Sentiment Analysis IMDB dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\iampr\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\iampr\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords\n",
    "import re\n",
    "nltk.download('wordnet')\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset-1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>type</th>\n",
       "      <th>review</th>\n",
       "      <th>label</th>\n",
       "      <th>file</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>test</td>\n",
       "      <td>Once again Mr. Costner has dragged out a movie...</td>\n",
       "      <td>neg</td>\n",
       "      <td>0_2.txt</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
       "      <td>test</td>\n",
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       "      <td>neg</td>\n",
       "      <td>10000_4.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>test</td>\n",
       "      <td>First of all I hate those moronic rappers, who...</td>\n",
       "      <td>neg</td>\n",
       "      <td>10001_1.txt</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>test</td>\n",
       "      <td>Not even the Beatles could write songs everyon...</td>\n",
       "      <td>neg</td>\n",
       "      <td>10002_3.txt</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>test</td>\n",
       "      <td>Brass pictures (movies is not a fitting word f...</td>\n",
       "      <td>neg</td>\n",
       "      <td>10003_3.txt</td>\n",
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      "text/plain": [
       "   Unnamed: 0  type                                             review label  \\\n",
       "0           0  test  Once again Mr. Costner has dragged out a movie...   neg   \n",
       "1           1  test  This is an example of why the majority of acti...   neg   \n",
       "2           2  test  First of all I hate those moronic rappers, who...   neg   \n",
       "3           3  test  Not even the Beatles could write songs everyon...   neg   \n",
       "4           4  test  Brass pictures (movies is not a fitting word f...   neg   \n",
       "\n",
       "          file  \n",
       "0      0_2.txt  \n",
       "1  10000_4.txt  \n",
       "2  10001_1.txt  \n",
       "3  10002_3.txt  \n",
       "4  10003_3.txt  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.read_csv(\"imdb_master.csv\",encoding='latin')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review</th>\n",
       "      <th>label</th>\n",
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       "      <td>Once again Mr. Costner has dragged out a movie...</td>\n",
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       "      <td>Not even the Beatles could write songs everyon...</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "                                              review  label\n",
       "0  Once again Mr. Costner has dragged out a movie...      0\n",
       "1  This is an example of why the majority of acti...      0\n",
       "2  First of all I hate those moronic rappers, who...      0\n",
       "3  Not even the Beatles could write songs everyon...      0\n",
       "4  Brass pictures (movies is not a fitting word f...      0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.drop(['Unnamed: 0','type','file'],axis=1,inplace=True)\n",
    "df1 = df1[df1.label != 'unsup']\n",
    "df1['label'] = df1['label'].map({'pos': 1, 'neg': 0})\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 25000, 1: 25000}"
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     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique, counts = np.unique(df1.label, return_counts=True)\n",
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>review</th>\n",
       "      <th>sentiment</th>\n",
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      "text/plain": [
       "                                              review  sentiment\n",
       "0  Once again Mr. Costner has dragged out a movie...          0\n",
       "1  This is an example of why the majority of acti...          0\n",
       "2  First of all I hate those moronic rappers, who...          0\n",
       "3  Not even the Beatles could write songs everyon...          0\n",
       "4  Brass pictures (movies is not a fitting word f...          0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns=['review','sentiment']\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset-2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>sentiment</th>\n",
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       "      <th>0</th>\n",
       "      <td>5814_8</td>\n",
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       "      <td>With all this stuff going down at the moment w...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2381_9</td>\n",
       "      <td>1</td>\n",
       "      <td>\\The Classic War of the Worlds\\\" by Timothy Hi...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7759_3</td>\n",
       "      <td>0</td>\n",
       "      <td>The film starts with a manager (Nicholas Bell)...</td>\n",
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       "      <th>3</th>\n",
       "      <td>3630_4</td>\n",
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       "      <td>9495_8</td>\n",
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      "text/plain": [
       "       id  sentiment                                             review\n",
       "0  5814_8          1  With all this stuff going down at the moment w...\n",
       "1  2381_9          1  \\The Classic War of the Worlds\\\" by Timothy Hi...\n",
       "2  7759_3          0  The film starts with a manager (Nicholas Bell)...\n",
       "3  3630_4          0  It must be assumed that those who praised this...\n",
       "4  9495_8          1  Superbly trashy and wondrously unpretentious 8..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('labeledTrainData.tsv', delimiter=\"\\t\")\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sentiment</th>\n",
       "      <th>review</th>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   sentiment                                             review\n",
       "0          1  With all this stuff going down at the moment w...\n",
       "1          1  \\The Classic War of the Worlds\\\" by Timothy Hi...\n",
       "2          0  The film starts with a manager (Nicholas Bell)...\n",
       "3          0  It must be assumed that those who praised this...\n",
       "4          1  Superbly trashy and wondrously unpretentious 8..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df2.drop(['id'], axis=1)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique, counts = np.unique(df2.sentiment, return_counts=True)\n",
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concatenation Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <th>review</th>\n",
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       "      <td>Once again Mr. Costner has dragged out a movie...</td>\n",
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       "      <th>3</th>\n",
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      "text/plain": [
       "                                              review  sentiment\n",
       "0  Once again Mr. Costner has dragged out a movie...          0\n",
       "1  This is an example of why the majority of acti...          0\n",
       "2  First of all I hate those moronic rappers, who...          0\n",
       "3  Not even the Beatles could write songs everyon...          0\n",
       "4  Brass pictures (movies is not a fitting word f...          0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df1, df2]).reset_index(drop=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique, counts = np.unique(df.sentiment, return_counts=True)\n",
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Preprossesing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "stop_words = set(stopwords.words(\"english\")) \n",
    "lemmatizer = WordNetLemmatizer()\n",
    "\n",
    "\n",
    "def clean_text(text):\n",
    "    text = text.lower()\n",
    "    text = text = re.sub(\"[^a-z]+\", \" \", text)\n",
    "    text = [lemmatizer.lemmatize(token) for token in text.split(\" \")]\n",
    "    text = [lemmatizer.lemmatize(token, \"v\") for token in text]\n",
    "    text = [word for word in text if not word in stop_words]\n",
    "    text = \" \".join(text)\n",
    "    return text\n",
    "\n",
    "df['Processed_Reviews'] = df.review.apply(lambda x: clean_text(x))   # df.review.map(clean_text) Also can be used\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Once again Mr. Costner has dragged out a movie...</td>\n",
       "      <td>0</td>\n",
       "      <td>mr costner ha drag movie far longer necessary ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>This is an example of why the majority of acti...</td>\n",
       "      <td>0</td>\n",
       "      <td>example majority action film generic bore real...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>First of all I hate those moronic rappers, who...</td>\n",
       "      <td>0</td>\n",
       "      <td>first hate moronic rapper could nt act gun pre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Not even the Beatles could write songs everyon...</td>\n",
       "      <td>0</td>\n",
       "      <td>even beatles could write song everyone like al...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brass pictures (movies is not a fitting word f...</td>\n",
       "      <td>0</td>\n",
       "      <td>brass picture movie fit word really somewhat b...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              review  sentiment  \\\n",
       "0  Once again Mr. Costner has dragged out a movie...          0   \n",
       "1  This is an example of why the majority of acti...          0   \n",
       "2  First of all I hate those moronic rappers, who...          0   \n",
       "3  Not even the Beatles could write songs everyon...          0   \n",
       "4  Brass pictures (movies is not a fitting word f...          0   \n",
       "\n",
       "                                   Processed_Reviews  \n",
       "0  mr costner ha drag movie far longer necessary ...  \n",
       "1  example majority action film generic bore real...  \n",
       "2  first hate moronic rapper could nt act gun pre...  \n",
       "3  even beatles could write song everyone like al...  \n",
       "4  brass picture movie fit word really somewhat b...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "from keras.layers import Dense , Input , GRU , Embedding, Dropout , Activation, Flatten\n",
    "from keras.layers import GlobalMaxPool1D\n",
    "from keras.models import Model, Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.Processed_Reviews\n",
    "y=df.sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_words = 10000\n",
    "embed_dim = 64\n",
    "tokenizer = Tokenizer(num_words=num_words,oov_token = \"<oov>\" )\n",
    "tokenizer.fit_on_texts(x)\n",
    "word_index=tokenizer.word_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequences = tokenizer.texts_to_sequences(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "75000\n",
      "Mean is:  125.32908\n",
      "Max is:  1457\n",
      "Min is:  3\n"
     ]
    }
   ],
   "source": [
    "length=[]\n",
    "for i in sequences:\n",
    "    length.append(len(i))\n",
    "print(len(length))\n",
    "print(\"Mean is: \",np.mean(length))\n",
    "print(\"Max is: \",np.max(length))\n",
    "print(\"Min is: \",np.min(length))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "pad_length = 130\n",
    "sequences = pad_sequences(sequences, maxlen = pad_length, truncating = 'pre', padding = 'post')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split as tts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(72000, 130)\n",
      "(3000, 130)\n",
      "(72000,)\n",
      "(3000,)\n"
     ]
    }
   ],
   "source": [
    "x_train, x_test, y_train, y_test = tts(sequences, y, test_size = 0.04)\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 36017, 1: 35983}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique, counts = np.unique(y_train, return_counts=True)\n",
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"IMDB Sentiment Analysis\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 130, 64)           640000    \n",
      "_________________________________________________________________\n",
      "gru_1 (GRU)                  (None, 130, 64)           24768     \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d_1 (Glob (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 65        \n",
      "=================================================================\n",
      "Total params: 668,993\n",
      "Trainable params: 668,993\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential([Embedding(num_words, embed_dim, input_length = pad_length),\n",
    "                   GRU(64, return_sequences = True),\n",
    "                   GlobalMaxPool1D(),\n",
    "                   Dense(64,activation = 'relu',kernel_initializer='he_uniform'),\n",
    "                   Dropout(0.25),\n",
    "                   Dense(1,activation = 'sigmoid')])\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "model.name = 'IMDB Sentiment Analysis'\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\iampr\\anaconda3\\envs\\tensorflow\\lib\\site-packages\\tensorflow_core\\python\\framework\\indexed_slices.py:433: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 69120 samples, validate on 2880 samples\n",
      "Epoch 1/5\n",
      "69120/69120 [==============================] - 347s 5ms/step - loss: 0.3175 - accuracy: 0.8640 - val_loss: 0.2416 - val_accuracy: 0.9017\n",
      "Epoch 2/5\n",
      "69120/69120 [==============================] - 342s 5ms/step - loss: 0.1826 - accuracy: 0.9329 - val_loss: 0.2117 - val_accuracy: 0.9187\n",
      "Epoch 3/5\n",
      "69120/69120 [==============================] - 339s 5ms/step - loss: 0.1145 - accuracy: 0.9614 - val_loss: 0.1836 - val_accuracy: 0.9389\n",
      "Epoch 4/5\n",
      "69120/69120 [==============================] - 339s 5ms/step - loss: 0.0609 - accuracy: 0.9809 - val_loss: 0.1892 - val_accuracy: 0.9410\n",
      "Epoch 5/5\n",
      "69120/69120 [==============================] - 215s 3ms/step - loss: 0.0321 - accuracy: 0.9908 - val_loss: 0.2232 - val_accuracy: 0.9438\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x= x_train, y= y_train, batch_size= 64, epochs=5, validation_split = 0.04)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3000/3000 [==============================] - 3s 900us/step\n",
      "Test Accuracy is : 93.13\n"
     ]
    }
   ],
   "source": [
    "test_stats = model.evaluate(x_test,y_test)\n",
    "print(\"Test Accuracy is : {:.2f}\".format(test_stats[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_acc = history.history['accuracy']\n",
    "valid_acc = history.history['val_accuracy']\n",
    "train_loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26fbbd16188>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10,10))\n",
    "fig.add_subplot(2, 1, 1)\n",
    "plt.title('Loss')\n",
    "plt.plot(train_loss, color='blue', label='Train')\n",
    "plt.plot(val_loss, color='red', label='Test')\n",
    "plt.legend()\n",
    "# plot accuracy\n",
    "fig.add_subplot(2, 1, 2)\n",
    "plt.title('Classification Accuracy')\n",
    "plt.plot(train_acc, color='blue', label='Train')\n",
    "plt.plot(valid_acc, color='red', label='Test')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('IMDBSentimentAnalysis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "tensorflow",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}