doc.ents:
57 |             if ent.label_ == 'PERSON' and ent.root.head.pos == VERB:
58 |                 counts[ent.orth_][ent.root.head.lemma_] += 1
59 |     return counts
60 | 


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/spacy_nlp/pos.py:
--------------------------------------------------------------------------------
 1 | import spacy
 2 | import requests
 3 | url=requests.get('https://s3-ap-south-1.amazonaws.com/av-blog-media/wp-content/uploads/2017/04/04080929/Tripadvisor_hotelreviews_Shivambansal.txt').content
 4 | nlp=spacy.load('en')
 5 | document = url.decode('utf8')
 6 | document = nlp(document)
 7 | 
 8 | 
 9 | for word in list(document.sents)[0]:
10 | 
11 |     print word, word.tag_
12 | noisy_pos_tags = ["PROP"]
13 | min_token_length = 2
14 | 
15 | def isNoise(token):
16 |     is_noise = False
17 |     if token.pos_ in noisy_pos_tags:
18 |         is_noise = True
19 |     elif token.is_stop == True:
20 |         is_noise = True
21 |     elif len(token.string) <= min_token_length:
22 |         is_noise = True
23 |     return is_noise
24 | def cleanup(token, lower = True):
25 |     if lower:
26 |        token = token.lower()
27 |     return token.strip()
28 | from collections import Counter
29 | cleaned_list = [cleanup(word.string) for word in document if not isNoise(word)]
30 | print Counter(cleaned_list) .most_common(5)
31 | 


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/spacy_nlp/readme.md:
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 1 | ## Spacy
 2 | 
 3 | Industrial-Strength
 4 | Natural Language
 5 | Processing
 6 | in Python
 7 | 
 8 | 
 9 | Fastest in the world --  It's written from the ground up in carefully memory-managed Cython. 
10 | 
11 | Deep learning -- spaCy is the best way to prepare text for deep learning 
12 | 
13 | 
14 | ## Some awesome features
15 | 
16 | 
17 | 
18 | Non-destructive tokenization
19 | 
20 | Syntax-driven sentence segmentation
21 | 
22 | Pre-trained word vectors
23 | 
24 | Part-of-speech tagging
25 | 
26 | Named entity recognition
27 | 
28 | Labelled dependency parsing
29 | 
30 | Convenient string-to-int mapping
31 | 
32 | Export to numpy data arrays
33 | 
34 | GIL-free multi-threading
35 | 
36 | Efficient binary serialization
37 | 
38 | Easy deep learning integration
39 | 
40 | Statistical models for English and German
41 | 
42 | State-of-the-art speed
43 | 
44 | Robust, rigorously evaluated accuracy
45 | 
46 | 
47 | 
48 | 
49 | 


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/spacy_nlp/sentiment.py:
--------------------------------------------------------------------------------
 1 | # -*- coding: UTF-8 -*-
 2 | 
 3 | 
 4 | from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS as stopwords
 5 | from sklearn.feature_extraction.text import CountVectorizer
 6 | from sklearn.metrics import accuracy_score
 7 | from sklearn.base import TransformerMixin
 8 | from sklearn.pipeline import Pipeline
 9 | from sklearn.svm import LinearSVC
10 | 
11 | import string
12 | punctuations = string.punctuation
13 | 
14 | from spacy.en import English
15 | parser = English()
16 | 
17 | class predictors(TransformerMixin):
18 |     def transform(self, X, **transform_params): #args
19 |         return [clean_text(text) for text in X]
20 |     def fit(self, X, y=None, **fit_params):
21 |         return self
22 |     def get_params(self, deep=True):
23 |         return {}
24 | 
25 | def clean_text(text):
26 |     return text.strip().lower()
27 | def spacy_tokenizer(sentence):
28 |     tokens = parser(sentence)
29 |     tokens = [tok.lemma_.lower().strip() if tok.lemma_ != "-PRON-" else tok.lower_ for tok in tokens]
30 |     tokens = [tok for tok in tokens if (tok not in stopwords and tok not in punctuations)]
31 |     return tokens
32 | 
33 | vectorizer = CountVectorizer(tokenizer = spacy_tokenizer, ngram_range=(1,1))
34 | classifier = LinearSVC()
35 | pipe = Pipeline([("cleaner", predictors()),
36 |                  ('vectorizer', vectorizer),
37 |                  ('classifier', classifier)])
38 | 
39 | train = [('I love this sandwich.', 'pos'),
40 |          ('this is an amazing place!', 'pos'),
41 |          ('I feel very good about these beers.', 'pos'),
42 |          ('this is my best work.', 'pos'),
43 |          ("what an awesome view", 'pos'),
44 |          ('I do not like this restaurant', 'neg'),
45 |          ('I am tired of this stuff.', 'neg'),
46 |          ("I can't deal with this", 'neg'),
47 |          ('he is my sworn enemy!', 'neg'),
48 |          ('my boss is horrible.', 'neg')]
49 | test =   [('the beer was good.', 'pos'),
50 |          ('I do not enjoy my job', 'neg'),
51 |          ("I ain't feelin dandy today.", 'neg'),
52 |          ("I feel amazing!", 'pos'),
53 |          ('Gary is a good friend of mine.', 'pos'),
54 |          ("I can't believe I'm doing this.", 'neg')]
55 | 
56 | pipe.fit([x[0] for x in train], [x[1] for x in train])
57 | pred_data = pipe.predict([x[0] for x in test])
58 | for (sample, pred) in zip(test, pred_data):
59 |     print sample, pred
60 | print "Accuracy:", accuracy_score([x[1] for x in test], pred_data)
61 | 


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/spacy_nlp/text2.txt:
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 1 | Chief Justice Roberts, President Carter, President Clinton, President Bush, President Obama, fellow Americans, and people of the world: Thank you.
 2 | We, the citizens of America, are now joined in a great national effort to rebuild our country and to restore its promise for all of our people.
 3 | Together, we will determine the course of America and the world for years to come.
 4 | We will face challenges. We will confront hardships. But we will get the job done.
 5 | Every four years, we gather on these steps to carry out the orderly and peaceful transfer of power, and we are grateful to President Obama and First Lady Michelle Obama for their gracious aid throughout this transition. They have been magnificent.
 6 | Today's ceremony, however, has very special meaning. Because today we are not merely transferring power from one administration to another, or from one party to another -- but we are transferring power from Washington, D.C. and giving it back to you, the American People.
 7 | Words from the past: Inauguration speech library
 8 | For too long, a small group in our nation's Capital has reaped the rewards of government while the people have borne the cost. Washington flourished -- but the people did not share in its wealth. Politicians prospered -- but the jobs left, and the factories closed.
 9 | The establishment protected itself, but not the citizens of our country. Their victories have not been your victories; their triumphs have not been your triumphs; and while they celebrated in our nation's capital, there was little to celebrate for struggling families all across our land.
10 | That all changes -- starting right here, and right now, because this moment is your moment: it belongs to you.
11 | It belongs to everyone gathered here today and everyone watching all across America. This is your day. This is your celebration. And this, the United States of America, is your country.
12 | What truly matters is not which party controls our government, but whether our government is controlled by the people. January 20th 2017, will be remembered as the day the people became the rulers of this nation again. The forgotten men and women of our country will be forgotten no longer.
13 | Everyone is listening to you now.
14 | You came by the tens of millions to become part of a historic movement the likes of which the world has never seen before. At the center of this movement is a crucial conviction: that a nation exists to serve its citizens.
15 | Americans want great schools for their children, safe neighborhoods for their families, and good jobs for themselves. These are the just and reasonable demands of a righteous public.
16 | But for too many of our citizens, a different reality exists: Mothers and children trapped in poverty in our inner cities; rusted-out factories scattered like tombstones across the landscape of our nation; an education system flush with cash, but which leaves our young and beautiful students deprived of knowledge; and the crime and gangs and drugs that have stolen too many lives and robbed our country of so much unrealized potential.
17 | This American carnage stops right here and stops right now.
18 | We are one nation -- and their pain is our pain. Their dreams are our dreams; and their success will be our success. We share one heart, one home, and one glorious destiny.
19 | The oath of office I take today is an oath of allegiance to all Americans.
20 | For many decades, we've enriched foreign industry at the expense of American industry; subsidized the armies of other countries while allowing for the very sad depletion of our military; we've defended other nation's borders while refusing to defend our own; and spent trillions of dollars overseas while America's infrastructure has fallen into disrepair and decay.
21 | We've made other countries rich while the wealth, strength, and confidence of our country has disappeared over the horizon.
22 | One by one, the factories shuttered and left our shores, with not even a thought about the millions upon millions of American workers left behind.
23 | The wealth of our middle class has been ripped from their homes and then redistributed across the entire world.
24 | But that is the past. And now we are looking only to the future. We assembled here today are issuing a new decree to be heard in every city, in every foreign capital, and in every hall of power.
25 | From this day forward, a new vision will govern our land.
26 | From this moment on, it's going to be America First.
27 | Every decision on trade, on taxes, on immigration, on foreign affairs, will be made to benefit American workers and American families. We must protect our borders from the ravages of other countries making our products, stealing our companies, and destroying our jobs. Protection will lead to great prosperity and strength.
28 | I will fight for you with every breath in my body -- and I will never, ever let you down.
29 | America will start winning again, winning like never before.
30 | We will bring back our jobs. We will bring back our borders. We will bring back our wealth. And we will bring back our dreams.
31 | We will build new roads, and highways, and bridges, and airports, and tunnels, and railways all across our wonderful nation.
32 | We will get our people off of welfare and back to work -- rebuilding our country with American hands and American labor.
33 | We will follow two simple rules: Buy American and hire American.
34 | We will seek friendship and goodwill with the nations of the world -- but we do so with the understanding that it is the right of all nations to put their own interests first.
35 | We do not seek to impose our way of life on anyone, but rather to let it shine as an example for everyone to follow.
36 | We will reinforce old alliances and form new ones -- and unite the civilized world against radical Islamic terrorism, which we will eradicate completely from the face of the Earth.
37 | At the bedrock of our politics will be a total allegiance to the United States of America, and through our loyalty to our country, we will rediscover our loyalty to each other.
38 | When you open your heart to patriotism, there is no room for prejudice. The Bible tells us, "How good and pleasant it is when God's people live together in unity."
39 | We must speak our minds openly, debate our disagreements honestly, but always pursue solidarity.
40 | When America is united, America is totally unstoppable.
41 | There should be no fear -- we are protected, and we will always be protected.
42 | We will be protected by the great men and women of our military and law enforcement and, most importantly, we are protected by God.
43 | Finally, we must think big and dream even bigger.
44 | In America, we understand that a nation is only living as long as it is striving.
45 | We will no longer accept politicians who are all talk and no action -- constantly complaining but never doing anything about it.
46 | The time for empty talk is over. Now arrives the hour of action.
47 | Do not let anyone tell you it cannot be done. No challenge can match the heart and fight and spirit of America.
48 | We will not fail. Our country will thrive and prosper again.
49 | We stand at the birth of a new millennium, ready to unlock the mysteries of space, to free the Earth from the miseries of disease, and to harness the energies, industries and technologies of tomorrow.
50 | A new national pride will stir our souls, lift our sights, and heal our divisions.
51 | It is time to remember that old wisdom our soldiers will never forget: that whether we are black or brown or white, we all bleed the same red blood of patriots, we all enjoy the same glorious freedoms, and we all salute the same great American Flag.
52 | And whether a child is born in the urban sprawl of Detroit or the windswept plains of Nebraska, they look up at the same night sky, they fill their heart with the same dreams, and they are infused with the breath of life by the same almighty Creator.
53 | So to all Americans, in every city near and far, small and large, from mountain to mountain, and from ocean to ocean, hear these words:
54 | You will never be ignored again.
55 | Your voice, your hopes, and your dreams will define our American destiny. And your courage and goodness and love will forever guide us along the way.
56 | Together, We will make America strong again.
57 | We will make wealthy again.
58 | We will make America proud again.
59 | We will make America safe again.
60 | And yes, together, we will make America great again. Thank you. God bless you. And God bless America.
61 | 


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/spacy_nlp/text_generate.py:
--------------------------------------------------------------------------------
 1 | import spacy
 2 | import random
 3 | 
 4 | nlp = spacy.load('en')
 5 | 
 6 | trump_text = open('text1.txt', 'r').read()
 7 | replacement_text = open('text2.txt').read()
 8 | 
 9 | trump_text_nlp = nlp(unicode(trump_text))
10 | replacement_nlp = nlp(unicode(replacement_text))
11 | 
12 | 
13 | replacement_vb = []
14 | replacement_vbd = []
15 | replacement_vbg = []
16 | replacement_vbn = []
17 | replacement_vbp = []
18 | replacement_vbz = []
19 | #nouns
20 | replacement_nn = []
21 | replacement_nnp = []
22 | replacement_nnps = []
23 | replacement_nns = []
24 | #adjectives
25 | replacement_adj = []
26 | replacement_det = []
27 | replacement_intj = []
28 | 
29 | for replacement_word in replacement_nlp:
30 | 	if replacement_word.tag_ == "VB": ##VERB
31 | 		replacement_vb.append(replacement_word.text)
32 | 	elif replacement_word.tag_ == "VBD":
33 | 		replacement_vbd.append(replacement_word.text)
34 | 	elif replacement_word.tag_ == "VBG":
35 | 		replacement_vbg.append(replacement_word.text)
36 | 	elif replacement_word.tag_ == "VBN":
37 | 		replacement_vbn.append(replacement_word.text)
38 | 	elif replacement_word.tag_ == "NN":
39 | 	 	replacement_nn.append(replacement_word.text)
40 | 	elif replacement_word.tag_ == "NNS":
41 | 	 	replacement_nns.append(replacement_word.text)
42 | 	elif replacement_word.pos_ == "ADJ":  ##adjective
43 | 	 	replacement_adj.append(replacement_word.text)
44 | 	elif replacement_word.pos_ == "DET":  ##determiner
45 | 	 	replacement_det.append(replacement_word.text)
46 | 	elif replacement_word.pos_ == "INTJ":   ## maybe interjection
47 | 		replacement_intj.append(replacement_word.text)
48 | 	pass
49 | 
50 | trump_new_speech = "" ##SORRy
51 | 
52 | for word in trump_text_nlp:
53 | 	if word.tag_ == "VB":
54 | 		trump_new_speech += (random.choice(replacement_vb) + " ")
55 | 	elif word.tag_ == "VBD":
56 | 		trump_new_speech += (random.choice(replacement_vbd) + " ")
57 | 	elif word.tag_ == "VBG":
58 | 		trump_new_speech += (random.choice(replacement_vbg) + " ")
59 | 	elif word.tag_ == "VBN":
60 | 		trump_new_speech += (random.choice(replacement_vbn) + " ")
61 | 	elif word.tag_ == "NN":
62 | 		trump_new_speech += (random.choice(replacement_nn) + " ")
63 | 	elif word.tag_ == "NNS":
64 | 		trump_new_speech += (random.choice(replacement_nns) + " ")
65 | 	elif word.pos_ == "ADJ":
66 | 		trump_new_speech += (random.choice(replacement_adj) + " ")
67 | 	elif word.pos_ == "DET":
68 | 		trump_new_speech += (random.choice(replacement_det) + " ")
69 | 	elif word.pos_ == "INTJ":
70 | 		trump_new_speech += (random.choice(replacement_intj) + " ")
71 | 	else:
72 | 		trump_new_speech += (word.text + " ")
73 | 
74 | trump_new_speech = trump_new_speech.replace(" , ", ", ")
75 | trump_new_speech = trump_new_speech.replace(" . ", ". ")
76 | trump_new_speech = trump_new_speech.replace(" ; ", "; ")
77 | trump_new_speech = trump_new_speech.replace(" : ", ": ")
78 | print trump_new_speech
79 | 
80 | replacement_text_saved = open("Generated.txt", "w")
81 | replacement_text_saved.write(trump_new_speech)
82 | replacement_text_saved.close()
83 | 


--------------------------------------------------------------------------------
/spacy_nlp/tokens.py:
--------------------------------------------------------------------------------
 1 | import spacy
 2 | import requests
 3 | url=requests.get('https://s3-ap-south-1.amazonaws.com/av-blog-media/wp-content/uploads/2017/04/04080929/Tripadvisor_hotelreviews_Shivambansal.txt').content
 4 | # print url
 5 | nlp=spacy.load('en')
 6 | document = url.decode('utf8')
 7 | document = nlp(document)
 8 | # print dir(document)
 9 | #print document[0]
10 | print list(document.sents)[0]
11 | 
12 | for word in list(document.sents)[0]:
13 |     print word, word.tag_
14 | 


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/spacy_nlp/word2vec_model.py:
--------------------------------------------------------------------------------
 1 | import numpy
 2 | from numpy.linalg import norm
 3 | from spacy.gold import GoldParse
 4 | from spacy.language import EntityRecognizer
 5 | from spacy.en import English
 6 | parser = English()
 7 | 
 8 | apple = parser.vocab[u'apple']
 9 | def cosine(v1,v2):
10 |     cos=numpy.dot(v1,v2)/(norm(v1)*norm(v2))
11 |     return cos
12 | others = list({w for w in parser.vocab if w.has_vector and w.orth_.islower() and w.lower_ != unicode("apple")})
13 | # def vector_fun():
14 | #     answer=cosine(w.vector,apple.vector)
15 | #     return answer
16 | others.sort(key=lambda w: cosine(w.vector, apple.vector))
17 | 
18 | others.reverse()
19 | 
20 | 
21 | print "top most similar words to apple:"
22 | for word in others[:10]:
23 |     print word.orth_
24 | 
25 | king = parser.vocab[u'king']
26 | man = parser.vocab[u'man']
27 | woman = parser.vocab[u'woman']
28 | 
29 | result = king.vector - man.vector + woman.vector
30 | 
31 | allWords = list({w for w in parser.vocab if w.has_vector and w.orth_.islower() and w.lower_ != "king" and w.lower_ != "man" and w.lower_ != "woman"})
32 | allWords.sort(key=lambda w: cosine(w.vector, result))
33 | allWords.reverse()
34 | print("\n----------------------------\nTop 3 closest results for king - man + woman:")
35 | for word in allWords[:3]:
36 |     print(word.orth_)
37 | 


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