Movies review sentiment analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "16cba715",
   "metadata": {},
   "source": [
    "# IMDB movies reviews sentiment analysis\n",
    "### 1. Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "546ea771",
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac93c608",
   "metadata": {},
   "outputs": [],
   "source": [
    "#readind the data\n",
    "df = pd.read_csv(\"IMDB Dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c828702f",
   "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>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>One of the other reviewers has mentioned that ...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I thought this was a wonderful way to spend ti...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basically there's a family where a little boy ...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>I thought this movie did a down right good job...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>Bad plot, bad dialogue, bad acting, idiotic di...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>I am a Catholic taught in parochial elementary...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>I'm going to have to disagree with the previou...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>No one expects the Star Trek movies to be high...</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  review sentiment\n",
       "0      One of the other reviewers has mentioned that ...  positive\n",
       "1      A wonderful little production. <br /><br />The...  positive\n",
       "2      I thought this was a wonderful way to spend ti...  positive\n",
       "3      Basically there's a family where a little boy ...  negative\n",
       "4      Petter Mattei's \"Love in the Time of Money\" is...  positive\n",
       "...                                                  ...       ...\n",
       "49995  I thought this movie did a down right good job...  positive\n",
       "49996  Bad plot, bad dialogue, bad acting, idiotic di...  negative\n",
       "49997  I am a Catholic taught in parochial elementary...  negative\n",
       "49998  I'm going to have to disagree with the previou...  negative\n",
       "49999  No one expects the Star Trek movies to be high...  negative\n",
       "\n",
       "[50000 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ce7a76fb",
   "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>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50000</td>\n",
       "      <td>50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>49582</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Loved today's show!!! It was a variety and not...</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>5</td>\n",
       "      <td>25000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   review sentiment\n",
       "count                                               50000     50000\n",
       "unique                                              49582         2\n",
       "top     Loved today's show!!! It was a variety and not...  positive\n",
       "freq                                                    5     25000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11a3732b",
   "metadata": {},
   "source": [
    "### 2. Cleaning the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "30f6a8b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 2)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1b3cc641",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sampling limited data\n",
    "#df = df.sample(20000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c3a7063a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   review     50000 non-null  object\n",
      " 1   sentiment  50000 non-null  object\n",
      "dtypes: object(2)\n",
      "memory usage: 781.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#checking for null values\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d69fb069",
   "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>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>One of the other reviewers has mentioned that ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A wonderful little production. &lt;br /&gt;&lt;br /&gt;The...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I thought this was a wonderful way to spend ti...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Basically there's a family where a little boy ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Petter Mattei's \"Love in the Time of Money\" is...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>I thought this movie did a down right good job...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>Bad plot, bad dialogue, bad acting, idiotic di...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>I am a Catholic taught in parochial elementary...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>I'm going to have to disagree with the previou...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>No one expects the Star Trek movies to be high...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  review  sentiment\n",
       "0      One of the other reviewers has mentioned that ...          1\n",
       "1      A wonderful little production. <br /><br />The...          1\n",
       "2      I thought this was a wonderful way to spend ti...          1\n",
       "3      Basically there's a family where a little boy ...          0\n",
       "4      Petter Mattei's \"Love in the Time of Money\" is...          1\n",
       "...                                                  ...        ...\n",
       "49995  I thought this movie did a down right good job...          1\n",
       "49996  Bad plot, bad dialogue, bad acting, idiotic di...          0\n",
       "49997  I am a Catholic taught in parochial elementary...          0\n",
       "49998  I'm going to have to disagree with the previou...          0\n",
       "49999  No one expects the Star Trek movies to be high...          0\n",
       "\n",
       "[50000 rows x 2 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#replacing positive and negative with 1 and 0\n",
    "df['sentiment'].replace({'positive':1,'negative':0},inplace = True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c0019caa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "def clean_html(text):# function for removing html from a text\n",
    "    clean = re.compile('<.*?>')\n",
    "    return re.sub(clean,'',text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2394eebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review']=df['review'].apply(clean_html)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c5ee3f6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting everything to lowercase\n",
    "def convert_lower(text):\n",
    "    return text.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ede75465",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review']=df['review'].apply(convert_lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fabed799",
   "metadata": {},
   "outputs": [],
   "source": [
    "#removing special characters\n",
    "def remove_special(text):\n",
    "    x=''\n",
    "    for i in text:\n",
    "        if i.isalnum():\n",
    "            x=x+i\n",
    "        else:\n",
    "            x=x+' '\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f567bed9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review']=df['review'].apply(remove_special)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fc891666",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\gaura\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# removing stop words\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "42683cd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "swlist = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bf953ca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_stopwords(text):\n",
    "    x =[]\n",
    "    for i in text.split():\n",
    "        if i not in swlist:\n",
    "            x.append(i)\n",
    "    y=x[:]\n",
    "    x.clear\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b8cffe69",
   "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>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[one, reviewers, mentioned, watching, 1, oz, e...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[wonderful, little, production, filming, techn...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[thought, wonderful, way, spend, time, hot, su...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[basically, family, little, boy, jake, thinks,...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[petter, mattei, love, time, money, visually, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>[thought, movie, right, good, job, creative, o...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>[bad, plot, bad, dialogue, bad, acting, idioti...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>[catholic, taught, parochial, elementary, scho...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>[going, disagree, previous, comment, side, mal...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>[one, expects, star, trek, movies, high, art, ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  review  sentiment\n",
       "0      [one, reviewers, mentioned, watching, 1, oz, e...          1\n",
       "1      [wonderful, little, production, filming, techn...          1\n",
       "2      [thought, wonderful, way, spend, time, hot, su...          1\n",
       "3      [basically, family, little, boy, jake, thinks,...          0\n",
       "4      [petter, mattei, love, time, money, visually, ...          1\n",
       "...                                                  ...        ...\n",
       "49995  [thought, movie, right, good, job, creative, o...          1\n",
       "49996  [bad, plot, bad, dialogue, bad, acting, idioti...          0\n",
       "49997  [catholic, taught, parochial, elementary, scho...          0\n",
       "49998  [going, disagree, previous, comment, side, mal...          0\n",
       "49999  [one, expects, star, trek, movies, high, art, ...          0\n",
       "\n",
       "[50000 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['review']=df['review'].apply(remove_stopwords)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f8198bbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#stemming\n",
    "from nltk.stem.porter import PorterStemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "21715e4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = []\n",
    "def stem_words(text):\n",
    "    for i in text:\n",
    "        y.append(PorterStemmer().stem(i))\n",
    "    z=y[:]\n",
    "    y.clear()\n",
    "    return z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9b251f79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['love', 'love', 'me', 'i']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#example of stemming\n",
    "stem_words(['loved','loving','me','I'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "592b46bb",
   "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>review</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[one, reviewers, mentioned, watching, 1, oz, e...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[wonderful, little, production, filming, techn...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[thought, wonderful, way, spend, time, hot, su...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[basically, family, little, boy, jake, thinks,...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[petter, mattei, love, time, money, visually, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49995</th>\n",
       "      <td>[thought, movie, right, good, job, creative, o...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>[bad, plot, bad, dialogue, bad, acting, idioti...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>[catholic, taught, parochial, elementary, scho...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>[going, disagree, previous, comment, side, mal...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>[one, expects, star, trek, movies, high, art, ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  review  sentiment\n",
       "0      [one, reviewers, mentioned, watching, 1, oz, e...          1\n",
       "1      [wonderful, little, production, filming, techn...          1\n",
       "2      [thought, wonderful, way, spend, time, hot, su...          1\n",
       "3      [basically, family, little, boy, jake, thinks,...          0\n",
       "4      [petter, mattei, love, time, money, visually, ...          1\n",
       "...                                                  ...        ...\n",
       "49995  [thought, movie, right, good, job, creative, o...          1\n",
       "49996  [bad, plot, bad, dialogue, bad, acting, idioti...          0\n",
       "49997  [catholic, taught, parochial, elementary, scho...          0\n",
       "49998  [going, disagree, previous, comment, side, mal...          0\n",
       "49999  [one, expects, star, trek, movies, high, art, ...          0\n",
       "\n",
       "[50000 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b33be5d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review']=df['review'].apply(stem_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e2963c4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#join back\n",
    "\n",
    "def join_back(list_input):\n",
    "    return \" \".join(list_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "44a2d523",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['review']=df['review'].apply(join_back)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ac6469bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        one review mention watch 1 oz episod hook righ...\n",
       "1        wonder littl product film techniqu unassum old...\n",
       "2        thought wonder way spend time hot summer weeke...\n",
       "3        basic famili littl boy jake think zombi closet...\n",
       "4        petter mattei love time money visual stun film...\n",
       "                               ...                        \n",
       "49995    thought movi right good job creativ origin fir...\n",
       "49996    bad plot bad dialogu bad act idiot direct anno...\n",
       "49997    cathol taught parochi elementari school nun ta...\n",
       "49998    go disagre previou comment side maltin one sec...\n",
       "49999    one expect star trek movi high art fan expect ...\n",
       "Name: review, Length: 50000, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['review']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2eb41f45",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,0:1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4deb47f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 1)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "adbf4e26",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(max_features=1500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "890a213f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=cv.fit_transform(df['review']).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7543516f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 1500)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cf83e733",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "47369c56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000,)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1005095d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting the dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(X,y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4d1cb636",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40000, 1500)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9fecc5de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 1500)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "42cf0b4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB,MultinomialNB,BernoulliNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "0b3fd6d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf1= GaussianNB()\n",
    "clf2= MultinomialNB()\n",
    "clf3= BernoulliNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "4a1f0eff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf1.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e299e283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf2.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7fd37229",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BernoulliNB()"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf3.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "80e56399",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred1  = clf1.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b07eac28",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred2  = clf2.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "40649186",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred3  = clf3.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "e8c04e25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "62dbc133",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "b153d20d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "715bea1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gaussian 0.7634\n",
      "Multinomial 0.8395\n",
      "Bernoulli 0.8449\n"
     ]
    }
   ],
   "source": [
    "print('Gaussian',accuracy_score(y_test,y_pred1))\n",
    "print('Multinomial',accuracy_score(y_test,y_pred2))\n",
    "print('Bernoulli',accuracy_score(y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd3393ad",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}