Hotel review sentiment analysis.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2><B><left>Hello, This Notebook Contains Example of Hotel Reviews Sentiment Analysis.</left></B></h2>\n",
    "<h4><left>- Classes is: Happy, notHappy. </left></h4>\n",
    "<h4><I><left>- We use some Regular-Expressions for data cleaning.</left></I></h4>\n",
    "<h4><I><left>- Vectorizing texts using TF-IDF vectorizer.</left></I></h4>\n",
    "<h4><I><left>- Train Logistic Regression Classifier using Scikit-Learn Library</left></I></h4>\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
    "    <ul>\n",
    "            <li><h3>Contents:</h3></li>\n",
    "   <ul>\n",
    "        <li><h4><a href=\"#explor\">Data Exploratory.</a></h4></li>\n",
    "        <li><h4><a href=\"#clean\">Cleaning.</a></h4></li>\n",
    "        <li><h4><a href=\"#model\">Modeling.</a></h4></li>\n",
    "       <li><h4><a href=\"#result\">Results.</a></h4></li>\n",
    "    </ul>\n",
    "    </ul>\n",
    "</div>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#warnings :)\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import os\n",
    "dir_Path = 'F:\\\\'\n",
    "os.chdir(dir_Path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"explor\"></a>\n",
    "<h3><B><left>- Data Exploratory: </left></B></h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "Reviewdata = pd.read_csv('train.csv')\n",
    "#Data Link - https://www.kaggle.com/anu0012/hotel-review/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38932, 5)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Reviewdata.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>User_ID</th>\n",
       "      <th>Description</th>\n",
       "      <th>Browser_Used</th>\n",
       "      <th>Device_Used</th>\n",
       "      <th>Is_Response</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>id10326</td>\n",
       "      <td>The room was kind of clean but had a VERY stro...</td>\n",
       "      <td>Edge</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>not happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id10327</td>\n",
       "      <td>I stayed at the Crown Plaza April -- - April -...</td>\n",
       "      <td>Internet Explorer</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>not happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>id10328</td>\n",
       "      <td>I booked this hotel through Hotwire at the low...</td>\n",
       "      <td>Mozilla</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>not happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>id10329</td>\n",
       "      <td>Stayed here with husband and sons on the way t...</td>\n",
       "      <td>InternetExplorer</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>happy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>id10330</td>\n",
       "      <td>My girlfriends and I stayed here to celebrate ...</td>\n",
       "      <td>Edge</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>not happy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   User_ID                                        Description  \\\n",
       "0  id10326  The room was kind of clean but had a VERY stro...   \n",
       "1  id10327  I stayed at the Crown Plaza April -- - April -...   \n",
       "2  id10328  I booked this hotel through Hotwire at the low...   \n",
       "3  id10329  Stayed here with husband and sons on the way t...   \n",
       "4  id10330  My girlfriends and I stayed here to celebrate ...   \n",
       "\n",
       "        Browser_Used Device_Used Is_Response  \n",
       "0               Edge      Mobile   not happy  \n",
       "1  Internet Explorer      Mobile   not happy  \n",
       "2            Mozilla      Tablet   not happy  \n",
       "3   InternetExplorer     Desktop       happy  \n",
       "4               Edge      Tablet   not happy  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Reviewdata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 38932 entries, 0 to 38931\n",
      "Data columns (total 5 columns):\n",
      "User_ID         38932 non-null object\n",
      "Description     38932 non-null object\n",
      "Browser_Used    38932 non-null object\n",
      "Device_Used     38932 non-null object\n",
      "Is_Response     38932 non-null object\n",
      "dtypes: object(5)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "Reviewdata.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>User_ID</th>\n",
       "      <td>38932</td>\n",
       "      <td>38932</td>\n",
       "      <td>id41139</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Description</th>\n",
       "      <td>38932</td>\n",
       "      <td>38932</td>\n",
       "      <td>I am a Hilton Diamond member and have stayed a...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Browser_Used</th>\n",
       "      <td>38932</td>\n",
       "      <td>11</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>7367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Device_Used</th>\n",
       "      <td>38932</td>\n",
       "      <td>3</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>15026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Is_Response</th>\n",
       "      <td>38932</td>\n",
       "      <td>2</td>\n",
       "      <td>happy</td>\n",
       "      <td>26521</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count unique                                                top  \\\n",
       "User_ID       38932  38932                                            id41139   \n",
       "Description   38932  38932  I am a Hilton Diamond member and have stayed a...   \n",
       "Browser_Used  38932     11                                            Firefox   \n",
       "Device_Used   38932      3                                            Desktop   \n",
       "Is_Response   38932      2                                              happy   \n",
       "\n",
       "               freq  \n",
       "User_ID           1  \n",
       "Description       1  \n",
       "Browser_Used   7367  \n",
       "Device_Used   15026  \n",
       "Is_Response   26521  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Reviewdata.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"clean\"></a>\n",
    "<h3><B><left>- Data Cleaning: </left></B></h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Count and percentage of missing values for the columns:\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Is_Response</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Device_Used</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Browser_Used</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Description</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>User_ID</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "              Count  Percentage\n",
       "Is_Response       0         0.0\n",
       "Device_Used       0         0.0\n",
       "Browser_Used      0         0.0\n",
       "Description       0         0.0\n",
       "User_ID           0         0.0"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking Missing values in the Data and printing the Percentage for Missing Values in Each Columns.\n",
    "\n",
    "count = Reviewdata.isnull().sum().sort_values(ascending=False)\n",
    "percentage = ((Reviewdata.isnull().sum()/len(Reviewdata)*100)).sort_values(ascending=False)\n",
    "missing_data = pd.concat([count, percentage], axis=1,\n",
    "keys=['Count','Percentage'])\n",
    "\n",
    "print('Count and percentage of missing values for the columns:')\n",
    "\n",
    "missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage for default\n",
      "\n",
      "happy        68.12\n",
      "not happy    31.88\n",
      "Name: Is_Response, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking for the Distribution of Default #\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "print('Percentage for default\\n')\n",
    "print(round(Reviewdata.Is_Response.value_counts(normalize=True)*100,2))\n",
    "round(Reviewdata.Is_Response.value_counts(normalize=True)*100,2).plot(kind='bar')\n",
    "plt.title('Percentage Distributions by review type')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing unwanted columns\n",
    "Reviewdata.drop(columns = ['User_ID', 'Browser_Used', 'Device_Used'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply first level cleaning\n",
    "import re\n",
    "import string\n",
    "\n",
    "#This function converts to lower-case, removes square bracket, removes numbers and punctuation\n",
    "def text_clean_1(text):\n",
    "    text = text.lower()\n",
    "    text = re.sub('\\[.*?\\]', '', text)\n",
    "    text = re.sub('[%s]' % re.escape(string.punctuation), '', text)\n",
    "    text = re.sub('\\w*\\d\\w*', '', text)\n",
    "    return text\n",
    "\n",
    "cleaned1 = lambda x: text_clean_1(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>Description</th>\n",
       "      <th>Is_Response</th>\n",
       "      <th>cleaned_description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The room was kind of clean but had a VERY stro...</td>\n",
       "      <td>not happy</td>\n",
       "      <td>the room was kind of clean but had a very stro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I stayed at the Crown Plaza April -- - April -...</td>\n",
       "      <td>not happy</td>\n",
       "      <td>i stayed at the crown plaza april   april   th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I booked this hotel through Hotwire at the low...</td>\n",
       "      <td>not happy</td>\n",
       "      <td>i booked this hotel through hotwire at the low...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stayed here with husband and sons on the way t...</td>\n",
       "      <td>happy</td>\n",
       "      <td>stayed here with husband and sons on the way t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>My girlfriends and I stayed here to celebrate ...</td>\n",
       "      <td>not happy</td>\n",
       "      <td>my girlfriends and i stayed here to celebrate ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>We had - rooms. One was very nice and clearly ...</td>\n",
       "      <td>happy</td>\n",
       "      <td>we had  rooms one was very nice and clearly ha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>My husband and I have stayed in this hotel a f...</td>\n",
       "      <td>not happy</td>\n",
       "      <td>my husband and i have stayed in this hotel a f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>My wife &amp; I stayed in this glorious city a whi...</td>\n",
       "      <td>happy</td>\n",
       "      <td>my wife  i stayed in this glorious city a whil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>My boyfriend and I stayed at the Fairmont on a...</td>\n",
       "      <td>happy</td>\n",
       "      <td>my boyfriend and i stayed at the fairmont on a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Wonderful staff, great location, but it was de...</td>\n",
       "      <td>not happy</td>\n",
       "      <td>wonderful staff great location but it was defi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Description Is_Response  \\\n",
       "0  The room was kind of clean but had a VERY stro...   not happy   \n",
       "1  I stayed at the Crown Plaza April -- - April -...   not happy   \n",
       "2  I booked this hotel through Hotwire at the low...   not happy   \n",
       "3  Stayed here with husband and sons on the way t...       happy   \n",
       "4  My girlfriends and I stayed here to celebrate ...   not happy   \n",
       "5  We had - rooms. One was very nice and clearly ...       happy   \n",
       "6  My husband and I have stayed in this hotel a f...   not happy   \n",
       "7  My wife & I stayed in this glorious city a whi...       happy   \n",
       "8  My boyfriend and I stayed at the Fairmont on a...       happy   \n",
       "9  Wonderful staff, great location, but it was de...   not happy   \n",
       "\n",
       "                                 cleaned_description  \n",
       "0  the room was kind of clean but had a very stro...  \n",
       "1  i stayed at the crown plaza april   april   th...  \n",
       "2  i booked this hotel through hotwire at the low...  \n",
       "3  stayed here with husband and sons on the way t...  \n",
       "4  my girlfriends and i stayed here to celebrate ...  \n",
       "5  we had  rooms one was very nice and clearly ha...  \n",
       "6  my husband and i have stayed in this hotel a f...  \n",
       "7  my wife  i stayed in this glorious city a whil...  \n",
       "8  my boyfriend and i stayed at the fairmont on a...  \n",
       "9  wonderful staff great location but it was defi...  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the cleaned text\n",
    "Reviewdata['cleaned_description'] = pd.DataFrame(Reviewdata.Description.apply(cleaned1))\n",
    "Reviewdata.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply a second round of cleaning\n",
    "def text_clean_2(text):\n",
    "    text = re.sub('[‘’“”…]', '', text)\n",
    "    text = re.sub('\\n', '', text)\n",
    "    return text\n",
    "\n",
    "cleaned2 = lambda x: text_clean_2(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>Description</th>\n",
       "      <th>Is_Response</th>\n",
       "      <th>cleaned_description</th>\n",
       "      <th>cleaned_description_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The room was kind of clean but had a VERY stro...</td>\n",
       "      <td>not happy</td>\n",
       "      <td>the room was kind of clean but had a very stro...</td>\n",
       "      <td>the room was kind of clean but had a very stro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I stayed at the Crown Plaza April -- - April -...</td>\n",
       "      <td>not happy</td>\n",
       "      <td>i stayed at the crown plaza april   april   th...</td>\n",
       "      <td>i stayed at the crown plaza april   april   th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I booked this hotel through Hotwire at the low...</td>\n",
       "      <td>not happy</td>\n",
       "      <td>i booked this hotel through hotwire at the low...</td>\n",
       "      <td>i booked this hotel through hotwire at the low...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Stayed here with husband and sons on the way t...</td>\n",
       "      <td>happy</td>\n",
       "      <td>stayed here with husband and sons on the way t...</td>\n",
       "      <td>stayed here with husband and sons on the way t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>My girlfriends and I stayed here to celebrate ...</td>\n",
       "      <td>not happy</td>\n",
       "      <td>my girlfriends and i stayed here to celebrate ...</td>\n",
       "      <td>my girlfriends and i stayed here to celebrate ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>We had - rooms. One was very nice and clearly ...</td>\n",
       "      <td>happy</td>\n",
       "      <td>we had  rooms one was very nice and clearly ha...</td>\n",
       "      <td>we had  rooms one was very nice and clearly ha...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>My husband and I have stayed in this hotel a f...</td>\n",
       "      <td>not happy</td>\n",
       "      <td>my husband and i have stayed in this hotel a f...</td>\n",
       "      <td>my husband and i have stayed in this hotel a f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>My wife &amp; I stayed in this glorious city a whi...</td>\n",
       "      <td>happy</td>\n",
       "      <td>my wife  i stayed in this glorious city a whil...</td>\n",
       "      <td>my wife  i stayed in this glorious city a whil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>My boyfriend and I stayed at the Fairmont on a...</td>\n",
       "      <td>happy</td>\n",
       "      <td>my boyfriend and i stayed at the fairmont on a...</td>\n",
       "      <td>my boyfriend and i stayed at the fairmont on a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Wonderful staff, great location, but it was de...</td>\n",
       "      <td>not happy</td>\n",
       "      <td>wonderful staff great location but it was defi...</td>\n",
       "      <td>wonderful staff great location but it was defi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Description Is_Response  \\\n",
       "0  The room was kind of clean but had a VERY stro...   not happy   \n",
       "1  I stayed at the Crown Plaza April -- - April -...   not happy   \n",
       "2  I booked this hotel through Hotwire at the low...   not happy   \n",
       "3  Stayed here with husband and sons on the way t...       happy   \n",
       "4  My girlfriends and I stayed here to celebrate ...   not happy   \n",
       "5  We had - rooms. One was very nice and clearly ...       happy   \n",
       "6  My husband and I have stayed in this hotel a f...   not happy   \n",
       "7  My wife & I stayed in this glorious city a whi...       happy   \n",
       "8  My boyfriend and I stayed at the Fairmont on a...       happy   \n",
       "9  Wonderful staff, great location, but it was de...   not happy   \n",
       "\n",
       "                                 cleaned_description  \\\n",
       "0  the room was kind of clean but had a very stro...   \n",
       "1  i stayed at the crown plaza april   april   th...   \n",
       "2  i booked this hotel through hotwire at the low...   \n",
       "3  stayed here with husband and sons on the way t...   \n",
       "4  my girlfriends and i stayed here to celebrate ...   \n",
       "5  we had  rooms one was very nice and clearly ha...   \n",
       "6  my husband and i have stayed in this hotel a f...   \n",
       "7  my wife  i stayed in this glorious city a whil...   \n",
       "8  my boyfriend and i stayed at the fairmont on a...   \n",
       "9  wonderful staff great location but it was defi...   \n",
       "\n",
       "                             cleaned_description_new  \n",
       "0  the room was kind of clean but had a very stro...  \n",
       "1  i stayed at the crown plaza april   april   th...  \n",
       "2  i booked this hotel through hotwire at the low...  \n",
       "3  stayed here with husband and sons on the way t...  \n",
       "4  my girlfriends and i stayed here to celebrate ...  \n",
       "5  we had  rooms one was very nice and clearly ha...  \n",
       "6  my husband and i have stayed in this hotel a f...  \n",
       "7  my wife  i stayed in this glorious city a whil...  \n",
       "8  my boyfriend and i stayed at the fairmont on a...  \n",
       "9  wonderful staff great location but it was defi...  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the updated text:\n",
    "Reviewdata['cleaned_description_new'] = pd.DataFrame(Reviewdata['cleaned_description'].apply(cleaned2))\n",
    "Reviewdata.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"model\"></a>\n",
    "<h3><B><left>- Modeling: </left></B></h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IV_train : 35038\n",
      "IV_test  : 3894\n",
      "DV_train : 35038\n",
      "DV_test  : 3894\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = Reviewdata.cleaned_description_new\n",
    "y = Reviewdata.Is_Response\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, random_state = 225)\n",
    "\n",
    "print('X_train :', len(IV_train))\n",
    "print('X_test  :', len(IV_test))\n",
    "print('y_train :', len(DV_train))\n",
    "print('y_test  :', len(DV_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "tvec = TfidfVectorizer()\n",
    "clf2 = LogisticRegression(solver = \"lbfgs\")\n",
    "\n",
    "\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2418,  306],\n",
       "       [ 153, 1017]], dtype=int64)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = Pipeline([('vectorizer',tvec),('classifier',clf2)])\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "predictions = model.predict(X_test)\n",
    "\n",
    "confusion_matrix(predictions, DV_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"result\"></a>\n",
    "<h3><B><left>- Result Metrices: </left></B></h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :  0.8821263482280431\n",
      "Precision :  0.8888758956340842\n",
      "Recall :  0.8821263482280431\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score\n",
    "\n",
    "print(\"Accuracy : \", accuracy_score(predictions, y_test))\n",
    "print(\"Precision : \", precision_score(predictions, y_test, average = 'weighted'))\n",
    "print(\"Recall : \", recall_score(predictions, y_test, average = 'weighted'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"clean\"></a>\n",
    "<h3><B><left>- Make Predictions: </left></B></h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['happy']\n"
     ]
    }
   ],
   "source": [
    "example = [\"I'm not happy\"]\n",
    "result = model.predict(example)\n",
    "\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3><B><center> ...The End... </center></B></h3>"
   ]
  }
 ],
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