Hotel_with_TextBlob.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import sklearn.ensemble\n",
    "import sklearn.metrics\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "import re\n",
    "\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score\n",
    "import lime\n",
    "from lime import lime_text\n",
    "from lime.lime_text import LimeTextExplainer\n",
    "from sklearn.pipeline import make_pipeline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "from nltk.sentiment.util import *\n",
    "from textblob import TextBlob\n",
    "from nltk import tokenize\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\Afaf.Athar\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     C:\\Users\\Afaf.Athar\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\Afaf.Athar\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     C:\\Users\\Afaf.Athar\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('vader_lexicon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = pd.read_csv('hotel-reviews.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(subset =\"Description\", keep = \"first\", inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38919, 5)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Description'] = df['Description'].astype('str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.dropna()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_polarity(text):\n",
    "    return TextBlob(text).sentiment.polarity\n",
    "\n",
    "\n",
    "df['Polarity'] = df['Description'].apply(get_polarity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Sentiment_Type']=''\n",
    "df.loc[df.Polarity>0,'Sentiment_Type']='POSITIVE'\n",
    "df.loc[df.Polarity==0,'Sentiment_Type']='NEUTRAL'\n",
    "df.loc[df.Polarity<0,'Sentiment_Type']='NEGATIVE'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "POSITIVE    35989\n",
       "NEGATIVE     2848\n",
       "NEUTRAL        82\n",
       "Name: Sentiment_Type, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Sentiment_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Sentiment Analysis'}>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eBQ5o8vcXS+qS1LVp06aBlB4REf1oORAk7QNcBXzS9nN9LdqkzX2099Vn+wZ7me2ZtmdOmDChv5IjImIAWgoESS+nCoPLbH+nNG8sw0CU58dL+3pgSkP3ycCjpX1yk/bt+kgaDewHPDXQNxMREYPXylFGAi4C7rX91YZZq4CFZXohcE1D+4Jy5NChVDuP15Rhpc2SZpd1ntyrT8+6TgBuKvsZIiKiTUa3sMxbgA8Dd0m6vbR9BjgLWClpEfAwcCKA7bWSVgL3UB2htMT2ttLvVOBiYAxwXXlAFTiXSuqm2jJYMLS3FRERA9VvINj+Cc3H+AHm7KTPUmBpk/Yu4Mgm7S9SAiUiIuqRM5UjIgJIIERERJFAiIgIIIEQERFFAiEiIoAEQkREFAmEiIgAEggREVEkECIiAkggREREkUCIiAgggRAREUUCISIigARCREQUCYSIiAASCBERUSQQIiICSCBERESRQIiICKCFQJD0LUmPS7q7oe0Lkh6RdHt5vKdh3hmSuiXdL+nYhvajJd1V5p0nSaV9L0lXlPbVkqbu4vcYEREtaGUL4WJgbpP2c23PKI/vAUiaDiwAjih9zpc0qix/AbAYmFYePetcBDxt+zDgXODsQb6XiIgYgn4DwfaPgKdaXN88YIXtLbYfBLqBWZImAmNt32LbwCXA/IY+y8v0lcCcnq2HiIhon6HsQ/i4pDvLkNK40jYJWNewzPrSNqlM927fro/trcCzwAFDqCsiIgZhsIFwAfBaYAawATintDf7Ze8+2vvqswNJiyV1SeratGnTgAqOiIi+DSoQbG+0vc3274ELgVll1npgSsOik4FHS/vkJu3b9ZE0GtiPnQxR2V5me6btmRMmTBhM6RERsRODCoSyT6DH8UDPEUirgAXlyKFDqXYer7G9AdgsaXbZP3AycE1Dn4Vl+gTgprKfISIi2mh0fwtIuhx4B3CgpPXA54F3SJpBNbTzEHAKgO21klYC9wBbgSW2t5VVnUp1xNIY4LryALgIuFRSN9WWwYJd8L4iImKA+g0E2yc1ab6oj+WXAkubtHcBRzZpfxE4sb86IiJi98qZyhERASQQIiKiSCBERASQQIiIiCKBEBERQAIhIiKKBEJERAAJhIiIKBIIEREBJBAiIqJIIEREBJBAiIiIIoEQERFAAiEiIooEQkREAAmEiIgoEggREQEkECIiokggREQEkECIiIii30CQ9C1Jj0u6u6FtvKQbJD1Qnsc1zDtDUrek+yUd29B+tKS7yrzzJKm07yXpitK+WtLUXfweIyKiBa1sIVwMzO3Vdjpwo+1pwI3lNZKmAwuAI0qf8yWNKn0uABYD08qjZ52LgKdtHwacC5w92DcTERGD128g2P4R8FSv5nnA8jK9HJjf0L7C9hbbDwLdwCxJE4Gxtm+xbeCSXn161nUlMKdn6yEiItpnsPsQDra9AaA8H1TaJwHrGpZbX9omlene7dv1sb0VeBY4YJB1RUTEIO3qncrNftm7j/a++uy4cmmxpC5JXZs2bRpkiRER0cxgA2FjGQaiPD9e2tcDUxqWmww8WtonN2nfro+k0cB+7DhEBYDtZbZn2p45YcKEQZYeERHNDDYQVgELy/RC4JqG9gXlyKFDqXYerynDSpslzS77B07u1adnXScAN5X9DBER0Uaj+1tA0uXAO4ADJa0HPg+cBayUtAh4GDgRwPZaSSuBe4CtwBLb28qqTqU6YmkMcF15AFwEXCqpm2rLYMEueWcRETEg/QaC7ZN2MmvOTpZfCixt0t4FHNmk/UVKoERERH1ypnJERAAJhIiIKBIIEREBJBAiIqJIIEREBJBAiIiIIoEQERFAAiEiIooEQkREAAmEiIgoEggREQEkECIiokggREQEkECIiIgigRAREUACISIiigRCREQACYSIiCgSCBERASQQIiKiGFIgSHpI0l2SbpfUVdrGS7pB0gPleVzD8mdI6pZ0v6RjG9qPLuvplnSeJA2lroiIGLhdsYXw57Zn2J5ZXp8O3Gh7GnBjeY2k6cAC4AhgLnC+pFGlzwXAYmBaeczdBXVFRMQA7I4ho3nA8jK9HJjf0L7C9hbbDwLdwCxJE4Gxtm+xbeCShj4REdEmQw0EA9dLulXS4tJ2sO0NAOX5oNI+CVjX0Hd9aZtUpnu370DSYkldkro2bdo0xNIjIqLR6CH2f4vtRyUdBNwg6b4+lm22X8B9tO/YaC8DlgHMnDmz6TIRETE4Q9pCsP1oeX4cuBqYBWwsw0CU58fL4uuBKQ3dJwOPlvbJTdojIqKNBh0Ikl4lad+eaeDdwN3AKmBhWWwhcE2ZXgUskLSXpEOpdh6vKcNKmyXNLkcXndzQJyIi2mQoQ0YHA1eXI0RHA9+2/X1JPwdWSloEPAycCGB7raSVwD3AVmCJ7W1lXacCFwNjgOvKIyIi2mjQgWD7V8BRTdqfBObspM9SYGmT9i7gyMHWEhERQ5czlSMiAkggREREkUCIiAgggRAREUUCISIigARCREQUCYSIiACGfi2jiGFh6unfrbuE3eahs/6i7hJiD5EthIiIABIIERFRJBAiIgJIIERERJFAiIgIIIEQERFFAiEiIoAEQkREFAmEiIgAEggREVEkECIiAkggRERE0TGBIGmupPsldUs6ve56IiJGmo4IBEmjgG8AxwHTgZMkTa+3qoiIkaUjAgGYBXTb/pXt3wErgHk11xQRMaJ0yv0QJgHrGl6vB47pvZCkxcDi8vJ5Sfe3oba6HAg80a4/prPb9ZdGhHx2w1tbP78aHLKzGZ0SCGrS5h0a7GXAst1fTv0kddmeWXcdMXD57Ia3kfz5dcqQ0XpgSsPrycCjNdUSETEidUog/ByYJulQSa8AFgCraq4pImJE6YghI9tbJX0c+AEwCviW7bU1l1W3ETE0tofKZze8jdjPT/YOQ/URETECdcqQUURE1CyBEBERQAIhIiKKBELEbiKpIw7aiGhVAqEDSHp9w/RevebNbn9F0SpJP2mYvrTX7DVtLid2IUkP111DuyUQOsO3G6Zv6TXv/HYWEgP2qobpI3rNa3YGfgwfI+7zSyB0Bu1kutnr6Cx9HbedY7qHtxH3+WWMszN4J9PNXkdn2V/S8VQ/rvaX9L7SLmC/+sqKVkj69M5mAfu0s5ZOkEDoDJMlnUf1H2HPNOX1pPrKihb8O/BXDdN/2TDvR+0vJwZo3z7mfa1tVXSInKncASQt7Gu+7eXtqiUGRtLBtjfWXUfsepLebPvnddfRTtlC6Ay/Aa61/WLdhcSA3SHpLuBy4Crbz9ZdUAxeuVPjAuAk4FlgRF0GO1sIHUDS1cBbgO9TfbFcb3tbvVVFK8rtX99F9SXyHqqjxC4HVtl+oc7aojWSDqEKgJOArVQ3kJlp+6E666pDAqFDSBoLHE/1xXIUcA1wue2MQw8T5dLtx1F9hn8O3Gj7g/VWFX2R9FOqnf8rgBW2H5D0oO1Day6tFjnstEPYfs72ctvHAW8Abge+Lmld3z2jU5T7gd8D3As8B0yvt6JowSaqHcsHAxNK24j9lZxA6DCSxgHvA94PjAeuqrei6I+kV0v6O0m3AddS3dNjnu031Vxa9MP2PKofYLcBZ0p6EBgnaVa9ldUjQ0YdQNK+wHyqMcw/obpb3ArgZucD6mhlyGEScCXVEF9XzSXFEEg6iGrIbwEwxfaUfrrsURIIHUDSE1R3i1sBfN/2SzWXFC2S9HbgRwnuPUvZUl9i+x/qrqWdcthpZ3i17d/WXUQMygnAX0vNrzBi+2/bW04MhKQpwN8DfwT8G9V1xb4IfJjqaLERJYHQGVZLavYLU4Btv7HdBUXLMkQ0vF1CdYb5VcBc4GfAWuCNth+rs7A6ZMioA5TjoHfK9q/bVUsMjKQv2f5M3XXE4Ei6w/ZRDa83Um2xb6mxrNpkC6EzXGj73XUXEYMyF0ggDGNlf0HPmN9jwCslvQrA9lO1FVaDBEJnmND/ItGhRvX6QtnOSPtCGYb2A25l+8/vtvJs4DVtr6hGGTLqAJJ+BfyPnc23/Z02lhMDIGkL8AjNA8G2R9QXynAj6ZAMyf6HbCF0hv2A97KTLxUggdC57skJaMPa1VTn/gQJhE7xa9sfrbuI2LVyaexhIXckbJBA6Az5j3L42u4mKpL2A/4a+ABwOLnBUaeb1HBDqh2MtPNIEgid4UN1FxCDY/tiSWOo7pr2Aarhh55LkeRKtZ3vBaqdykF2KncESZtpfoXFnhPTxra5pGiRpMuAtwHXU1165Cage6RePnm4kXSb7exDKLKF0AFs93Vf1+hsRwJPU13y+j7b23Zy1nl0pt/VXUAnyRZCB5D0SuClnovaSXod1d23HrJ9da3FRb8kvZ5quOj9wOPA64E3jMRLHww3ko5m+61zA0/YHpH3IUkgdABJPwIWlbs1HQasAS6jusHKz22fXmuB0TJJM6nC4QRgve0/rbmk6IOkm5s0jwdeAZxk+/b2VlSvBEIHkHSX7TeU6S8C420vKbdkvLVnXgwfqi5/+jbb/153LTFwJdi/avttddfSTtmH0BkaU/mdwJehuiWjpN/XU1K0QtIRwGttryqvz6U60RDgn2orLIbEdpekfequo91yC83OcKekr0j6FHAY1RErSNq/1qqiFWcBTzS8Phb4LnAz8LlaKoohk3QwI/DeytlC6Ax/A3wCmAq8u+FmOdOBr9RVVLRkou2fNrx+zvZVAJJOqammaJGkr7PjF/944E+p/p8cUbIPoYNI2ptqC8HAL22/WHNJ0Q9J99t+3U7m/cL2H7e7pmidpIW9mgw8SXUwx+M1lFSrbCF0AEmjgS8B/xV4mGoob7KkfwY+m3ssd7RHJR1je3Vjo6TZwKM11RStu9r2c81mSHq17YfbXVCdsoXQAcqOyH2BT9neXNrGUg0XvWB7xG26DheSZgFXABfzH9fRPxpYCLzf9pqaSosWNJ6pLOlG23OazRspslO5M7wX+JueMAAov1pOpTpBLTpU+cI/BhgFfKQ8XgbMThgMC40Xlhzfx7wRIUNGncFusqmWyyB0Pkljy1jzDkcUjcQhh2Go91nKO5s3IiQQOsM9kk62fUljo6QPAffVVFO05oeUG6z0HnIA/o3cfKXTHSTp01RbAz3TlNcj7ta2CYTOsAT4jqSPUl2K18CbgTHA8XUWFv3KkMPwdiHV/rve0wD/p/3l1CuB0AFsPwIcI+mdwBFUXyTX2b6x3sqiBRlyGMZsn1l3DZ0kgdAByvkHH6M6B+Eu4CLbW+utKlqUIYdhTFJfZ5Pb9hfbVkwHyGGnHUDSFcBLwI+B46gue/3JWouKlkj6fF/z8wu0s0k6rUnzq4BFwAG2R9T1jBIIHaDX1U5HA2tG2vHPEXWTtC/V5SoWASuBc0ba2coZMuoMfzgT2fbW6srJMRxkyGH4kzQe+DTwQWA58Ce2n663qnokEDrDUZJ6Tp8XMKa8zj2VO99vmrT9YcgBSCB0MElfBt4HLKO6y93zNZdUqwwZRewiGXIYfsr9RrYAW9n+qLAR+WMsWwgRQ5Qhh+HLdi7f0yCBEDEEGXKIPUmGjCKGIEMOsSdJIEREBJDLX0dERJFAiIgIIIEQERFFAiEiIoAEQkREFP8fvtTC2EDLX6AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.Sentiment_Type.value_counts().plot(kind='bar',title=\"Sentiment Analysis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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(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",
    "cleaned = lambda x: text_clean(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>User_ID</th>\n",
       "      <th>Description</th>\n",
       "      <th>Browser_Used</th>\n",
       "      <th>Device_Used</th>\n",
       "      <th>Is_Response</th>\n",
       "      <th>Polarity</th>\n",
       "      <th>Sentiment_Type</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",
       "      <td>0.346190</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>id10327</td>\n",
       "      <td>i stayed at the crown plaza april   april   th...</td>\n",
       "      <td>Internet Explorer</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>not happy</td>\n",
       "      <td>0.042363</td>\n",
       "      <td>POSITIVE</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",
       "      <td>0.124969</td>\n",
       "      <td>POSITIVE</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",
       "      <td>0.718500</td>\n",
       "      <td>POSITIVE</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",
       "      <td>0.117192</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>id10331</td>\n",
       "      <td>we had  rooms one was very nice and clearly ha...</td>\n",
       "      <td>InternetExplorer</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>happy</td>\n",
       "      <td>0.205686</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>id10332</td>\n",
       "      <td>my husband and i have stayed in this hotel a f...</td>\n",
       "      <td>Firefox</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>not happy</td>\n",
       "      <td>0.151852</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>id10333</td>\n",
       "      <td>my wife  i stayed in this glorious city a whil...</td>\n",
       "      <td>Google Chrome</td>\n",
       "      <td>Mobile</td>\n",
       "      <td>happy</td>\n",
       "      <td>0.217632</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>id10334</td>\n",
       "      <td>my boyfriend and i stayed at the fairmont on a...</td>\n",
       "      <td>Internet Explorer</td>\n",
       "      <td>Desktop</td>\n",
       "      <td>happy</td>\n",
       "      <td>0.333836</td>\n",
       "      <td>POSITIVE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>id10335</td>\n",
       "      <td>wonderful staff great location but it was defi...</td>\n",
       "      <td>Chrome</td>\n",
       "      <td>Tablet</td>\n",
       "      <td>not happy</td>\n",
       "      <td>0.234167</td>\n",
       "      <td>POSITIVE</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   th...   \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",
       "5  id10331  we had  rooms one was very nice and clearly ha...   \n",
       "6  id10332  my husband and i have stayed in this hotel a f...   \n",
       "7  id10333  my wife  i stayed in this glorious city a whil...   \n",
       "8  id10334  my boyfriend and i stayed at the fairmont on a...   \n",
       "9  id10335  wonderful staff great location but it was defi...   \n",
       "\n",
       "        Browser_Used Device_Used Is_Response  Polarity Sentiment_Type  \n",
       "0               Edge      Mobile   not happy  0.346190       POSITIVE  \n",
       "1  Internet Explorer      Mobile   not happy  0.042363       POSITIVE  \n",
       "2            Mozilla      Tablet   not happy  0.124969       POSITIVE  \n",
       "3   InternetExplorer     Desktop       happy  0.718500       POSITIVE  \n",
       "4               Edge      Tablet   not happy  0.117192       POSITIVE  \n",
       "5   InternetExplorer     Desktop       happy  0.205686       POSITIVE  \n",
       "6            Firefox      Tablet   not happy  0.151852       POSITIVE  \n",
       "7      Google Chrome      Mobile       happy  0.217632       POSITIVE  \n",
       "8  Internet Explorer     Desktop       happy  0.333836       POSITIVE  \n",
       "9             Chrome      Tablet   not happy  0.234167       POSITIVE  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's take a look at the updated text\n",
    "df['Description'] = pd.DataFrame(df.Description.apply(cleaned))\n",
    "df.head(10)"
   ]
  }
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