├── Get Model.ipynb
├── Model Building.ipynb
├── README.md
├── __init__.py
├── __pycache__
    ├── __init__.cpython-310.pyc
    └── get_tweets.cpython-310.pyc
├── dataset
    └── twitter_sentiments.csv
├── get_sentiment.py
├── get_tweets.py
├── templates
    └── home.html
└── text_classification.joblib


/Get Model.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {},
 7 |    "outputs": [],
 8 |    "source": [
 9 |     "import pandas as pd\n",
10 |     "from joblib import load"
11 |    ]
12 |   },
13 |   {
14 |    "cell_type": "code",
15 |    "execution_count": 5,
16 |    "metadata": {},
17 |    "outputs": [],
18 |    "source": [
19 |     "text = [\"Virat Kohli, AB de Villiers set to auction their 'Green Day' kits from 2016 IPL match to raise funds\"]"
20 |    ]
21 |   },
22 |   {
23 |    "cell_type": "code",
24 |    "execution_count": 6,
25 |    "metadata": {},
26 |    "outputs": [],
27 |    "source": [
28 |     "pipeline = load(\"text_classification.joblib\")"
29 |    ]
30 |   },
31 |   {
32 |    "cell_type": "code",
33 |    "execution_count": 7,
34 |    "metadata": {},
35 |    "outputs": [
36 |     {
37 |      "data": {
38 |       "text/plain": [
39 |        "array([0])"
40 |       ]
41 |      },
42 |      "execution_count": 7,
43 |      "metadata": {},
44 |      "output_type": "execute_result"
45 |     }
46 |    ],
47 |    "source": [
48 |     "pipeline.predict(text)"
49 |    ]
50 |   },
51 |   {
52 |    "cell_type": "code",
53 |    "execution_count": null,
54 |    "metadata": {},
55 |    "outputs": [],
56 |    "source": []
57 |   }
58 |  ],
59 |  "metadata": {
60 |   "kernelspec": {
61 |    "display_name": "Python 3",
62 |    "language": "python",
63 |    "name": "python3"
64 |   },
65 |   "language_info": {
66 |    "codemirror_mode": {
67 |     "name": "ipython",
68 |     "version": 3
69 |    },
70 |    "file_extension": ".py",
71 |    "mimetype": "text/x-python",
72 |    "name": "python",
73 |    "nbconvert_exporter": "python",
74 |    "pygments_lexer": "ipython3",
75 |    "version": "3.7.3"
76 |   }
77 |  },
78 |  "nbformat": 4,
79 |  "nbformat_minor": 2
80 | }
81 | 


--------------------------------------------------------------------------------
/Model Building.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 35,
  6 |    "metadata": {},
  7 |    "outputs": [],
  8 |    "source": [
  9 |     "# importing required libraries\n",
 10 |     "import pandas as pd\n",
 11 |     "from sklearn.feature_extraction.text import ENGLISH_STOP_WORDS, TfidfVectorizer\n",
 12 |     "from sklearn.linear_model import LogisticRegression\n",
 13 |     "from sklearn.pipeline import Pipeline\n",
 14 |     "from sklearn.metrics import f1_score\n",
 15 |     "from sklearn.model_selection import train_test_split"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "code",
 20 |    "execution_count": 24,
 21 |    "metadata": {},
 22 |    "outputs": [],
 23 |    "source": [
 24 |     "data = pd.read_csv('dataset/twitter_sentiments.csv')"
 25 |    ]
 26 |   },
 27 |   {
 28 |    "cell_type": "code",
 29 |    "execution_count": 95,
 30 |    "metadata": {},
 31 |    "outputs": [
 32 |     {
 33 |      "data": {
 34 |       "text/html": [
 35 |        "<div>\n",
 36 |        "<style scoped>\n",
 37 |        "    .dataframe tbody tr th:only-of-type {\n",
 38 |        "        vertical-align: middle;\n",
 39 |        "    }\n",
 40 |        "\n",
 41 |        "    .dataframe tbody tr th {\n",
 42 |        "        vertical-align: top;\n",
 43 |        "    }\n",
 44 |        "\n",
 45 |        "    .dataframe thead th {\n",
 46 |        "        text-align: right;\n",
 47 |        "    }\n",
 48 |        "</style>\n",
 49 |        "<table border=\"1\" class=\"dataframe\">\n",
 50 |        "  <thead>\n",
 51 |        "    <tr style=\"text-align: right;\">\n",
 52 |        "      <th></th>\n",
 53 |        "      <th>id</th>\n",
 54 |        "      <th>label</th>\n",
 55 |        "      <th>tweet</th>\n",
 56 |        "    </tr>\n",
 57 |        "  </thead>\n",
 58 |        "  <tbody>\n",
 59 |        "    <tr>\n",
 60 |        "      <th>0</th>\n",
 61 |        "      <td>1</td>\n",
 62 |        "      <td>0</td>\n",
 63 |        "      <td>@user when a father is dysfunctional and is s...</td>\n",
 64 |        "    </tr>\n",
 65 |        "    <tr>\n",
 66 |        "      <th>1</th>\n",
 67 |        "      <td>2</td>\n",
 68 |        "      <td>0</td>\n",
 69 |        "      <td>@user @user thanks for #lyft credit i can't us...</td>\n",
 70 |        "    </tr>\n",
 71 |        "    <tr>\n",
 72 |        "      <th>2</th>\n",
 73 |        "      <td>3</td>\n",
 74 |        "      <td>0</td>\n",
 75 |        "      <td>bihday your majesty</td>\n",
 76 |        "    </tr>\n",
 77 |        "    <tr>\n",
 78 |        "      <th>3</th>\n",
 79 |        "      <td>4</td>\n",
 80 |        "      <td>0</td>\n",
 81 |        "      <td>#model   i love u take with u all the time in ...</td>\n",
 82 |        "    </tr>\n",
 83 |        "    <tr>\n",
 84 |        "      <th>4</th>\n",
 85 |        "      <td>5</td>\n",
 86 |        "      <td>0</td>\n",
 87 |        "      <td>factsguide: society now    #motivation</td>\n",
 88 |        "    </tr>\n",
 89 |        "  </tbody>\n",
 90 |        "</table>\n",
 91 |        "</div>"
 92 |       ],
 93 |       "text/plain": [
 94 |        "   id  label                                              tweet\n",
 95 |        "0   1      0   @user when a father is dysfunctional and is s...\n",
 96 |        "1   2      0  @user @user thanks for #lyft credit i can't us...\n",
 97 |        "2   3      0                                bihday your majesty\n",
 98 |        "3   4      0  #model   i love u take with u all the time in ...\n",
 99 |        "4   5      0             factsguide: society now    #motivation"
100 |       ]
101 |      },
102 |      "execution_count": 95,
103 |      "metadata": {},
104 |      "output_type": "execute_result"
105 |     }
106 |    ],
107 |    "source": [
108 |     "data.head()"
109 |    ]
110 |   },
111 |   {
112 |    "cell_type": "code",
113 |    "execution_count": 96,
114 |    "metadata": {},
115 |    "outputs": [
116 |     {
117 |      "data": {
118 |       "text/plain": [
119 |        "(31962, 3)"
120 |       ]
121 |      },
122 |      "execution_count": 96,
123 |      "metadata": {},
124 |      "output_type": "execute_result"
125 |     }
126 |    ],
127 |    "source": [
128 |     "data.shape"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 26,
134 |    "metadata": {},
135 |    "outputs": [
136 |     {
137 |      "data": {
138 |       "text/plain": [
139 |        "0    29720\n",
140 |        "1     2242\n",
141 |        "Name: label, dtype: int64"
142 |       ]
143 |      },
144 |      "execution_count": 26,
145 |      "metadata": {},
146 |      "output_type": "execute_result"
147 |     }
148 |    ],
149 |    "source": [
150 |     "data.label.value_counts()"
151 |    ]
152 |   },
153 |   {
154 |    "cell_type": "code",
155 |    "execution_count": 27,
156 |    "metadata": {},
157 |    "outputs": [],
158 |    "source": [
159 |     "train, test = train_test_split(data, test_size = 0.2, stratify = data['label'], random_state=21)"
160 |    ]
161 |   },
162 |   {
163 |    "cell_type": "code",
164 |    "execution_count": 28,
165 |    "metadata": {
166 |     "scrolled": true
167 |    },
168 |    "outputs": [
169 |     {
170 |      "data": {
171 |       "text/plain": [
172 |        "((25569, 3), (6393, 3))"
173 |       ]
174 |      },
175 |      "execution_count": 28,
176 |      "metadata": {},
177 |      "output_type": "execute_result"
178 |     }
179 |    ],
180 |    "source": [
181 |     "train.shape, test.shape"
182 |    ]
183 |   },
184 |   {
185 |    "cell_type": "code",
186 |    "execution_count": 29,
187 |    "metadata": {},
188 |    "outputs": [
189 |     {
190 |      "data": {
191 |       "text/plain": [
192 |        "0    0.929837\n",
193 |        "1    0.070163\n",
194 |        "Name: label, dtype: float64"
195 |       ]
196 |      },
197 |      "execution_count": 29,
198 |      "metadata": {},
199 |      "output_type": "execute_result"
200 |     }
201 |    ],
202 |    "source": [
203 |     "train.label.value_counts(normalize=True)"
204 |    ]
205 |   },
206 |   {
207 |    "cell_type": "code",
208 |    "execution_count": 30,
209 |    "metadata": {},
210 |    "outputs": [
211 |     {
212 |      "data": {
213 |       "text/plain": [
214 |        "0    0.929923\n",
215 |        "1    0.070077\n",
216 |        "Name: label, dtype: float64"
217 |       ]
218 |      },
219 |      "execution_count": 30,
220 |      "metadata": {},
221 |      "output_type": "execute_result"
222 |     }
223 |    ],
224 |    "source": [
225 |     "test.label.value_counts(normalize=True)"
226 |    ]
227 |   },
228 |   {
229 |    "cell_type": "code",
230 |    "execution_count": 61,
231 |    "metadata": {},
232 |    "outputs": [],
233 |    "source": [
234 |     "tfidf_vectorizer = TfidfVectorizer(lowercase= True, max_features=1000, stop_words=ENGLISH_STOP_WORDS)"
235 |    ]
236 |   },
237 |   {
238 |    "cell_type": "code",
239 |    "execution_count": 62,
240 |    "metadata": {},
241 |    "outputs": [
242 |     {
243 |      "data": {
244 |       "text/plain": [
245 |        "TfidfVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
246 |        "                dtype=<class 'numpy.float64'>, encoding='utf-8',\n",
247 |        "                input='content', lowercase=True, max_df=1.0, max_features=1000,\n",
248 |        "                min_df=1, ngram_range=(1, 1), norm='l2', preprocessor=None,\n",
249 |        "                smooth_idf=True,\n",
250 |        "                stop_words=frozenset({'a', 'about', 'above', 'across', 'after',\n",
251 |        "                                      'afterwards', 'again', 'against', 'all',\n",
252 |        "                                      'almost', 'alone', 'along', 'already',\n",
253 |        "                                      'also', 'although', 'always', 'am',\n",
254 |        "                                      'among', 'amongst', 'amoungst', 'amount',\n",
255 |        "                                      'an', 'and', 'another', 'any', 'anyhow',\n",
256 |        "                                      'anyone', 'anything', 'anyway',\n",
257 |        "                                      'anywhere', ...}),\n",
258 |        "                strip_accents=None, sublinear_tf=False,\n",
259 |        "                token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', tokenizer=None, use_idf=True,\n",
260 |        "                vocabulary=None)"
261 |       ]
262 |      },
263 |      "execution_count": 62,
264 |      "metadata": {},
265 |      "output_type": "execute_result"
266 |     }
267 |    ],
268 |    "source": [
269 |     "tfidf_vectorizer.fit(train.tweet)"
270 |    ]
271 |   },
272 |   {
273 |    "cell_type": "code",
274 |    "execution_count": 63,
275 |    "metadata": {},
276 |    "outputs": [],
277 |    "source": [
278 |     "train_idf = tfidf_vectorizer.transform(train.tweet)\n",
279 |     "test_idf  = tfidf_vectorizer.transform(test.tweet)"
280 |    ]
281 |   },
282 |   {
283 |    "cell_type": "code",
284 |    "execution_count": 64,
285 |    "metadata": {},
286 |    "outputs": [],
287 |    "source": [
288 |     "model_LR = LogisticRegression()"
289 |    ]
290 |   },
291 |   {
292 |    "cell_type": "code",
293 |    "execution_count": 65,
294 |    "metadata": {},
295 |    "outputs": [
296 |     {
297 |      "data": {
298 |       "text/plain": [
299 |        "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
300 |        "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
301 |        "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
302 |        "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
303 |        "                   warm_start=False)"
304 |       ]
305 |      },
306 |      "execution_count": 65,
307 |      "metadata": {},
308 |      "output_type": "execute_result"
309 |     }
310 |    ],
311 |    "source": [
312 |     "model_LR.fit(train_idf, train.label)"
313 |    ]
314 |   },
315 |   {
316 |    "cell_type": "code",
317 |    "execution_count": 66,
318 |    "metadata": {},
319 |    "outputs": [],
320 |    "source": [
321 |     "predict_train = model_LR.predict(train_idf)"
322 |    ]
323 |   },
324 |   {
325 |    "cell_type": "code",
326 |    "execution_count": 67,
327 |    "metadata": {},
328 |    "outputs": [],
329 |    "source": [
330 |     "predict_test = model_LR.predict(test_idf)"
331 |    ]
332 |   },
333 |   {
334 |    "cell_type": "code",
335 |    "execution_count": 68,
336 |    "metadata": {},
337 |    "outputs": [
338 |     {
339 |      "data": {
340 |       "text/plain": [
341 |        "0.4888178913738019"
342 |       ]
343 |      },
344 |      "execution_count": 68,
345 |      "metadata": {},
346 |      "output_type": "execute_result"
347 |     }
348 |    ],
349 |    "source": [
350 |     "# f1 score on train data\n",
351 |     "f1_score(y_true= train.label, y_pred= predict_train)"
352 |    ]
353 |   },
354 |   {
355 |    "cell_type": "code",
356 |    "execution_count": 69,
357 |    "metadata": {},
358 |    "outputs": [
359 |     {
360 |      "data": {
361 |       "text/plain": [
362 |        "0.45751633986928114"
363 |       ]
364 |      },
365 |      "execution_count": 69,
366 |      "metadata": {},
367 |      "output_type": "execute_result"
368 |     }
369 |    ],
370 |    "source": [
371 |     "f1_score(y_true= test.label, y_pred= predict_test)"
372 |    ]
373 |   },
374 |   {
375 |    "cell_type": "code",
376 |    "execution_count": 73,
377 |    "metadata": {},
378 |    "outputs": [],
379 |    "source": [
380 |     "pipeline = Pipeline(steps= [('tfidf', TfidfVectorizer(lowercase=True,\n",
381 |     "                                                      max_features=1000,\n",
382 |     "                                                      stop_words= ENGLISH_STOP_WORDS)),\n",
383 |     "                            ('model', LogisticRegression())])"
384 |    ]
385 |   },
386 |   {
387 |    "cell_type": "code",
388 |    "execution_count": 75,
389 |    "metadata": {},
390 |    "outputs": [
391 |     {
392 |      "data": {
393 |       "text/plain": [
394 |        "Pipeline(memory=None,\n",
395 |        "         steps=[('tfidf',\n",
396 |        "                 TfidfVectorizer(analyzer='word', binary=False,\n",
397 |        "                                 decode_error='strict',\n",
398 |        "                                 dtype=<class 'numpy.float64'>,\n",
399 |        "                                 encoding='utf-8', input='content',\n",
400 |        "                                 lowercase=True, max_df=1.0, max_features=1000,\n",
401 |        "                                 min_df=1, ngram_range=(1, 1), norm='l2',\n",
402 |        "                                 preprocessor=None, smooth_idf=True,\n",
403 |        "                                 stop_words=frozenset({'a', 'about', 'above',\n",
404 |        "                                                       'across', 'after',\n",
405 |        "                                                       'afterward...\n",
406 |        "                                 strip_accents=None, sublinear_tf=False,\n",
407 |        "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
408 |        "                                 tokenizer=None, use_idf=True,\n",
409 |        "                                 vocabulary=None)),\n",
410 |        "                ('model',\n",
411 |        "                 LogisticRegression(C=1.0, class_weight=None, dual=False,\n",
412 |        "                                    fit_intercept=True, intercept_scaling=1,\n",
413 |        "                                    l1_ratio=None, max_iter=100,\n",
414 |        "                                    multi_class='auto', n_jobs=None,\n",
415 |        "                                    penalty='l2', random_state=None,\n",
416 |        "                                    solver='lbfgs', tol=0.0001, verbose=0,\n",
417 |        "                                    warm_start=False))],\n",
418 |        "         verbose=False)"
419 |       ]
420 |      },
421 |      "execution_count": 75,
422 |      "metadata": {},
423 |      "output_type": "execute_result"
424 |     }
425 |    ],
426 |    "source": [
427 |     "pipeline.fit(train.tweet, train.label)"
428 |    ]
429 |   },
430 |   {
431 |    "cell_type": "code",
432 |    "execution_count": 77,
433 |    "metadata": {},
434 |    "outputs": [
435 |     {
436 |      "data": {
437 |       "text/plain": [
438 |        "array([0, 0, 0, ..., 0, 0, 0])"
439 |       ]
440 |      },
441 |      "execution_count": 77,
442 |      "metadata": {},
443 |      "output_type": "execute_result"
444 |     }
445 |    ],
446 |    "source": [
447 |     "pipeline.predict(train.tweet)"
448 |    ]
449 |   },
450 |   {
451 |    "cell_type": "code",
452 |    "execution_count": 93,
453 |    "metadata": {},
454 |    "outputs": [],
455 |    "source": [
456 |     "text = [\"Virat Kohli, AB de Villiers set to auction their 'Green Day' kits from 2016 IPL match to raise funds\"]"
457 |    ]
458 |   },
459 |   {
460 |    "cell_type": "code",
461 |    "execution_count": 94,
462 |    "metadata": {},
463 |    "outputs": [
464 |     {
465 |      "data": {
466 |       "text/plain": [
467 |        "array([0])"
468 |       ]
469 |      },
470 |      "execution_count": 94,
471 |      "metadata": {},
472 |      "output_type": "execute_result"
473 |     }
474 |    ],
475 |    "source": [
476 |     "pipeline.predict(text)"
477 |    ]
478 |   },
479 |   {
480 |    "cell_type": "code",
481 |    "execution_count": 89,
482 |    "metadata": {},
483 |    "outputs": [],
484 |    "source": [
485 |     "from joblib import dump"
486 |    ]
487 |   },
488 |   {
489 |    "cell_type": "code",
490 |    "execution_count": 90,
491 |    "metadata": {},
492 |    "outputs": [
493 |     {
494 |      "data": {
495 |       "text/plain": [
496 |        "['text_classification.joblib']"
497 |       ]
498 |      },
499 |      "execution_count": 90,
500 |      "metadata": {},
501 |      "output_type": "execute_result"
502 |     }
503 |    ],
504 |    "source": [
505 |     "dump(pipeline, filename=\"text_classification.joblib\")"
506 |    ]
507 |   },
508 |   {
509 |    "cell_type": "code",
510 |    "execution_count": 92,
511 |    "metadata": {},
512 |    "outputs": [
513 |     {
514 |      "data": {
515 |       "text/html": [
516 |        "<div>\n",
517 |        "<style scoped>\n",
518 |        "    .dataframe tbody tr th:only-of-type {\n",
519 |        "        vertical-align: middle;\n",
520 |        "    }\n",
521 |        "\n",
522 |        "    .dataframe tbody tr th {\n",
523 |        "        vertical-align: top;\n",
524 |        "    }\n",
525 |        "\n",
526 |        "    .dataframe thead th {\n",
527 |        "        text-align: right;\n",
528 |        "    }\n",
529 |        "</style>\n",
530 |        "<table border=\"1\" class=\"dataframe\">\n",
531 |        "  <thead>\n",
532 |        "    <tr style=\"text-align: right;\">\n",
533 |        "      <th></th>\n",
534 |        "      <th>id</th>\n",
535 |        "      <th>label</th>\n",
536 |        "      <th>tweet</th>\n",
537 |        "    </tr>\n",
538 |        "  </thead>\n",
539 |        "  <tbody>\n",
540 |        "    <tr>\n",
541 |        "      <th>13</th>\n",
542 |        "      <td>14</td>\n",
543 |        "      <td>1</td>\n",
544 |        "      <td>@user #cnn calls #michigan middle school 'buil...</td>\n",
545 |        "    </tr>\n",
546 |        "    <tr>\n",
547 |        "      <th>14</th>\n",
548 |        "      <td>15</td>\n",
549 |        "      <td>1</td>\n",
550 |        "      <td>no comment!  in #australia   #opkillingbay #se...</td>\n",
551 |        "    </tr>\n",
552 |        "    <tr>\n",
553 |        "      <th>17</th>\n",
554 |        "      <td>18</td>\n",
555 |        "      <td>1</td>\n",
556 |        "      <td>retweet if you agree!</td>\n",
557 |        "    </tr>\n",
558 |        "    <tr>\n",
559 |        "      <th>23</th>\n",
560 |        "      <td>24</td>\n",
561 |        "      <td>1</td>\n",
562 |        "      <td>@user @user lumpy says i am a . prove it lumpy.</td>\n",
563 |        "    </tr>\n",
564 |        "    <tr>\n",
565 |        "      <th>34</th>\n",
566 |        "      <td>35</td>\n",
567 |        "      <td>1</td>\n",
568 |        "      <td>it's unbelievable that in the 21st century we'...</td>\n",
569 |        "    </tr>\n",
570 |        "    <tr>\n",
571 |        "      <th>...</th>\n",
572 |        "      <td>...</td>\n",
573 |        "      <td>...</td>\n",
574 |        "      <td>...</td>\n",
575 |        "    </tr>\n",
576 |        "    <tr>\n",
577 |        "      <th>31934</th>\n",
578 |        "      <td>31935</td>\n",
579 |        "      <td>1</td>\n",
580 |        "      <td>lady banned from kentucky mall. @user  #jcpenn...</td>\n",
581 |        "    </tr>\n",
582 |        "    <tr>\n",
583 |        "      <th>31946</th>\n",
584 |        "      <td>31947</td>\n",
585 |        "      <td>1</td>\n",
586 |        "      <td>@user omfg i'm offended! i'm a  mailbox and i'...</td>\n",
587 |        "    </tr>\n",
588 |        "    <tr>\n",
589 |        "      <th>31947</th>\n",
590 |        "      <td>31948</td>\n",
591 |        "      <td>1</td>\n",
592 |        "      <td>@user @user you don't have the balls to hashta...</td>\n",
593 |        "    </tr>\n",
594 |        "    <tr>\n",
595 |        "      <th>31948</th>\n",
596 |        "      <td>31949</td>\n",
597 |        "      <td>1</td>\n",
598 |        "      <td>makes you ask yourself, who am i? then am i a...</td>\n",
599 |        "    </tr>\n",
600 |        "    <tr>\n",
601 |        "      <th>31960</th>\n",
602 |        "      <td>31961</td>\n",
603 |        "      <td>1</td>\n",
604 |        "      <td>@user #sikh #temple vandalised in in #calgary,...</td>\n",
605 |        "    </tr>\n",
606 |        "  </tbody>\n",
607 |        "</table>\n",
608 |        "<p>2242 rows × 3 columns</p>\n",
609 |        "</div>"
610 |       ],
611 |       "text/plain": [
612 |        "          id  label                                              tweet\n",
613 |        "13        14      1  @user #cnn calls #michigan middle school 'buil...\n",
614 |        "14        15      1  no comment!  in #australia   #opkillingbay #se...\n",
615 |        "17        18      1                             retweet if you agree! \n",
616 |        "23        24      1    @user @user lumpy says i am a . prove it lumpy.\n",
617 |        "34        35      1  it's unbelievable that in the 21st century we'...\n",
618 |        "...      ...    ...                                                ...\n",
619 |        "31934  31935      1  lady banned from kentucky mall. @user  #jcpenn...\n",
620 |        "31946  31947      1  @user omfg i'm offended! i'm a  mailbox and i'...\n",
621 |        "31947  31948      1  @user @user you don't have the balls to hashta...\n",
622 |        "31948  31949      1   makes you ask yourself, who am i? then am i a...\n",
623 |        "31960  31961      1  @user #sikh #temple vandalised in in #calgary,...\n",
624 |        "\n",
625 |        "[2242 rows x 3 columns]"
626 |       ]
627 |      },
628 |      "execution_count": 92,
629 |      "metadata": {},
630 |      "output_type": "execute_result"
631 |     }
632 |    ],
633 |    "source": [
634 |     "data[data.label == 1]"
635 |    ]
636 |   },
637 |   {
638 |    "cell_type": "code",
639 |    "execution_count": null,
640 |    "metadata": {},
641 |    "outputs": [],
642 |    "source": []
643 |   }
644 |  ],
645 |  "metadata": {
646 |   "kernelspec": {
647 |    "display_name": "Python 3",
648 |    "language": "python",
649 |    "name": "python3"
650 |   },
651 |   "language_info": {
652 |    "codemirror_mode": {
653 |     "name": "ipython",
654 |     "version": 3
655 |    },
656 |    "file_extension": ".py",
657 |    "mimetype": "text/x-python",
658 |    "name": "python",
659 |    "nbconvert_exporter": "python",
660 |    "pygments_lexer": "ipython3",
661 |    "version": "3.7.3"
662 |   }
663 |  },
664 |  "nbformat": 4,
665 |  "nbformat_minor": 2
666 | }
667 | 


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/README.md:
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1 | # Hate-Speech-Classification-deployed-using-Flask


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/__init__.py:
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1 | 
2 | 


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/__pycache__/__init__.cpython-310.pyc:
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/__pycache__/get_tweets.cpython-310.pyc:
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https://raw.githubusercontent.com/lakshay-arora/Hate-Speech-Classification-deployed-using-Flask/f69c873667f327907d1ab2e34ec119eaf75840f6/__pycache__/get_tweets.cpython-310.pyc


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/get_sentiment.py:
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 1 | from flask import Flask, render_template, request, redirect, url_for
 2 | from joblib import load
 3 | from get_tweets import get_related_tweets
 4 | 
 5 | 
 6 | pipeline = load("text_classification.joblib")
 7 | 
 8 | 
 9 | def requestResults(name):
10 |     tweets = get_related_tweets(name)
11 |     tweets['prediction'] = pipeline.predict(tweets['tweet_text'])
12 |     data = str(tweets.prediction.value_counts()) + '\n\n'
13 |     return data + str(tweets)
14 | 
15 | 
16 | app = Flask(__name__)
17 | 
18 | 
19 | @app.route('/')
20 | def home():
21 |     return render_template('home.html')
22 | 
23 | 
24 | @app.route('/', methods=['POST', 'GET'])
25 | def get_data():
26 |     if request.method == 'POST':
27 |         user = request.form['search']
28 |         return redirect(url_for('success', name=user))
29 | 
30 | 
31 | @app.route('/success/<name>')
32 | def success(name):
33 |     return "<xmp>" + str(requestResults(name)) + " </xmp> "
34 | 
35 | 
36 | if __name__ == '__main__' :
37 |     app.run(debug=True)


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/get_tweets.py:
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 1 | import tweepy
 2 | import time
 3 | import pandas as pd
 4 | pd.set_option('display.max_colwidth', 1000)
 5 | 
 6 | # api key
 7 | api_key = "Enter API Key"
 8 | # api secret key
 9 | api_secret_key = "Enter API Secret Key"
10 | # access token
11 | access_token = "Enter Access Token"
12 | # access token secret
13 | access_token_secret = "Enter Access Token Secret"
14 | 
15 | authentication = tweepy.OAuthHandler(api_key, api_secret_key)
16 | authentication.set_access_token(access_token, access_token_secret)
17 | api = tweepy.API(authentication, wait_on_rate_limit=True)
18 | 
19 | def get_related_tweets(text_query):
20 |     # list to store tweets
21 |     tweets_list = []
22 |     # no of tweets
23 |     count = 50
24 |     try:
25 |         # Pulling individual tweets from query
26 |         for tweet in api.search_tweets(q=text_query, count=count):
27 |             print(tweet.text)
28 |             # Adding to list that contains all tweets
29 |             tweets_list.append({'created_at': tweet.created_at,
30 |                                 'tweet_id': tweet.id,
31 |                                 'tweet_text': tweet.text})
32 |         return pd.DataFrame.from_dict(tweets_list)
33 | 
34 |     except BaseException as e:
35 |         print('failed on_status,', str(e))
36 |         time.sleep(3)


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/templates/home.html:
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  1 | <!DOCTYPE html>
  2 | <html>
  3 | <head>
  4 | <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
  5 | <style>
  6 | * {box-sizing: border-box;}
  7 | 
  8 | body {
  9 |   margin: 0;
 10 |   font-family: Arial, Helvetica, sans-serif;
 11 | }
 12 | 
 13 | .topnav {
 14 |   overflow: hidden;
 15 |   background-color: #e9e9e9;
 16 | }
 17 | 
 18 | .topnav a {
 19 |   float: left;
 20 |   display: block;
 21 |   color: black;
 22 |   text-align: center;
 23 |   padding: 14px 16px;
 24 |   text-decoration: none;
 25 |   font-size: 17px;
 26 | }
 27 | 
 28 | .topnav a:hover {
 29 |   background-color: #ddd;
 30 |   color: black;
 31 | }
 32 | 
 33 | .topnav a.active {
 34 |   background-color: #2196F3;
 35 |   color: white;
 36 | }
 37 | 
 38 | .topnav .search-container {
 39 |   float: right ;
 40 | }
 41 | 
 42 | .topnav input[type=text] {
 43 |   padding: 6px;
 44 |   margin-top: 8px;
 45 |   font-size: 17px;
 46 |   border: none;
 47 |   width: 700px;
 48 | }
 49 | 
 50 | .topnav .search-container button {
 51 |   float: right;
 52 |   padding: 6px 10px;
 53 |   margin-top: 8px;
 54 |   margin-right: 16px;
 55 |   background: #ddd;
 56 |   font-size: 17px;
 57 |   border: none;
 58 |   cursor: pointer;
 59 |   width: 100px;
 60 | }
 61 | 
 62 | .topnav .search-container button:hover {
 63 |   background: #ccc;
 64 | }
 65 | 
 66 | @media screen and (max-width: 600px) {
 67 |   .topnav .search-container {
 68 |     float: none;
 69 |   }
 70 |   .topnav a, .topnav input[type=text], .topnav .search-container button {
 71 |     float: none;
 72 |     display: block;
 73 |     text-align: left;
 74 |     width: 100%;
 75 |     margin: 0;
 76 |     padding: 14px;
 77 |   }
 78 |   .topnav input[type=text] {
 79 |     border: 1px solid #ccc;
 80 |   }
 81 | }
 82 | </style>
 83 | </head>
 84 | <body>
 85 | 
 86 | <div class="topnav">
 87 |   <a class="active" href="#home">Home</a>
 88 |   <div class="search-container">
 89 |      <form action = "http://localhost:5000/" method = "post">
 90 |       <input type="text" placeholder="Search" name="search">
 91 |       <button type="submit"><i class="fa fa-search"></i></button>
 92 |     </form>
 93 |   </div>
 94 | </div>
 95 | 
 96 | <div style="padding-left:16px">
 97 |   <h2> Twitter Sentiment Analysis </h2>
 98 | </div>
 99 | 
100 | </body>
101 | </html>
102 | 
103 | 
104 | 


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/text_classification.joblib:
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https://raw.githubusercontent.com/lakshay-arora/Hate-Speech-Classification-deployed-using-Flask/f69c873667f327907d1ab2e34ec119eaf75840f6/text_classification.joblib


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