Week_8/Day_4/Sentiment_analysis_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import naive_bayes as nb\n",
    "from sklearn.metrics import roc_auc_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train_data.txt', sep='\\t', names=['liked','txt'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>liked</th>\n",
       "      <th>txt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>In the beginning God created the heavens and t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>And God said, Let there be light, and there wa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>And God said, Let there be a vault between the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>And God said, Let the water under the sky be g...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>Then God said, Let the land produce vegetation...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30454</th>\n",
       "      <td>0</td>\n",
       "      <td>Brokeback Mountain was boring.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30455</th>\n",
       "      <td>0</td>\n",
       "      <td>So Brokeback Mountain was really depressing.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30456</th>\n",
       "      <td>0</td>\n",
       "      <td>As I sit here, watching the MTV Movie Awards, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30457</th>\n",
       "      <td>0</td>\n",
       "      <td>Ok brokeback mountain is such a horrible movie.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30458</th>\n",
       "      <td>0</td>\n",
       "      <td>Oh, and Brokeback Mountain was a terrible movie.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30459 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       liked                                                txt\n",
       "0          0  In the beginning God created the heavens and t...\n",
       "1          1  And God said, Let there be light, and there wa...\n",
       "2          1  And God said, Let there be a vault between the...\n",
       "3          1  And God said, Let the water under the sky be g...\n",
       "4          1  Then God said, Let the land produce vegetation...\n",
       "...      ...                                                ...\n",
       "30454      0                     Brokeback Mountain was boring.\n",
       "30455      0       So Brokeback Mountain was really depressing.\n",
       "30456      0  As I sit here, watching the MTV Movie Awards, ...\n",
       "30457      0    Ok brokeback mountain is such a horrible movie.\n",
       "30458      0   Oh, and Brokeback Mountain was a terrible movie.\n",
       "\n",
       "[30459 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "stopset = set(stopwords.words('english'))\n",
    "vectorizer = TfidfVectorizer(use_idf=True,lowercase=True,strip_accents='ascii', stop_words = stopset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.liked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = vectorizer.fit_transform(df.txt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30459, 7121)\n",
      "(30459,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = nb.MultinomialNB()\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00656358, 0.00372963, 0.99287227, ..., 0.98470662, 0.99977619,\n",
       "       0.5314481 ])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted = clf.predict_proba(X_test)[:,1]\n",
    "predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9986838806095952"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "roc_auc_score(y_test, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_text = [\"The movie was good at being bad, and when I say bad, I mean bad ass \"]\n",
    "\n",
    "test_text = vectorizer.transform(test_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(test_text)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}