├── README ├── analise_sentimento_ok.ipynb └── valid_pos.csv /README: -------------------------------------------------------------------------------- 1 | LIAR: A BENCHMARK DATASET FOR FAKE NEWS DETECTION 2 | 3 | William Yang Wang, "Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection, to appear in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), short paper, Vancouver, BC, Canada, July 30-August 4, ACL. 4 | ===================================================================== 5 | Description of the TSV format: 6 | 7 | Column 1: the ID of the statement ([ID].json). 8 | Column 2: the label. 9 | Column 3: the statement. 10 | Column 4: the subject(s). 11 | Column 5: the speaker. 12 | Column 6: the speaker's job title. 13 | Column 7: the state info. 14 | Column 8: the party affiliation. 15 | Column 9-13: the total credit history count, including the current statement. 16 | 9: barely true counts. 17 | 10: false counts. 18 | 11: half true counts. 19 | 12: mostly true counts. 20 | 13: pants on fire counts. 21 | Column 14: the context (venue / location of the speech or statement). 22 | 23 | Note that we do not provide the full-text verdict report in this current version of the dataset, 24 | but you can use the following command to access the full verdict report and links to the source documents: 25 | wget http://www.politifact.com//api/v/2/statement/[ID]/?format=json 26 | 27 | ====================================================================== 28 | The original sources retain the copyright of the data. 29 | 30 | Note that there are absolutely no guarantees with this data, 31 | and we provide this dataset "as is", 32 | but you are welcome to report the issues of the preliminary version 33 | of this data. 34 | 35 | You are allowed to use this dataset for research purposes only. 36 | 37 | For more question about the dataset, please contact: 38 | William Wang, william@cs.ucsb.edu 39 | 40 | v1.0 04/23/2017 41 | 42 | 43 | 44 | this dataset is the same used in named search: 45 | 46 | "Liar, Liar Pants on Fire": 47 | The New Benchmark Dataset for Fake News Detection 48 | 49 | LINK: https://www.cs.ucsb.edu/~william/data/liar_dataset.zip 50 | 51 | 2019-03-02 52 | 53 | 54 | 55 | 56 | 57 | 58 | -------------------------------------------------------------------------------- /analise_sentimento_ok.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 0, 4 | "metadata": { 5 | "colab": { 6 | "name": "analise_sentimento_ok.ipynb", 7 | "version": "0.3.2", 8 | "provenance": [], 9 | "include_colab_link": true 10 | }, 11 | "kernelspec": { 12 | "name": "python3", 13 | "display_name": "Python 3" 14 | } 15 | }, 16 | "cells": [ 17 | { 18 | "cell_type": "markdown", 19 | "metadata": { 20 | "id": "view-in-github", 21 | "colab_type": "text" 22 | }, 23 | "source": [ 24 | "\"Open" 25 | ] 26 | }, 27 | { 28 | "metadata": { 29 | "id": "6Ea_3O-qaj32", 30 | "colab_type": "code", 31 | "colab": { 32 | "base_uri": "https://localhost:8080/", 33 | "height": 52 34 | }, 35 | "outputId": "47f8a2ad-9b99-493a-df2d-2dc37a6ae45e" 36 | }, 37 | "cell_type": "code", 38 | "source": [ 39 | "!pip install langdetect" 40 | ], 41 | "execution_count": 28, 42 | "outputs": [ 43 | { 44 | "output_type": "stream", 45 | "text": [ 46 | "Requirement already satisfied: langdetect in /usr/local/lib/python3.6/dist-packages (1.0.7)\n", 47 | "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from langdetect) (1.11.0)\n" 48 | ], 49 | "name": "stdout" 50 | } 51 | ] 52 | }, 53 | { 54 | "metadata": { 55 | "id": "SlBb7LodTqP5", 56 | "colab_type": "code", 57 | "colab": { 58 | "base_uri": "https://localhost:8080/", 59 | "height": 123 60 | }, 61 | "outputId": "cbf33833-0fa7-4909-c036-d83e517329c9" 62 | }, 63 | "cell_type": "code", 64 | "source": [ 65 | "import pandas as pd\n", 66 | "import numpy as np\n", 67 | "import warnings\n", 68 | "warnings.filterwarnings('ignore')\n", 69 | "import nltk\n", 70 | "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n", 71 | "nltk.download('vader_lexicon')\n", 72 | "import string\n", 73 | "import nltk\n", 74 | "from nltk import PorterStemmer\n", 75 | "import re\n", 76 | "nltk.download('stopwords')\n", 77 | "nltk.download('wordnet')\n", 78 | "stopwords = nltk.corpus.stopwords.words('english')\n", 79 | "from gensim import corpora\n", 80 | "import gensim\n", 81 | "from sklearn.metrics import confusion_matrix, f1_score, classification_report\n", 82 | "from sklearn.naive_bayes import MultinomialNB\n", 83 | "from sklearn.pipeline import Pipeline\n", 84 | "from sklearn.feature_extraction.text import CountVectorizer\n", 85 | "\n" 86 | ], 87 | "execution_count": 74, 88 | "outputs": [ 89 | { 90 | "output_type": "stream", 91 | "text": [ 92 | "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n", 93 | "[nltk_data] Package vader_lexicon is already up-to-date!\n", 94 | "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", 95 | "[nltk_data] Package stopwords is already up-to-date!\n", 96 | "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", 97 | "[nltk_data] Package wordnet is already up-to-date!\n" 98 | ], 99 | "name": "stdout" 100 | } 101 | ] 102 | }, 103 | { 104 | "metadata": { 105 | "id": "ch-a61ODUTE6", 106 | "colab_type": "code", 107 | "colab": {} 108 | }, 109 | "cell_type": "code", 110 | "source": [ 111 | "uri_train = 'https://raw.githubusercontent.com/thiagorainmaker77/liar_dataset/master/train.tsv'\n", 112 | "uri_valid = 'https://raw.githubusercontent.com/thiagorainmaker77/liar_dataset/master/valid.tsv'\n", 113 | "uri_test = 'https://raw.githubusercontent.com/thiagorainmaker77/liar_dataset/master/test.tsv'\n", 114 | "\n", 115 | " \n", 116 | "df_train = pd.read_table(uri_train,\n", 117 | " names = ['id',\t'label'\t,'statement',\t'subject',\t'speaker', \t'job', \t'state',\t'party',\t'barely_true_c',\t'false_c',\t'half_true_c',\t'mostly_true_c',\t'pants_on_fire_c',\t'venue'])\n", 118 | "\n", 119 | " \n", 120 | "df_valid = pd.read_table(uri_valid,\n", 121 | " names =['id',\t'label'\t,'statement',\t'subject',\t'speaker', \t'job', \t'state',\t'party',\t'barely_true_c',\t'false_c',\t'half_true_c',\t'mostly_true_c',\t'pants_on_fire_c',\t'venue'])\n", 122 | "\n", 123 | "\n", 124 | "df_test = pd.read_csv(uri_test, sep='\\t', \n", 125 | " names =['id',\t'label'\t,'statement',\t'subject',\t'speaker', \t'job', \t'state',\t'party',\t'barely_true_c',\t'false_c',\t'half_true_c',\t'mostly_true_c',\t'pants_on_fire_c',\t'venue']) \n", 126 | "\n", 127 | "\n", 128 | "df = pd.concat([df_train, df_valid])" 129 | ], 130 | "execution_count": 0, 131 | "outputs": [] 132 | }, 133 | { 134 | "metadata": { 135 | "id": "m3268GrRUvtA", 136 | "colab_type": "code", 137 | "colab": {} 138 | }, 139 | "cell_type": "code", 140 | "source": [ 141 | "liar_list = [df,df_test]\n", 142 | "\n", 143 | "n = 10\n", 144 | "\n", 145 | "subject = liar_list[0].groupby(['subject']).count().label.sort_values(ascending=False)[:n].index.tolist()\n", 146 | "speaker = liar_list[0].groupby(['speaker']).count().label.sort_values(ascending=False)[:n].index.tolist()\n", 147 | "job = liar_list[0].groupby(['job']).count().label.sort_values(ascending=False)[:n].index.tolist()\n", 148 | "state = liar_list[0].groupby(['state']).count().label.sort_values(ascending=False)[:n].index.tolist()\n", 149 | "party = liar_list[0].groupby(['party']).count().label.sort_values(ascending=False)[:n].index.tolist()\n", 150 | "venue = liar_list[0].groupby(['venue']).count().label.sort_values(ascending=False)[:n].index.tolist()\n" 151 | ], 152 | "execution_count": 0, 153 | "outputs": [] 154 | }, 155 | { 156 | "metadata": { 157 | "id": "3X3QP4RUhUDg", 158 | "colab_type": "code", 159 | "colab": {} 160 | }, 161 | "cell_type": "code", 162 | "source": [ 163 | "class MultiColumnLabelEncoder:\n", 164 | " \n", 165 | " \n", 166 | " countV = CountVectorizer(analyzer='word', binary=True, decode_error='strict', encoding='utf-8', input='content',\n", 167 | " lowercase=True, max_df=0.5, max_features=None, min_df=1,\n", 168 | " ngram_range=(1, 1), preprocessor=None, stop_words='english',\n", 169 | " strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n", 170 | " tokenizer=None, vocabulary=None)\n", 171 | " \n", 172 | " def __init__(self,columns = None):\n", 173 | " self.columns = columns # array of column names to encode\n", 174 | "\n", 175 | " def fit(self,X,y=None):\n", 176 | " return self # not relevant here\n", 177 | "\n", 178 | " def transform(self,X):\n", 179 | " '''\n", 180 | " Transforms columns of X specified in self.columns using\n", 181 | " LabelEncoder(). If no columns specified, transforms all\n", 182 | " columns in X.\n", 183 | " '''\n", 184 | " output = X.copy()\n", 185 | " if self.columns is not None:\n", 186 | " for col in self.columns:\n", 187 | " output[col] = countV().fit_transform(output[col])\n", 188 | " else:\n", 189 | " for colname,col in output.iteritems():\n", 190 | " output[colname] = countV().fit_transform(col)\n", 191 | " return output\n", 192 | "\n", 193 | " def fit_transform(self,X,y=None):\n", 194 | " return self.fit(X,y).transform(X)" 195 | ], 196 | "execution_count": 0, 197 | "outputs": [] 198 | }, 199 | { 200 | "metadata": { 201 | "id": "tVUVl7D-WYvY", 202 | "colab_type": "code", 203 | "colab": {} 204 | }, 205 | "cell_type": "code", 206 | "source": [ 207 | "features_of_interest = state+subject+speaker+party+venue+job\n", 208 | "to_encode = [('speaker',speaker),\n", 209 | " ('party',party),\n", 210 | " ('subject',subject),\n", 211 | " ('state',state),\n", 212 | " ('venue',venue),\n", 213 | " ('job',job)]\n", 214 | "for i in liar_list:\n", 215 | " for var in to_encode:\n", 216 | " for value in var[1]:\n", 217 | " i[value] = i[var[0]].apply(lambda x: x==value)" 218 | ], 219 | "execution_count": 0, 220 | "outputs": [] 221 | }, 222 | { 223 | "metadata": { 224 | "id": "gKvnX_GhWz46", 225 | "colab_type": "code", 226 | "colab": {} 227 | }, 228 | "cell_type": "code", 229 | "source": [ 230 | "truth = {'false':0.,'half-true':0.5,'mostly-true':0.75,'true':1.,'pants-fire':-0.25,'barely-true':0.25} \n", 231 | "\n", 232 | "\n", 233 | "for i in liar_list:\n", 234 | " i['numer_truth'] = i['label'].apply(lambda x: truth[x])" 235 | ], 236 | "execution_count": 0, 237 | "outputs": [] 238 | }, 239 | { 240 | "metadata": { 241 | "id": "kqQJrjfskIPm", 242 | "colab_type": "code", 243 | "colab": {} 244 | }, 245 | "cell_type": "code", 246 | "source": [ 247 | "def score(file):\n", 248 | " data_pre = pd.read_csv(file, sep='\\n', names = [\"label\"]) \n", 249 | " score = f1_score(df_test['label'],data_pre, average='micro')\n", 250 | " return score" 251 | ], 252 | "execution_count": 0, 253 | "outputs": [] 254 | }, 255 | { 256 | "metadata": { 257 | "id": "ZHE-8CBdXZFf", 258 | "colab_type": "code", 259 | "colab": {} 260 | }, 261 | "cell_type": "code", 262 | "source": [ 263 | "\n", 264 | "sid = SentimentIntensityAnalyzer()\n", 265 | "\n", 266 | "ps = PorterStemmer()\n", 267 | "wn = nltk.WordNetLemmatizer()\n", 268 | "\n", 269 | "def clean_stem (sent, seq=False):\n", 270 | " temp1=sent\n", 271 | " temp2 = re.split('\\W+',temp1)\n", 272 | " temp3 = [ps.stem(x) for x in temp2 if x not in stopwords]\n", 273 | " return temp3\n", 274 | "def sentiment(x):\n", 275 | " score = sid.polarity_scores(x)\n", 276 | " return score['compound']" 277 | ], 278 | "execution_count": 0, 279 | "outputs": [] 280 | }, 281 | { 282 | "metadata": { 283 | "id": "nPxdVRKtYSkE", 284 | "colab_type": "code", 285 | "colab": {} 286 | }, 287 | "cell_type": "code", 288 | "source": [ 289 | "for i in liar_list:\n", 290 | " i['sentiment'] = i['statement'].apply(lambda x: sentiment(' '.join(clean_stem(x))))" 291 | ], 292 | "execution_count": 0, 293 | "outputs": [] 294 | }, 295 | { 296 | "metadata": { 297 | "id": "gMSPFemgY7mI", 298 | "colab_type": "code", 299 | "colab": {} 300 | }, 301 | "cell_type": "code", 302 | "source": [ 303 | "df_test['sentiment'] = df_test['statement'].apply(lambda x: sentiment(' '.join(clean_stem(x))))" 304 | ], 305 | "execution_count": 0, 306 | "outputs": [] 307 | }, 308 | { 309 | "metadata": { 310 | "id": "J17BdtpUjVkZ", 311 | "colab_type": "code", 312 | "colab": {} 313 | }, 314 | "cell_type": "code", 315 | "source": [ 316 | "countV = CountVectorizer(analyzer='word', binary=True, decode_error='strict', encoding='utf-8', input='content',\n", 317 | " lowercase=True, max_df=0.5, max_features=None, min_df=1,\n", 318 | " ngram_range=(1, 1), preprocessor=None, stop_words='english',\n", 319 | " strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n", 320 | " tokenizer=None, vocabulary=None)" 321 | ], 322 | "execution_count": 0, 323 | "outputs": [] 324 | }, 325 | { 326 | "metadata": { 327 | "id": "yiBzdtzknrrp", 328 | "colab_type": "code", 329 | "colab": { 330 | "base_uri": "https://localhost:8080/", 331 | "height": 52 332 | }, 333 | "outputId": "fa691519-e0e6-4a0c-db8a-a230ce704432" 334 | }, 335 | "cell_type": "code", 336 | "source": [ 337 | "countV.fit_transform(df['statement'].values)\n", 338 | "\n" 339 | ], 340 | "execution_count": 92, 341 | "outputs": [ 342 | { 343 | "output_type": "execute_result", 344 | "data": { 345 | "text/plain": [ 346 | "<11524x12590 sparse matrix of type ''\n", 347 | "\twith 114275 stored elements in Compressed Sparse Row format>" 348 | ] 349 | }, 350 | "metadata": { 351 | "tags": [] 352 | }, 353 | "execution_count": 92 354 | } 355 | ] 356 | }, 357 | { 358 | "metadata": { 359 | "id": "bc16o9-dnnF4", 360 | "colab_type": "code", 361 | "colab": {} 362 | }, 363 | "cell_type": "code", 364 | "source": [ 365 | "\n", 366 | "nb_cv = Pipeline([\n", 367 | " ('NBCV', countV),\n", 368 | " ('nb_clf', MultinomialNB(alpha=1.5, class_prior=None, fit_prior=True))])" 369 | ], 370 | "execution_count": 0, 371 | "outputs": [] 372 | }, 373 | { 374 | "metadata": { 375 | "id": "Hmj6zOg5j-LO", 376 | "colab_type": "code", 377 | "colab": { 378 | "base_uri": "https://localhost:8080/", 379 | "height": 565 380 | }, 381 | "outputId": "7ceae854-6525-4001-f77d-76fdb76133a8" 382 | }, 383 | "cell_type": "code", 384 | "source": [ 385 | "nb_cv.fit(df['statement'] + df['sentiment'],df['label'])\n", 386 | "\n", 387 | "\n", 388 | "\n" 389 | ], 390 | "execution_count": 91, 391 | "outputs": [ 392 | { 393 | "output_type": "error", 394 | "ename": "TypeError", 395 | "evalue": "ignored", 396 | "traceback": [ 397 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 398 | "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", 399 | 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\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 401 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_numexpr\u001b[0;34m(op, op_str, a, b, truediv, reversed, **eval_kwargs)\u001b[0m\n\u001b[1;32m 122\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 123\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_evaluate_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop_str\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", 402 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/computation/expressions.py\u001b[0m in \u001b[0;36m_evaluate_standard\u001b[0;34m(op, op_str, a, b, **eval_kwargs)\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", 403 | "\u001b[0;31mTypeError\u001b[0m: must be str, not float", 404 | "\nDuring handling of the above exception, another exception occurred:\n", 405 | "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", 406 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36msafe_na_op\u001b[0;34m(lvalues, rvalues)\u001b[0m\n\u001b[1;32m 1528\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1529\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mna_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1530\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 407 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mna_op\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m 1506\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1507\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmasked_arith_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1508\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", 408 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mmasked_arith_op\u001b[0;34m(x, y, op)\u001b[0m\n\u001b[1;32m 1008\u001b[0m result[mask] = op(xrav[mask],\n\u001b[0;32m-> 1009\u001b[0;31m com.values_from_object(yrav[mask]))\n\u001b[0m\u001b[1;32m 1010\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", 409 | "\u001b[0;31mTypeError\u001b[0m: must be str, not float", 410 | "\nDuring handling of the above exception, another exception occurred:\n", 411 | "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", 412 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnb_cv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'statement'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'sentiment'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'label'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", 413 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(left, right)\u001b[0m\n\u001b[1;32m 1581\u001b[0m \u001b[0mrvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1582\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1583\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msafe_na_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1584\u001b[0m return construct_result(left, result,\n\u001b[1;32m 1585\u001b[0m index=left.index, name=res_name, dtype=None)\n", 414 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36msafe_na_op\u001b[0;34m(lvalues, rvalues)\u001b[0m\n\u001b[1;32m 1531\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1532\u001b[0m return libalgos.arrmap_object(lvalues,\n\u001b[0;32m-> 1533\u001b[0;31m lambda x: op(x, rvalues))\n\u001b[0m\u001b[1;32m 1534\u001b[0m \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", 415 | "\u001b[0;32mpandas/_libs/algos.pyx\u001b[0m in \u001b[0;36mpandas._libs.algos.arrmap\u001b[0;34m()\u001b[0m\n", 416 | "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36m\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 1531\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1532\u001b[0m return libalgos.arrmap_object(lvalues,\n\u001b[0;32m-> 1533\u001b[0;31m lambda x: op(x, rvalues))\n\u001b[0m\u001b[1;32m 1534\u001b[0m \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1535\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", 417 | "\u001b[0;31mTypeError\u001b[0m: ufunc 'add' did not contain a loop with signature matching types dtype(''\n", 488 | "\twith 114275 stored elements in Compressed Sparse Row format>" 489 | ] 490 | }, 491 | "metadata": { 492 | "tags": [] 493 | }, 494 | "execution_count": 81 495 | } 496 | ] 497 | }, 498 | { 499 | "metadata": { 500 | "id": "4Ya9dKwklrAc", 501 | "colab_type": "code", 502 | "colab": {} 503 | }, 504 | "cell_type": "code", 505 | "source": [ 506 | "nb_cv" 507 | ], 508 | "execution_count": 0, 509 | "outputs": [] 510 | } 511 | ] 512 | } -------------------------------------------------------------------------------- /valid_pos.csv: -------------------------------------------------------------------------------- 1 | index,$,'',(,),",",--,.,:,CC,CD,DT,EX,FW,IN,JJ,JJR,JJS,LS,MD,NN,NNP,NNPS,NNS,PDT,POS,PRP,PRP$,RB,RBR,RBS,RP,SYM,TO,UH,VB,VBD,VBG,VBN,VBP,VBZ,WDT,WP,WP$,WRB,`` 2 | 0,0,0,0,0,0,0,0,0,0,0,1,0,0,2,0,1,0,0,0,0,0,1,1,0,0,1,0,1,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0 3 | 0,0,0,0,0,0,0,0,0,2,0,2,0,0,1,2,0,0,0,0,2,6,0,1,0,0,1,1,2,0,0,0,0,1,0,1,1,0,1,0,1,0,0,0,1,0 4 | 0,0,0,0,0,0,0,0,0,1,1,3,0,0,3,2,0,0,0,0,3,0,0,3,0,0,1,0,3,0,0,0,0,1,0,0,2,4,2,0,2,0,0,0,1,0 5 | 0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,1,1,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0 6 | 0,0,0,0,0,0,0,0,0,0,1,3,0,0,9,4,1,0,0,0,4,0,1,2,1,0,2,0,1,0,0,0,0,0,0,0,0,0,0,2,2,0,0,0,0,0 7 | 0,0,0,0,0,0,0,0,0,0,3,2,0,0,3,2,0,0,0,0,3,0,0,5,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,2,1,0,0,0,1,0 8 | 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