NLP Classifying Positive and Negative Restaurant Reviews (Bag of Words model)..ipynb

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   "outputs": [],
   "source": [
    "# Importing Libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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",
       "      <th>0</th>\n",
       "      <td>Wow... Loved this place.</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Crust is not good.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Not tasty and the texture was just nasty.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stopped by during the late May bank holiday of...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>The selection on the menu was great and so wer...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>I think food should have flavor and texture an...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>Appetite instantly gone.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>Overall I was not impressed and would not go b...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>The whole experience was underwhelming, and I ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Then, as if I hadn't wasted enough of my life ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\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\n",
       "..                                                 ...    ...\n",
       "995  I think food should have flavor and texture an...      0\n",
       "996                           Appetite instantly gone.      0\n",
       "997  Overall I was not impressed and would not go b...      0\n",
       "998  The whole experience was underwhelming, and I ...      0\n",
       "999  Then, as if I hadn't wasted enough of my life ...      0\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing dataset\n",
    "# quoting = 3 is for ignoring \"\" for our safety.\n",
    "dataset = pd.read_csv('Restaurant_Reviews.tsv', delimiter='\\t',quoting = 3)\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaning the text\n",
    "# stopwords is a list of unwanted words like the,and,of,etc...\n",
    "# corpus is a collection of text.\n",
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stemming means taking the root of the word eg. loved, loving, will love -> love\n",
    "# This will reduce different versions of the same word and will hence reduce the sparsity of matrix\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "corpus = []\n",
    "for i in range (0, 1000):\n",
    "    # Removing unnecessary punctuations and numbers except letters and replacing removed words with space.\n",
    "    review = re.sub('[^a-zA-Z]', ' ', dataset['Review'][i])\n",
    "    # Converting review to lowercase\n",
    "    review = review.lower()\n",
    "    # Converting review to list(of strings)\n",
    "    review = review.split()\n",
    "    # Loop through all words and keep those which are not in stopwords list.\n",
    "    # set is much faster than a list and is considered when the review is very large eg. an article,a book\n",
    "    ps = PorterStemmer()\n",
    "    review = [ps.stem(word) for word in review if not word in set(stopwords.words('english'))]\n",
    "    # Joining back the review list to a string with each word seperated by a space.\n",
    "    review = ' '.join(review)\n",
    "    corpus.append(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the Bag of Words Model\n",
    "# Bag of Words Model is a sparse matrix where each row is the review and each column is a unique \n",
    "# word from the reviews.\n",
    "# Tokenization - process of taking all unique words of reviews and creating columns for each word.\n",
    "# Since this a problem of classification we have dependent and independent variables and each \n",
    "# unique word/column is like an independent variable and the review(good/bad) depends on these words.\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0,\n",
       "       1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0,\n",
       "       1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0,\n",
       "       0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1,\n",
       "       0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# max_features keeps most frequent words and removes least frequent words (extra cleaning)\n",
    "# max_feature reduces sparsity, increases precision, better learning and hence better prediction.\n",
    "cv = CountVectorizer(max_features = 1500)\n",
    "X = cv.fit_transform(corpus).toarray()\n",
    "y = dataset.iloc[:, 1].values\n",
    "\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)\n",
    "\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "classifier = GaussianNB()\n",
    "classifier.fit(X_train, y_train)\n",
    "\n",
    "y_pred = classifier.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[55, 42],\n",
       "       [12, 91]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making the Confusion Matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "55+42+12+91"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.73"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overall TP and TN Accuracy\n",
    "(55+91)/200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overall FP and FN Accuracy\n",
    "(42+12)/200"
   ]
  }
 ],
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