imdb movie reviews.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8459e2cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#first import necessary packages\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from collections import Counter\n",
    "import warnings\n",
    "import nltk\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.metrics import accuracy_score, f1_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "\n",
    "stopwords= nltk.corpus.stopwords.words('english')\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4c3990ac",
   "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",
       "  </tbody>\n",
       "</table>\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"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#read the dataset\n",
    "data = pd.read_csv('imdbds.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2020b02",
   "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": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "49a760b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                   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\n"
     ]
    }
   ],
   "source": [
    "print(data.describe())\n",
    "X=data['review']\n",
    "y=data['sentiment']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d9a196f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_special_characters(text):\n",
    "    \"\"\"Function to parse raw review data and extract\n",
    "        text from it.\n",
    "    \"\"\"\n",
    "    soup = BeautifulSoup(text, \"html.parser\")\n",
    "    text = soup.get_text()\n",
    "    text = re.sub('\\[[^]]*\\]', '', text)\n",
    "    text = re.sub('[^a-zA-z0-9\\s+\\,]','',text)\n",
    "    text = re.sub('\\.{2,}','',text)\n",
    "    return text\n",
    "\n",
    "def remove_stopwords(text):\n",
    "    \"\"\"Function to remove stopwords\"\"\"\n",
    "    text = text.lower()\n",
    "    words = text.split(\" \")\n",
    "    filtered_words = [i for i in words if i not in stopwords]\n",
    "    filtered_words = ' '.join(filtered_words)\n",
    "    return filtered_words\n",
    "\n",
    "class BuildVocab:\n",
    "    \"\"\"Class to build vocabulary for a given series of reviews and vocab size\"\"\"\n",
    "    def __init__(self, sentences: pd.Series, vocab_size:int, unk_token=False):\n",
    "        self.sentences = sentences\n",
    "        self.vocab_size = vocab_size\n",
    "        self.oov_token = unk_token\n",
    "    \n",
    "    def __process_sentences(self, sentence:str):\n",
    "        sentence = re.sub(r'[^a-zA-Z\\s+]', '', sentence).strip()\n",
    "        return sentence\n",
    "    \n",
    "    def __sentence2words(self, sentence: str):\n",
    "        word_list = list()\n",
    "        for word in sentence.split(\" \"):\n",
    "            if len(word) >= 1:\n",
    "                word_list.append(word)\n",
    "            else:\n",
    "                pass\n",
    "        return list(word_list)\n",
    "    \n",
    "    def __get_word_dictionary(self, sentences:list):\n",
    "        words = list()\n",
    "        for sentence in sentences:\n",
    "            word_list = self.__sentence2words(sentence=sentence)\n",
    "            words.extend(word_list)\n",
    "        return list(words)\n",
    "    \n",
    "    def __get_topn_words(self, word_dictionary:dict):\n",
    "        sorted_items = sorted(word_dictionary.items(), key=lambda item: item[1], reverse=True)\n",
    "        top_n_words = [word for word, _ in sorted_items[:self.vocab_size]]\n",
    "        return top_n_words\n",
    "\n",
    "\n",
    "    def build(self):\n",
    "        sentences = self.sentences.apply(self.__process_sentences)\n",
    "        words = self.__get_word_dictionary(sentences=sentences.values)\n",
    "        word_count_dict = Counter(words)\n",
    "        vocab = self.__get_topn_words(word_dictionary=word_count_dict)\n",
    "        \n",
    "        if self.oov_token:\n",
    "            vocab.append(\"UNK\")\n",
    "        else:\n",
    "            pass\n",
    "\n",
    "        index_dict = {x: index for x, index in zip(vocab, range(len(vocab)))}\n",
    "\n",
    "        return vocab, index_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d4cf2ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of reviews: 50000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='sentiment', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Number of reviews: {len(X.values)}\")\n",
    "sns.countplot(data=data, x=\"sentiment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "305ec977",
   "metadata": {},
   "outputs": [],
   "source": [
    "#cleaning and removing stopwords from reviews\n",
    "X = X.apply(remove_special_characters)\n",
    "X = X.apply(remove_stopwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f23bb612",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Building vocabulary with desired vocab size\n",
    "vocab, vocab_dict = BuildVocab(sentences=X, vocab_size=1000, unk_token=True).build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3b2bf0fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "#splitting dataset into 75% train and 25% test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, \n",
    "                                                    random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "11f4ad50",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf = TfidfVectorizer(max_features = 500)\n",
    "tfidf = tfidf.fit(X_train)\n",
    "X_train, X_test = tfidf.transform(X_train), tfidf.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "10eb09c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(37500, 1) (12500, 1)\n"
     ]
    }
   ],
   "source": [
    "encoder = LabelBinarizer()\n",
    "encoder = encoder.fit(y_train)\n",
    "y_train,y_test = encoder.transform(y_train), encoder.transform(y_test)\n",
    "print(y_train.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "49557066",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(penalty=\"l2\", max_iter=500, C=1, random_state=42)\n",
    "lr = lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0e411637",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training accuracy: 0.8459466666666666\n",
      "Testing accuracy: 0.83248\n",
      "F1 score(train): 0.8477051643687555\n",
      "F1 Score(test): 0.8331208160663054\n"
     ]
    }
   ],
   "source": [
    "#checking accuracy\n",
    "train_pred = lr.predict(X_train)\n",
    "test_pred = lr. predict(X_test)\n",
    "train_acc = accuracy_score(y_train,train_pred)\n",
    "print(\"Training accuracy: {}\".format(train_acc))\n",
    "test_acc = accuracy_score(y_test, test_pred)\n",
    "print(\"Testing accuracy: {}\".format(test_acc))\n",
    "f1train = f1_score(y_train, train_pred)\n",
    "print(\"F1 score(train): {}\".format(f1train))\n",
    "f1test = f1_score(y_test, test_pred)\n",
    "print(\"F1 Score(test): {}\".format(f1test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ad1cef07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.84      0.82      0.83      6291\n",
      "           1       0.82      0.84      0.83      6209\n",
      "\n",
      "    accuracy                           0.83     12500\n",
      "   macro avg       0.83      0.83      0.83     12500\n",
      "weighted avg       0.83      0.83      0.83     12500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b7aede09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test,test_pred)\n",
    "sns.heatmap(cm, fmt='d', annot=True)"
   ]
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}