14. NLP -Sentiment Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter = '\\t', quoting = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Liked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Wow... Loved this place.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Crust is not good.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Not tasty and the texture was just nasty.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Stopped by during the late May bank holiday of...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>The selection on the menu was great and so wer...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Review  Liked\n",
       "0                           Wow... Loved this place.      1\n",
       "1                                 Crust is not good.      0\n",
       "2          Not tasty and the texture was just nasty.      0\n",
       "3  Stopped by during the late May bank holiday of...      1\n",
       "4  The selection on the menu was great and so wer...      1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /home/sudharsan/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()\n",
    "data=[]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for i in range(0,1000):\n",
    "        review=dataset[\"Review\"][i]\n",
    "        review = re.sub('[^a-zA-Z]', ' ', review)\n",
    "        review = review.lower()\n",
    "        review = review.split()\n",
    "        review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]\n",
    "        review = ' '.join(review)\n",
    "        data.append(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['wow love place',\n",
       " 'crust good',\n",
       " 'tasti textur nasti',\n",
       " 'stop late may bank holiday rick steve recommend love',\n",
       " 'select menu great price',\n",
       " 'get angri want damn pho',\n",
       " 'honeslti tast fresh',\n",
       " 'potato like rubber could tell made ahead time kept warmer']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "        \n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "cv = CountVectorizer(max_features=1500)\n",
    "X = cv.fit_transform(data).toarray()\n",
    "y=dataset.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\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 = 0.20, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using Theano backend.\n",
      "WARNING (theano.tensor.blas): Using NumPy C-API based implementation for BLAS functions.\n",
      "/home/sudharsan/anaconda3/envs/universe/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/home/sudharsan/anaconda3/envs/universe/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/home/sudharsan/anaconda3/envs/universe/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/home/sudharsan/anaconda3/envs/universe/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/home/sudharsan/anaconda3/envs/universe/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/home/sudharsan/anaconda3/envs/universe/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sudharsan/anaconda3/envs/universe/lib/python3.6/site-packages/ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", input_dim=1500, units=6, kernel_initializer=\"uniform\")`\n",
      "  \"\"\"\n",
      "/home/sudharsan/anaconda3/envs/universe/lib/python3.6/site-packages/ipykernel_launcher.py:7: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=6, kernel_initializer=\"uniform\")`\n",
      "  import sys\n",
      "/home/sudharsan/anaconda3/envs/universe/lib/python3.6/site-packages/ipykernel_launcher.py:9: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"sigmoid\", units=1, kernel_initializer=\"uniform\")`\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "\n",
    "\n",
    "model.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu', input_dim = 1500))\n",
    "\n",
    "model.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu'))\n",
    "\n",
    "model.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "800/800 [==============================] - 0s 135us/step - loss: 0.6932 - accuracy: 0.4850\n",
      "Epoch 2/25\n",
      "800/800 [==============================] - 0s 102us/step - loss: 0.6931 - accuracy: 0.4963\n",
      "Epoch 3/25\n",
      "800/800 [==============================] - 0s 92us/step - loss: 0.6923 - accuracy: 0.7600\n",
      "Epoch 4/25\n",
      "800/800 [==============================] - 0s 194us/step - loss: 0.6885 - accuracy: 0.7887\n",
      "Epoch 5/25\n",
      "800/800 [==============================] - 0s 144us/step - loss: 0.6773 - accuracy: 0.8350\n",
      "Epoch 6/25\n",
      "800/800 [==============================] - 0s 48us/step - loss: 0.6542 - accuracy: 0.8250\n",
      "Epoch 7/25\n",
      "800/800 [==============================] - 0s 60us/step - loss: 0.6155 - accuracy: 0.8537\n",
      "Epoch 8/25\n",
      "800/800 [==============================] - 0s 64us/step - loss: 0.5640 - accuracy: 0.8725\n",
      "Epoch 9/25\n",
      "800/800 [==============================] - 0s 60us/step - loss: 0.5062 - accuracy: 0.8950\n",
      "Epoch 10/25\n",
      "800/800 [==============================] - 0s 63us/step - loss: 0.4496 - accuracy: 0.9062\n",
      "Epoch 11/25\n",
      "800/800 [==============================] - 0s 60us/step - loss: 0.3974 - accuracy: 0.9275\n",
      "Epoch 12/25\n",
      "800/800 [==============================] - 0s 60us/step - loss: 0.3506 - accuracy: 0.9362\n",
      "Epoch 13/25\n",
      "800/800 [==============================] - 0s 60us/step - loss: 0.3086 - accuracy: 0.9388\n",
      "Epoch 14/25\n",
      "800/800 [==============================] - 0s 64us/step - loss: 0.2710 - accuracy: 0.9513\n",
      "Epoch 15/25\n",
      "800/800 [==============================] - 0s 61us/step - loss: 0.2372 - accuracy: 0.9588\n",
      "Epoch 16/25\n",
      "800/800 [==============================] - 0s 83us/step - loss: 0.2083 - accuracy: 0.9712\n",
      "Epoch 17/25\n",
      "800/800 [==============================] - 0s 56us/step - loss: 0.1834 - accuracy: 0.9712\n",
      "Epoch 18/25\n",
      "800/800 [==============================] - 0s 60us/step - loss: 0.1628 - accuracy: 0.9725\n",
      "Epoch 19/25\n",
      "800/800 [==============================] - 0s 63us/step - loss: 0.1447 - accuracy: 0.9775\n",
      "Epoch 20/25\n",
      "800/800 [==============================] - 0s 63us/step - loss: 0.1300 - accuracy: 0.9787\n",
      "Epoch 21/25\n",
      "800/800 [==============================] - 0s 107us/step - loss: 0.1174 - accuracy: 0.9812\n",
      "Epoch 22/25\n",
      "800/800 [==============================] - 0s 65us/step - loss: 0.1071 - accuracy: 0.9825\n",
      "Epoch 23/25\n",
      "800/800 [==============================] - 0s 102us/step - loss: 0.0977 - accuracy: 0.9837\n",
      "Epoch 24/25\n",
      "800/800 [==============================] - 0s 177us/step - loss: 0.0901 - accuracy: 0.9837\n",
      "Epoch 25/25\n",
      "800/800 [==============================] - 0s 74us/step - loss: 0.0836 - accuracy: 0.9825\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.callbacks.History at 0x7fcc986c4128>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train,batch_size=32,epochs=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(X_test)\n",
    "\n",
    "y_pred=y_pred>0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "r=model.predict(cv.transform([\"bad food\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r=r>0.5\n",
    "\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
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   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  }
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