Sucharita_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0d4c73af",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\HP\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping corpora\\stopwords.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "\n",
    "# Import functions for data preprocessing & data preparation\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.utils import resample\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.stem import PorterStemmer, LancasterStemmer\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.corpus import wordnet\n",
    "import string\n",
    "from string import punctuation\n",
    "import nltk\n",
    "import re\n",
    "\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "70921c00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in c:\\users\\hp\\anaconda3\\lib\\site-packages (3.7)\n",
      "Requirement already satisfied: tqdm in c:\\users\\hp\\anaconda3\\lib\\site-packages (from nltk) (4.64.0)\n",
      "Requirement already satisfied: regex>=2021.8.3 in c:\\users\\hp\\anaconda3\\lib\\site-packages (from nltk) (2022.3.15)\n",
      "Requirement already satisfied: joblib in c:\\users\\hp\\anaconda3\\lib\\site-packages (from nltk) (1.1.0)\n",
      "Requirement already satisfied: click in c:\\users\\hp\\anaconda3\\lib\\site-packages (from nltk) (8.0.4)\n",
      "Requirement already satisfied: colorama in c:\\users\\hp\\anaconda3\\lib\\site-packages (from click->nltk) (0.4.4)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a8bdad4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'Comment'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading data\n",
    "# For the first dataset: Sucharita: Darlings Review\n",
    "\n",
    "data = pd.read_csv('E:/SEM 7/H SET PROJECT/comments CSVs/scrappedfile_sucharita.csv')\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "50a58296",
   "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>Comment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>You got 50k subscribers!!!!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Loved the movie. The scene where Alia is break...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Noone could have reviewed this better than you...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The ‘Women telling Women Stories’ Dance! It’s ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>This movie was way too good. Wished this came ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>Bollywood people consideing fhe kjo papa nepo ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>I don't like your reviews at all! I'm sorry!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Fake review u nonsense. It's yet to release on...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>The film is misandrist potraying men as villai...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>Promoting violence against men is good for you</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>119 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Comment\n",
       "0                          You got 50k subscribers!!!!\n",
       "1    Loved the movie. The scene where Alia is break...\n",
       "2    Noone could have reviewed this better than you...\n",
       "3    The ‘Women telling Women Stories’ Dance! It’s ...\n",
       "4    This movie was way too good. Wished this came ...\n",
       "..                                                 ...\n",
       "114  Bollywood people consideing fhe kjo papa nepo ...\n",
       "115       I don't like your reviews at all! I'm sorry!\n",
       "116  Fake review u nonsense. It's yet to release on...\n",
       "117  The film is misandrist potraying men as villai...\n",
       "118     Promoting violence against men is good for you\n",
       "\n",
       "[119 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=data.drop(['Unnamed: 0'],axis=1)\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7dc1a2c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\HP\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    },
    {
     "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>Comment</th>\n",
       "      <th>Positive</th>\n",
       "      <th>Negative</th>\n",
       "      <th>Neutral</th>\n",
       "      <th>Compound</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>You got 50k subscribers!!!!</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Loved the movie. The scene where Alia is break...</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.098</td>\n",
       "      <td>0.665</td>\n",
       "      <td>0.7326</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Noone could have reviewed this better than you...</td>\n",
       "      <td>0.277</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.723</td>\n",
       "      <td>0.8006</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The ‘Women telling Women Stories’ Dance! It’s ...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>This movie was way too good. Wished this came ...</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.091</td>\n",
       "      <td>0.807</td>\n",
       "      <td>0.0772</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Comment  Positive  Negative  \\\n",
       "0                        You got 50k subscribers!!!!     0.000     0.000   \n",
       "1  Loved the movie. The scene where Alia is break...     0.236     0.098   \n",
       "2  Noone could have reviewed this better than you...     0.277     0.000   \n",
       "3  The ‘Women telling Women Stories’ Dance! It’s ...     0.000     0.000   \n",
       "4  This movie was way too good. Wished this came ...     0.102     0.091   \n",
       "\n",
       "   Neutral  Compound Sentiment  \n",
       "0    1.000    0.0000   Neutral  \n",
       "1    0.665    0.7326  Positive  \n",
       "2    0.723    0.8006  Positive  \n",
       "3    1.000    0.0000   Neutral  \n",
       "4    0.807    0.0772  Positive  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Labelling\n",
    "\n",
    "nltk.download('vader_lexicon')\n",
    "sentiments = SentimentIntensityAnalyzer()\n",
    "data1[\"Positive\"] = [sentiments.polarity_scores(i)[\"pos\"] for i in data1[\"Comment\"]]\n",
    "data1[\"Negative\"] = [sentiments.polarity_scores(i)[\"neg\"] for i in data1[\"Comment\"]]\n",
    "data1[\"Neutral\"] = [sentiments.polarity_scores(i)[\"neu\"] for i in data1[\"Comment\"]]\n",
    "data1['Compound'] = [sentiments.polarity_scores(i)[\"compound\"] for i in data1[\"Comment\"]]\n",
    "score = data1[\"Compound\"].values\n",
    "sentiment = []\n",
    "for i in score:\n",
    "    if i >= 0.05 :\n",
    "        sentiment.append('Positive')\n",
    "    elif i <= -0.05 :\n",
    "        sentiment.append('Negative')\n",
    "    else:\n",
    "        sentiment.append('Neutral')\n",
    "data1[\"Sentiment\"] = sentiment\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bb43b65c",
   "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>Comment</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>You got 50k subscribers!!!!</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Loved the movie. The scene where Alia is break...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Noone could have reviewed this better than you...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The ‘Women telling Women Stories’ Dance! It’s ...</td>\n",
       "      <td>Neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>This movie was way too good. Wished this came ...</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Comment Sentiment\n",
       "0                        You got 50k subscribers!!!!   Neutral\n",
       "1  Loved the movie. The scene where Alia is break...  Positive\n",
       "2  Noone could have reviewed this better than you...  Positive\n",
       "3  The ‘Women telling Women Stories’ Dance! It’s ...   Neutral\n",
       "4  This movie was way too good. Wished this came ...  Positive"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final Data\n",
    "\n",
    "data2=data1.drop(['Positive','Negative','Neutral','Compound'],axis=1)\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d93685ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Transformation\n",
    "\n",
    "stop_words = stopwords.words('english')\n",
    "porter_stemmer = PorterStemmer()\n",
    "lancaster_stemmer = LancasterStemmer() \n",
    "snowball_stemer = SnowballStemmer(language=\"english\")\n",
    "lzr = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3b2e628c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_processing(text):   \n",
    "    # convert text into lowercase\n",
    "    text = text.lower()\n",
    "\n",
    "    # remove new line characters in text\n",
    "    text = re.sub(r'\\n',' ', text)\n",
    "    \n",
    "    # remove punctuations from text\n",
    "    text = re.sub('[%s]' % re.escape(punctuation), \"\", text)\n",
    "    \n",
    "    # remove references and hashtags from text\n",
    "    text = re.sub(\"^a-zA-Z0-9$,.\", \"\", text)\n",
    "    \n",
    "    # remove multiple spaces from text\n",
    "    text = re.sub(r'\\s+', ' ', text, flags=re.I)\n",
    "    \n",
    "    # remove special characters from text\n",
    "    text = re.sub(r'\\W', ' ', text)\n",
    "\n",
    "    text = ' '.join([word for word in word_tokenize(text) if word not in stop_words])\n",
    "    \n",
    "    # stemming using porter stemmer from nltk package - msh a7sn 7aga - momken: lancaster, snowball\n",
    "    # text=' '.join([porter_stemmer.stem(word) for word in word_tokenize(text)])\n",
    "    # text=' '.join([lancaster_stemmer.stem(word) for word in word_tokenize(text)])\n",
    "    # text=' '.join([snowball_stemer.stem(word) for word in word_tokenize(text)])\n",
    "    \n",
    "    # lemmatizer using WordNetLemmatizer from nltk package\n",
    "    text=' '.join([lzr.lemmatize(word) for word in word_tokenize(text)])\n",
    "\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "62a7566d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package omw-1.4 to\n",
      "[nltk_data]     C:\\Users\\HP\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package omw-1.4 is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\HP\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\HP\\AppData\\Roaming\\nltk_data...\n"
     ]
    }
   ],
   "source": [
    "nltk.download('omw-1.4')\n",
    "nltk.download('punkt')\n",
    "nltk.download('wordnet')\n",
    "data_copy = data2.copy()\n",
    "data_copy.Comment = data_copy.Comment.apply(lambda text: text_processing(text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a8f8ba7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "data_copy['Sentiment'] = le.fit_transform(data_copy['Sentiment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "076ece57",
   "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>Sentence</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>got 50k subscriber</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>loved movie scene alia breaking plate anger ge...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>noone could reviewed better sucharita thank he...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>woman telling woman story dance finally</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>movie way good wished came cinema see uncomfor...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Sentence  Sentiment\n",
       "0                                 got 50k subscriber          1\n",
       "1  loved movie scene alia breaking plate anger ge...          2\n",
       "2  noone could reviewed better sucharita thank he...          2\n",
       "3            woman telling woman story dance finally          1\n",
       "4  movie way good wished came cinema see uncomfor...          2"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_data = {\n",
    "    'Sentence':data_copy.Comment,\n",
    "    'Sentiment':data_copy['Sentiment']\n",
    "}\n",
    "\n",
    "processed_data = pd.DataFrame(processed_data)\n",
    "processed_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f2d8e7de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    63\n",
       "1    36\n",
       "0    20\n",
       "Name: Sentiment, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_data['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a598bb0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_neutral = processed_data[(processed_data['Sentiment']==1)] \n",
    "df_negative = processed_data[(processed_data['Sentiment']==0)]\n",
    "df_positive = processed_data[(processed_data['Sentiment']==2)]\n",
    "\n",
    "# upsample minority classes\n",
    "df_negative_upsampled = resample(df_negative, \n",
    "                                 replace=True,    \n",
    "                                 n_samples= 205, \n",
    "                                 random_state=42)  \n",
    "\n",
    "df_neutral_upsampled = resample(df_neutral, \n",
    "                                 replace=True,    \n",
    "                                 n_samples= 205, \n",
    "                                 random_state=42)  \n",
    "\n",
    "\n",
    "# Concatenate the upsampled dataframes with the neutral dataframe\n",
    "final_data = pd.concat([df_negative_upsampled,df_neutral_upsampled,df_positive])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b64b8417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    205\n",
       "1    205\n",
       "2     63\n",
       "Name: Sentiment, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data['Sentiment'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7c403777",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dont get revenge abuse story domestic violence ka badla domestic violence thar also come fold really boring film thar haider film like dont celebrate revenge domestic violence celebrated told root domestic violence nonsense',\n",
       " 'film misandrist potraying men villain narrative new narrative taken film world day',\n",
       " 'unsubscribedbecause constant irritating rant 50k',\n",
       " 'aint no1 calling u darling matesjokingss',\n",
       " 'second half hopeless like someone kindergarten wrote']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus = []\n",
    "for sentence in final_data['Sentence']:\n",
    "    corpus.append(sentence)\n",
    "corpus[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "63a95af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(max_features=1500)\n",
    "X = cv.fit_transform(corpus).toarray()\n",
    "y = final_data.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6944076d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Machine Learning Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8e7c59ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB()"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\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=0.3, random_state=0)\n",
    "classifier = GaussianNB()\n",
    "classifier.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a96b18a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[64,  0,  0],\n",
       "       [ 0, 57,  0],\n",
       "       [ 5,  1, 15]], dtype=int64)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "y_pred = classifier.predict(X_test)\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "40179461",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy 0.9577464788732394\n"
     ]
    }
   ],
   "source": [
    "nb_score = accuracy_score(y_test, y_pred)\n",
    "print('accuracy',nb_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55ae864c",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}