Text Mining：Amazon unlocked mobile review .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using review to predict Rating\n",
    "In this notebook, I will try to use the reviews in the \"Amazon_Unlocked_Mobile.csv\" to predict the Ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Amazon_Unlocked_Mobile.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Product Name</th>\n",
       "      <th>Brand Name</th>\n",
       "      <th>Price</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Review Votes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>\"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7...</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>199.99</td>\n",
       "      <td>5</td>\n",
       "      <td>I feel so LUCKY to have found this used (phone...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>\"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7...</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>199.99</td>\n",
       "      <td>4</td>\n",
       "      <td>nice phone, nice up grade from my pantach revu...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>\"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7...</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>199.99</td>\n",
       "      <td>5</td>\n",
       "      <td>Very pleased</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7...</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>199.99</td>\n",
       "      <td>4</td>\n",
       "      <td>It works good but it goes slow sometimes but i...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7...</td>\n",
       "      <td>Samsung</td>\n",
       "      <td>199.99</td>\n",
       "      <td>4</td>\n",
       "      <td>Great phone to replace my lost phone. The only...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Product Name Brand Name   Price  \\\n",
       "0  \"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7...    Samsung  199.99   \n",
       "1  \"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7...    Samsung  199.99   \n",
       "2  \"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7...    Samsung  199.99   \n",
       "3  \"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7...    Samsung  199.99   \n",
       "4  \"CLEAR CLEAN ESN\" Sprint EPIC 4G Galaxy SPH-D7...    Samsung  199.99   \n",
       "\n",
       "   Rating                                            Reviews  Review Votes  \n",
       "0       5  I feel so LUCKY to have found this used (phone...           1.0  \n",
       "1       4  nice phone, nice up grade from my pantach revu...           0.0  \n",
       "2       5                                       Very pleased           0.0  \n",
       "3       4  It works good but it goes slow sometimes but i...           0.0  \n",
       "4       4  Great phone to replace my lost phone. The only...           0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "413840"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.dropna(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "334335"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "308277"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#regard rating 3 as neutral and drop it\n",
    "df=df[df['Rating']!= 3]\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Positive Review']= df['Rating']>3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Positive Review'] = df['Positive Review'].apply(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['Reviews']\n",
    "y = df['Positive Review']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7482686025879323"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use CountVectorizer and LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "vect = CountVectorizer().fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_vectorized = vect.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score:  0.940570489366771\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import roc_auc_score\n",
    "lr_clf = LogisticRegression()\n",
    "lr_clf.fit(X_train_vectorized,y_train)\n",
    "y_pred = lr_clf.predict(vect.transform(X_test))\n",
    "print(\"AUC score: \", roc_auc_score(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_clf.predict(vect.transform(['good','not good','bad','not bad']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "we can see that our model will ignore 'not', so maybe we can use 2-gram to improve the performance of our model\n",
    "# Use 2-gram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score using 2-gram : 0.9650655031770067\n"
     ]
    }
   ],
   "source": [
    "lr_clf1 = LogisticRegression()\n",
    "vect = CountVectorizer(ngram_range=(1,2)).fit(X_train)\n",
    "X_train_vect = vect.transform(X_train)\n",
    "lr_clf1.fit(X_train_vect,y_train)\n",
    "y_pred1= lr_clf1.predict(vect.transform(X_test))\n",
    "print(\"AUC score using 2-gram :\", roc_auc_score(y_test,y_pred1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 0, 1], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_clf.predict(vect.transform(['good','not good','bad','not bad']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Can  we use the min_df para in CountVectorizer to improve the performance?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "vect = CountVectorizer(ngram_range=(1,2),min_df = 5).fit(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score using 2-gram and min_df =5 : 0.964181812584712\n"
     ]
    }
   ],
   "source": [
    "X_train_vect2 = vect.transform(X_train)\n",
    "lr_clf.fit(X_train_vect2,y_train)\n",
    "y_pred = lr_clf.predict(vect.transform(X_test))\n",
    "print(\"AUC score using 2-gram and min_df =5 :\", roc_auc_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score using 2-gram and min_df =5 : 0.9866816810439873\n"
     ]
    }
   ],
   "source": [
    "print(\"AUC score using 2-gram and min_df =5 :\", roc_auc_score(y_train,lr_clf.predict(X_train_vect2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Use TFIDF to Do Feature Extraction "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score using 2-gram and min_df =5 (TFIDF): 0.9488645714453975\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "lr_clf2 = LogisticRegression()\n",
    "vect1 = TfidfVectorizer(ngram_range=(1,2),min_df= 5)\n",
    "X_train_tfidf = vect1.fit_transform(X_train)\n",
    "lr_clf2.fit(X_train_tfidf,y_train)\n",
    "y_pred2 = lr_clf2.predict(vect1.transform(X_test))\n",
    "print(\"AUC score using 2-gram and min_df =5 (TFIDF):\", roc_auc_score(y_test,y_pred2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combine CountVectorizer and TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scipy.sparse.csr.csr_matrix"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X_train_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.sparse import hstack\n",
    "X_combined = hstack((vect.transform(X_train),vect1.transform(X_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score using combined feature: 0.9705983578252696\n"
     ]
    }
   ],
   "source": [
    "lr_clf3 = LogisticRegression()\n",
    "lr_clf3.fit(X_combined,y_train)\n",
    "X_test_combined = hstack((vect.transform(X_test),vect1.transform(X_test)))\n",
    "y_pred3 = lr_clf3.predict(X_test_combined)\n",
    "print(\"AUC score using combined feature:\", roc_auc_score(y_test,y_pred3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ensemble All Three Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y_pred1+y_pred2+y_pred3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_voting = [int(x>1) for x in y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC score using voting classifer: 0.9673473488657627\n"
     ]
    }
   ],
   "source": [
    "print(\"AUC score using voting classifer:\", roc_auc_score(y_test,y_voting))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# the combined feature classifier outperforms other classifies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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