NLP_Sentiment Analysis & Classifier..ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "862c353b",
   "metadata": {},
   "source": [
    "## Trip Advisor Hotel Reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77a3431",
   "metadata": {},
   "source": [
    "### Important lLibraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8b38ae6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import itertools\n",
    "import time\n",
    "import nltk\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "386c8a3a",
   "metadata": {},
   "source": [
    "#### Let's read the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d3a5f09a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Trip_Advisor_cleaned.csv', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2793d38e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>Rating</th>\n",
       "      <th>Review2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nice hotel expens park good deal stay hotel an...</td>\n",
       "      <td>4</td>\n",
       "      <td>nice hotel expens park good deal hotel anniver...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>noth special charg diamond member hilton decid...</td>\n",
       "      <td>2</td>\n",
       "      <td>noth special charg diamond member hilton decid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nice room experi hotel monaco seattl good hote...</td>\n",
       "      <td>3</td>\n",
       "      <td>nice room experi hotel monaco seattl good hote...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>uniqu great stay wonder time hotel monaco loca...</td>\n",
       "      <td>5</td>\n",
       "      <td>uniqu great wonder hotel monaco locat excel sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>great stay great stay go seahawk game awesom d...</td>\n",
       "      <td>5</td>\n",
       "      <td>great great seahawk game awesom downfal view b...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Review  Rating  \\\n",
       "0  nice hotel expens park good deal stay hotel an...       4   \n",
       "1  noth special charg diamond member hilton decid...       2   \n",
       "2  nice room experi hotel monaco seattl good hote...       3   \n",
       "3  uniqu great stay wonder time hotel monaco loca...       5   \n",
       "4  great stay great stay go seahawk game awesom d...       5   \n",
       "\n",
       "                                             Review2  \n",
       "0  nice hotel expens park good deal hotel anniver...  \n",
       "1  noth special charg diamond member hilton decid...  \n",
       "2  nice room experi hotel monaco seattl good hote...  \n",
       "3  uniqu great wonder hotel monaco locat excel sh...  \n",
       "4  great great seahawk game awesom downfal view b...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1db58f10",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Review', 'Rating', 'Review2'], dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "19f775bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20491, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b424806",
   "metadata": {},
   "source": [
    "#### Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "312e741e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>Rating</th>\n",
       "      <th>Review2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>nice hotel expens park good deal hotel anniver...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>noth special charg diamond member hilton decid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>nice room experi hotel monaco seattl good hote...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>uniqu great wonder hotel monaco locat excel sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>great great seahawk game awesom downfal view b...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rating                                            Review2\n",
       "0       4  nice hotel expens park good deal hotel anniver...\n",
       "1       2  noth special charg diamond member hilton decid...\n",
       "2       3  nice room experi hotel monaco seattl good hote...\n",
       "3       5  uniqu great wonder hotel monaco locat excel sh...\n",
       "4       5  great great seahawk game awesom downfal view b..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df.drop(['Review'],axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db503f7b",
   "metadata": {},
   "source": [
    "#### Counting the word length "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dcd3651c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Word_count']= data['Review2'].map(lambda x: len(x.split()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f710899",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rating</th>\n",
       "      <th>Review2</th>\n",
       "      <th>Word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>nice hotel expens park good deal hotel anniver...</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>noth special charg diamond member hilton decid...</td>\n",
       "      <td>195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>nice room experi hotel monaco seattl good hote...</td>\n",
       "      <td>149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>uniqu great wonder hotel monaco locat excel sh...</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>great great seahawk game awesom downfal view b...</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rating                                            Review2  Word_count\n",
       "0       4  nice hotel expens park good deal hotel anniver...          68\n",
       "1       2  noth special charg diamond member hilton decid...         195\n",
       "2       3  nice room experi hotel monaco seattl good hote...         149\n",
       "3       5  uniqu great wonder hotel monaco locat excel sh...          74\n",
       "4       5  great great seahawk game awesom downfal view b...         148"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b4e2ebd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rating', ylabel='Word_count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_palette('Oranges_r')\n",
    "sns.lineplot(x='Rating', data=data, y='Word_count')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7cd9e55",
   "metadata": {},
   "source": [
    "## Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e9dc8ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    9054\n",
       "4    6039\n",
       "3    2184\n",
       "2    1793\n",
       "1    1421\n",
       "Name: Rating, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# most reviewers leave a 4 & 5 star review\n",
    "data.Rating.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "81bf9c1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package vader_lexicon to\n",
      "[nltk_data]     /Users/AAHMT/nltk_data...\n",
      "[nltk_data]   Package vader_lexicon is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('vader_lexicon')\n",
    "from nltk.sentiment.vader import SentimentIntensityAnalyzer\n",
    "sid = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8c0e488",
   "metadata": {},
   "source": [
    "### Segregating and Encoding Positive and Negative labels\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "01cd9046",
   "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>Rating</th>\n",
       "      <th>Review2</th>\n",
       "      <th>Word_count</th>\n",
       "      <th>Sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>nice hotel expens park good deal hotel anniver...</td>\n",
       "      <td>68</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>noth special charg diamond member hilton decid...</td>\n",
       "      <td>195</td>\n",
       "      <td>Negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>uniqu great wonder hotel monaco locat excel sh...</td>\n",
       "      <td>74</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>great great seahawk game awesom downfal view b...</td>\n",
       "      <td>148</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>love monaco staff husband hotel crazi weekend ...</td>\n",
       "      <td>115</td>\n",
       "      <td>Positive</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rating                                            Review2  Word_count  \\\n",
       "0       4  nice hotel expens park good deal hotel anniver...          68   \n",
       "1       2  noth special charg diamond member hilton decid...         195   \n",
       "3       5  uniqu great wonder hotel monaco locat excel sh...          74   \n",
       "4       5  great great seahawk game awesom downfal view b...         148   \n",
       "5       5  love monaco staff husband hotel crazi weekend ...         115   \n",
       "\n",
       "  Sentiment  \n",
       "0  Positive  \n",
       "1  Negative  \n",
       "3  Positive  \n",
       "4  Positive  \n",
       "5  Positive  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pos = [4, 5]\n",
    "neg = [1, 2]\n",
    "neu = [3]\n",
    "\n",
    "# Remove 3 star reviews\n",
    "data = data[data.Rating!=3]\n",
    "\n",
    "def sentiment(rating):\n",
    "    if rating in pos:\n",
    "        return 'Positive'\n",
    "    else:\n",
    "        return 'Negative'  \n",
    "data['Sentiment'] = data['Rating'].apply(sentiment)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4cc07348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18307, 4)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "64ad358f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/AAHMT/opt/anaconda3/lib/python3.8/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sentiment', ylabel='count'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.countplot(data['Sentiment'],palette='Oranges_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "592b6cb1",
   "metadata": {},
   "source": [
    "#### Generating Sentiment Scores using Vader Sentiment Analyzer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b2ce7b8",
   "metadata": {},
   "source": [
    "VADER A SentimentAnalyzer is a tool to implement and facilitate Sentiment Analysis tasks using NLTK features and classifiers, especially for teaching and demonstrative purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "708c80b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['scores'] = data['Review2'].apply(lambda review: sid.polarity_scores(review))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "84417d01",
   "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>Rating</th>\n",
       "      <th>Review2</th>\n",
       "      <th>Word_count</th>\n",
       "      <th>Sentiment</th>\n",
       "      <th>scores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>nice hotel expens park good deal hotel anniver...</td>\n",
       "      <td>68</td>\n",
       "      <td>Positive</td>\n",
       "      <td>{'neg': 0.031, 'neu': 0.645, 'pos': 0.325, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>noth special charg diamond member hilton decid...</td>\n",
       "      <td>195</td>\n",
       "      <td>Negative</td>\n",
       "      <td>{'neg': 0.024, 'neu': 0.722, 'pos': 0.254, 'co...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>uniqu great wonder hotel monaco locat excel sh...</td>\n",
       "      <td>74</td>\n",
       "      <td>Positive</td>\n",
       "      <td>{'neg': 0.0, 'neu': 0.587, 'pos': 0.413, 'comp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>great great seahawk game awesom downfal view b...</td>\n",
       "      <td>148</td>\n",
       "      <td>Positive</td>\n",
       "      <td>{'neg': 0.07, 'neu': 0.661, 'pos': 0.269, 'com...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>love monaco staff husband hotel crazi weekend ...</td>\n",
       "      <td>115</td>\n",
       "      <td>Positive</td>\n",
       "      <td>{'neg': 0.021, 'neu': 0.83, 'pos': 0.149, 'com...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rating                                            Review2  Word_count  \\\n",
       "0       4  nice hotel expens park good deal hotel anniver...          68   \n",
       "1       2  noth special charg diamond member hilton decid...         195   \n",
       "3       5  uniqu great wonder hotel monaco locat excel sh...          74   \n",
       "4       5  great great seahawk game awesom downfal view b...         148   \n",
       "5       5  love monaco staff husband hotel crazi weekend ...         115   \n",
       "\n",
       "  Sentiment                                             scores  \n",
       "0  Positive  {'neg': 0.031, 'neu': 0.645, 'pos': 0.325, 'co...  \n",
       "1  Negative  {'neg': 0.024, 'neu': 0.722, 'pos': 0.254, 'co...  \n",
       "3  Positive  {'neg': 0.0, 'neu': 0.587, 'pos': 0.413, 'comp...  \n",
       "4  Positive  {'neg': 0.07, 'neu': 0.661, 'pos': 0.269, 'com...  \n",
       "5  Positive  {'neg': 0.021, 'neu': 0.83, 'pos': 0.149, 'com...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "714819bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Positive    0.824439\n",
       "Negative    0.175561\n",
       "Name: Sentiment, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Note that the dataset has mostly positive reviews\n",
    "data.Sentiment.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1082280b",
   "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>Sentiment</th>\n",
       "      <th>Review2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Positive</td>\n",
       "      <td>nice hotel expens park good deal hotel anniver...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Negative</td>\n",
       "      <td>noth special charg diamond member hilton decid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Positive</td>\n",
       "      <td>uniqu great wonder hotel monaco locat excel sh...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Positive</td>\n",
       "      <td>great great seahawk game awesom downfal view b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Positive</td>\n",
       "      <td>love monaco staff husband hotel crazi weekend ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sentiment                                            Review2\n",
       "0  Positive  nice hotel expens park good deal hotel anniver...\n",
       "1  Negative  noth special charg diamond member hilton decid...\n",
       "3  Positive  uniqu great wonder hotel monaco locat excel sh...\n",
       "4  Positive  great great seahawk game awesom downfal view b...\n",
       "5  Positive  love monaco staff husband hotel crazi weekend ..."
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Include only the sentiment and reviews columns\n",
    "df1 = data[['Sentiment', 'Review2']]\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd0d29a",
   "metadata": {},
   "source": [
    "## Prepare Data for Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7dd13a55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into X and y data sets\n",
    "X = df1.Review2\n",
    "y = df1.Sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f87da588",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and test sets\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=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4c276bec",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create TF-IDF versions \n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidf1 = TfidfVectorizer(stop_words='english')\n",
    "X_train_tfidf1 = tfidf1.fit_transform(X_train)\n",
    "X_test_tfidf1  = tfidf1.transform(X_test)\n",
    "\n",
    "tfidf2 = TfidfVectorizer(ngram_range=(1,2), binary=True, stop_words='english')\n",
    "X_train_tfidf2 = tfidf2.fit_transform(X_train)\n",
    "X_test_tfidf2  = tfidf2.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aebba60",
   "metadata": {},
   "source": [
    "### 1- Classifying using Logistic Regression With TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e5caab9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a logistic regression model to use\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9fd5609a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function to calculate the error metrics, since we'll be doing this several times\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "def conf_matrix(actual, predicted):\n",
    "    cm = confusion_matrix(actual, predicted)\n",
    "    sns.heatmap(cm, xticklabels=['predicted_negative', 'predicted_positive'], \n",
    "                yticklabels=['actual_negative', 'actual_positive'], annot=True,\n",
    "                fmt='d', annot_kws={'fontsize':20}, cmap=\"Oranges\");\n",
    "\n",
    "    true_neg, false_pos = cm[0]\n",
    "    false_neg, true_pos = cm[1]\n",
    "\n",
    "    accuracy = round((true_pos + true_neg) / (true_pos + true_neg + false_pos + false_neg),3)\n",
    "    precision = round((true_pos) / (true_pos + false_pos),3)\n",
    "    recall = round((true_pos) / (true_pos + false_neg),3)\n",
    "    f1 = round(2 * (precision * recall) / (precision + recall),3)\n",
    "\n",
    "    cm_results = [accuracy, precision, recall, f1]\n",
    "    return cm_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c6901058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit the first logistic regression on the TF-IDF data\n",
    "lr.fit(X_train_tfidf1, y_train)\n",
    "y_pred_tfidf1_lr = lr.predict(X_test_tfidf1)\n",
    "cm1 = conf_matrix(y_test, y_pred_tfidf1_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1d36c5f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fit the second logistic regression on the TF-IDF data\n",
    "lr.fit(X_train_tfidf2, y_train)\n",
    "y_pred_tfidf2_lr = lr.predict(X_test_tfidf2)\n",
    "cm2 = conf_matrix(y_test, y_pred_tfidf2_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "de2fd69e",
   "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>LR1-TFIDF</th>\n",
       "      <th>LR2-TFIDF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.944</td>\n",
       "      <td>0.905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.943</td>\n",
       "      <td>0.897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.991</td>\n",
       "      <td>0.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1 Score</th>\n",
       "      <td>0.966</td>\n",
       "      <td>0.945</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           LR1-TFIDF  LR2-TFIDF\n",
       "Accuracy       0.944      0.905\n",
       "Precision      0.943      0.897\n",
       "Recall         0.991      0.999\n",
       "F1 Score       0.966      0.945"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compile all of the error metrics into a dataframe for comparison\n",
    "results = pd.DataFrame(list(zip(cm1, cm2)))\n",
    "results = results.set_index([['Accuracy', 'Precision', 'Recall', 'F1 Score']])\n",
    "results.columns = ['LR1-TFIDF', 'LR2-TFIDF']\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "660b574a",
   "metadata": {},
   "source": [
    "###  2 - Classifying using Naive Bayes  With TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "41e2d561",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the first Naive Bayes model\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "mnb = MultinomialNB()\n",
    "mnb.fit(X_train_tfidf1, y_train)\n",
    "\n",
    "y_pred_tfidf1_nb = mnb.predict(X_test_tfidf1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ea28bb60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the second Naive Bayes model\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "\n",
    "bnb = BernoulliNB()\n",
    "bnb.fit(X_train_tfidf2, y_train)\n",
    "\n",
    "y_pred_tfidf2_nb = bnb.predict(X_test_tfidf2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "10bd9a4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here's the heat map for the first Naive Bayes model\n",
    "cm3 = conf_matrix(y_test, y_pred_tfidf1_nb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1bafb01e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UmZmPLO+ci5R0qUHHPRaHpN6SPgeWAJOBL4E3UxSXc86VW7qMxZHIYEl/AroBi8ysLXAy8F5KonLOuYpIUoaWVEPSDElzJX0i6Q9heUNJ4yV9Hn5tELPP7ZIWS/pM0ukx5UdJmheue1RxVPMTSdDbzOx7IEtSlplNBDolsL9zzu0RSaxBbwVOMrPDCfLdGeH7WG8DJphZe2BCuIykDkB/oCNwBvCEpMKxZp8EBgHtw+mMsk6eSIJeK6kOMAUYKekRID+B/Z1zbo+QFPdUGgv8EC7mhJMBfYARYfkI4Nxwvg/wTzPbamZLgMVAV0nNgVwzmxaOqf9czD4lSiRB9wE2AzcB44D/AecksL9zzu0RWVmKe4p9PV84DYo9lqRsSXOAVcB4M/sAaGpmywHCr03CzVsAS2N2zwvLWoTzu5eXKpGXxm6MWRxR4obOOVfZErj7t8vr+YpfXwB0klQfeFXSIaWdubhDlFJeqkTeSRj7wEo1gqr+xsIHVZxzLipS0TvDzNZKmkTQdrxSUnMzWx42X6wKN8sDYt+m0BJYFpa3LKa8VHE3cZhZXTPLDacawPnA4/Hu75xze0qy2qAlNQ5rzkiqCZwCLATGAAPCzQYAr4fzY4D+kqpLaktwM3BG2AyyQVK3sPfGpTH7lKjcY3GY2WuSbivv/s45lypJrEE3B0aEPTGygFFmNlbSNGCUpIHA18AFAGb2iaRRwKcEnSiuDZtIAK4GhgM1CZ4hKfM5kkSaOM6LWcwieMu3jwHtnIscxfki3rKY2cfAEcWUf0/wLEhx+9wN3F1M+UygtPbrIhKpQcf22MgneJKwTyInc865PSHqTwjGK5EEPdTMdnlyUFJ3djaOO+dcJGTcWBzAY3GWOedc5VICU4TFMx70McCxQGNJN8esygWyi9/LOecqT7LaoCtbPE0c1YA64baxY0OvB/qlIijnnKuQNGniiGfA/snAZEnDzeyrPRCTc85ViLLS45/7RP4PGFrYYRtAUgNJbyU/JOecq6A0GRA6kV4cjcxsbeGCma0J367inHPREvHEG69EatDbJe1buCCpDf6ginMugpL1qHdlS6QG/VtgqqTJ4XIPgsGnnXMuWpQ5vTgAMLNxkjoTJOU5BAN9bE5RXM45V27KzrAELekK4AaCYfLmELyfcBpwUkoic8658kqTGnQiV3ED0AX4ysxOJBhA5NuUROWccxWQiW3QW8xsS3hR1c1soaQDUxaZc86VV8QTb7wSSdB5YT/o14DxktYQxxsBnHNuj8u0BG1mfcPZwZImAvUIXh7rnHORojRpgy7XG1XCx7+dcy6SMq4Xh3POVRmZXIN2zrlIy7Q2aOecqyqi3n0uXp6gnXPpxxO0c85FlLdBO+dcNHkvDueci6iM7gftnHOR5m3QzjkXUZ6gnXMumrybnXPORVWavNXbE7RzLu14Ddq5Cvpj73MrOwQXQYMXbqv4QbK8F4dzzkWT16Cdcy6ivB+0c85FlNegnXMuorwXh3PORZQ3cTjnXER5E4dzzkWU16Cdcy6ivAbtnHMRlSYJOj3+D3DOuVhZ2fFPpZDUStJESQskfSLphrC8oaTxkj4PvzaI2ed2SYslfSbp9JjyoyTNC9c9qjieR/cE7ZxLP8qKfypdPvArMzsY6AZcK6kDcBswwczaAxPCZcJ1/YGOwBnAE5IKPwWeBAYB7cPpjLJO7gnaOZd+pPinUpjZcjP7KJzfACwAWgB9gBHhZiOAc8P5PsA/zWyrmS0BFgNdJTUHcs1smpkZ8FzMPiXyBO2cSz8J1KAlDZI0M2YaVOwhpTbAEcAHQFMzWw5BEgeahJu1AJbG7JYXlrUI53cvL5XfJHTOpZ8EbhKa2RBgSOmHUx3gX8CNZra+lObj4lZYKeWl8gTtnEs/SXzUW1IOQXIeaWajw+KVkpqb2fKw+WJVWJ4HtIrZvSWwLCxvWUx5qbyJwzmXfpJ0kzDsafF3YIGZ/SVm1RhgQDg/AHg9pry/pOqS2hLcDJwRNoNskNQtPOalMfuUyGvQzrn0k7x+0N2BS4B5kuaEZb8B7gVGSRoIfA1cAGBmn0gaBXxK0APkWjMrCPe7GhgO1ATeDKdSeYJ2zqWfJD3qbWZTKb79GODkEva5G7i7mPKZwCGJnN8TtHMu/WTik4SSjpP083C+cdjG4pxz0ZK8B1UqVdw1aEm/BzoDBwLDgBzgBYI2Gueci44MHLC/L0En7cKnapZJqpuSqJxzriLSpIkjkQT9o5mZJAOQVDtFMTnnXMVEvOkiXolcxShJTwP1JV0JvAM8k5qwnHOuArIU/xRhcdegzexBSacC6wnaoe80s/Epi8w558or05o4JN0EvOxJ2TkXeWnSxJFIG3Qu8Jak1cA/gVfMbGVqwnLOuQpIk14ccX/MmNkfzKwjcC2wDzBZ0jspi8w558or0/pBx1gFrAC+Z+cYqM45Fx0RT7zxivsqJF0taRLB610aAVea2WGpCsw558otSW9UqWyJ1KBbEwxWPSdFsTjnXHKkSQ26zAQtKdfM1gP3h8sNY9eb2eoUxeacc+WTJjcJ46lB/wM4G5hF0Ve3GLBfCuJyzrnyy5QatJmdHX71keucc1VDmiToRG4SToinzDnnKl2mdLOTVAOoBTSS1ICdTRy5BP2hnXMuWiLeOyNe8bRBXwXcSJCMP4opXw/8LQUxOedcxUS8ZhyveNqgHwEekXSdmT22B2JyzrmKyU6Pt/nF08Rxkpm9C3wj6bzd15vZ6JRE5pxz5ZVBTRwnAO8C5xSzzgBP0M65aMmgJo7fh19/nvpwnHMuCdIkQSfSze4GSbkKDJX0kaTTUhmcc86VS5qMxZHIx8zl4SPfpxGMYvdz4N6UROWccxWRtVf8U4QlEl3hR00vYJiZzZUi/vHjnMtMadLEkUiCniXpbaAtcLukusD21ITlnHMVkCZ1x0QS9ECgE/CFmW2StDdBM4dzzkVLptWgzWy7pJbARWHLxmQzeyNlkVUx48ZP5MNZs1mw6HMWLlrMxo2bOKfXaTx49++LbLt8xUqefvZ5PlnwGcuWr2Dd+g3Ur5fLvq1acH6fs+nd63RycqLdNubgsN4/47z7hwMw5o6r+OiVZ3esq9+iNTdOWFzivvP//RKv/OriXcpaHXEsB518Dm2O7kn9Fq2pXieXDauWsWTaRKY+cz+rv/5fmTHt3aY9V43+kGq1avPxmH8w+pYB5bu4qi7TErSke4EuwMiw6HpJx5rZ7SmJrIp5cuhwFi5aTK1aNWnWtAlfLPmqxG2/zvuGN958m8MP6cDJPY+nfr1c1q5bz5T3pvObwffw2tg3Gfbkw+y1lyfpqMpt1pJedzzM1o0bqF67bonbrVgwl4UTXi9SvurzT4qU/fTRl6jVsDFLZ09j3hsvsr0gn5adunHkBZdzSK+f8NzAM8mbM73Ec2VlZ9P3vuGYectjJjZx9AI6WfjTlzQCmA14ggZu//X1NGvShNb7tmTGrNlceuV1JW57xOGH8uHkcWRl7fopv21bPpdfcyMzZs7m7Xcn0+u0k1Mdtiunc+8Zyqa137Ng/Gt0H/irErdbsXAukx7/U1zHnDbiET4eM5INq5bvUn78Vbdy8k13cc4fn+TJ3keUuP/xV91Gs4MPZ/wDt3Hmb/8a34Wkq4j3zohXov8H1I+Zr5fEOKq8bl2Ook3rVsTTsaVaTk6R5AyQk7MXp5zYA4Cvvs5LeowuOY6+5DradjuR139zBds2b0zacd8b+mCR5Aww9ZkH2LZ5E00POISa9RsWsyfsc8hR9Lj6t0x54m5WfvZx0mKqsjJluNEYfwZmS5pI0OWuB157TqqCggKmTJ0GwIHt21VyNK44jfY7iFN+dTfTn3uMr2ZOpW23E0vdvm6T5hz10yupVb8hm9auJm/2dFYumpfYSc3YXpAfzBYUFFm9V/Ua9L33WVYsnMvUZ+5n36O6J3b8dJSVYU0cZvZi+FbvLmHRrWa2IiVRZYjVa9Yy8qV/YWasXrOW96d/yFdL8zj7zFM5sYf/kUVNVnY2590/nHXLv2bCX++Ia5923U+lXfdTdylb8sEkXrvtctYtXxrXMTqc0Y/qdXJZOmc6WzasK7L+lF/dQ4NW+/H0eV3ZXkwCz0gRrxnHK9GGmmOA4wgGScoGXk16RBlkzdp1PP70zjv/krj80gu5+Ze/iKupxO1ZJ1xzB80O7sSzP+tJ/tYtpW67bfMmJv/tLhZOGMOapV8A0PTAQ+n5yztp2+1ELh32Fk/17cy2zZtKPU79Fm3odcfDFGzbxtv33VJkfdtuJ3L0xdfyzkO/4dv/LSj/xaWbNEnQiYzF8QTwC2AeMB+4SpIP2F8B7dq25rPZ7/HpzClM/M+/uP3X1zNq9BguHngNa9etr+zwXIwWh3bh+KtuY9qwv5bak6LQxtXfMvGxP7D809ls2bCOLRvW8dXMqTw/8Ezy5nzA3m3ac2S/gaUeo3bDxlz8zBvU3rsJ4+65maWzp+2yvkbdepx7z1DyPp7B+8My/Kbg7tKkDTqR6E4ATjezYWY2jKBXR8+SNpY0SNJMSTOHPPtcBcNMb9nZ2ezTvBkDLvoJf/ztLcyZ9wmPPvlMZYflQlnZ2fS9fxjff7mIdx8p2q89EdsLCnb0l27d5bgSt6vdsDEDhr9No/0O4s27b+LDF58qss3ptz1IrQaNeO32gdh271q3izRJ0Ik0cXwG7AsUdvBtBZR4u9jMhgBDANj0nZUzvozTo3s3AGbMnF3JkbhC1WrVoVHbAwH43bzie230vutpet/1NNNHPMq4P5fc7Q6C2jVAtZq1i11fp3EzBgx7i0b7HcS//3BdsckZoHmHI8ipWYvr3izapxrgsN4XcVjvi1ixYC5P9e1cakxpJ+KJN16JJOi9gQWSZoTLXYBpksYAmFnvZAeXiVauCv54s7OzKzkSVyj/x6189PKzxa5r3uEImnc8gq9mTuX7JYtYGkfzR8tORwOwZumSIutym7ZgwIi3abjv/owdfC2zRg0t8TgLxr/KsvmzipTXadyMA3r2YvVXi/lyxhTWLf+6zJjSTprcw0kkQd+ZsigyzNx5n3DA/u2oWbPGLuUbN23i7gceBqDn8cdWQmSuOPlbtzDmd1cVu67nL39H845HMPe153d51LvFYV1ZsWA2Bdu27bJ926N7csyAGwD4+I1/7LKuXvNWDBgxnvot2vD6HYOYM3pEqXFNfuLuYsvbdO3BAT17kTd3Rolxp7/k1aAlPQucDawys0PCsobAS0Ab4EvgJ2a2Jlx3O8HYRQXA9Wb2Vlh+FDAcqAn8B7jBzEptXUikm93kMi5impkdE+/x0s07E6fwzsQpAHz7/WoA5nw8n9vuvAuABvXrc+vNvwTg6WefZ8bM2XQ5qhP7NGtKjZo1WLFiFVPem876DRs44vBDGXT5JZVzIS4pTv31PTTevwNfzpjM+hXfAEEvjv2OOQmAdx++s8hNv8uen0CDlm1ZNn8W9ffZl56//F2R48559TnWflPyMAIulNwa9HDgcSD2ZtptwAQzu1fSbeHyrZI6AP2BjsA+wDuSDjCzAuBJYBAwnSBBnwG8WdqJk/k8ZI2yN0lfCz77nFff2PV7vTRvGUvzlgHQonmzHQn6J+f1pmbNGsz/ZCEzZs1my5Yt5NatS8eDD+TM007i/D5n+TgcVdzc10dy8Kl9aHFoZ9offwZZOTls/G4l8/8zihkjn+DrWe8V2adBy7ZA8FTgPoccVexxv5wx2RN0PJS8JkIzmyKpzW7FfdjZSWIEMAm4NSz/p5ltBZZIWgx0lfQlkGtm0wAkPQecSxkJWmXUsOMm6SMzO7LYlX6T0BVj8JHNKzsEF0GDF26rcPV3+4JX48452R3Ou4qgZltoSNjJYYcwQY+NaeJYa2b1Y9avMbMGkh4HppvZC2H53wmS8JfAvWZ2Slh+PMHDfmeXFptX05xz6SeBJo5depwl4czFnaKU8lIlsy9Ketw2dc6lgawEpnJZKak5QPh1VVieR9AFuVBLYFlY3rKY8jKvIln8rpZzLhpS/1bvMUDh2xAGAK/HlPeXVF1SW6A9MMPMlgMbJHUL3+V6acw+JSqziUPSBoqvigswM8slmJlf1rGcc26PSOKDKpJeJLgh2EhSHvB74F5glKSBwNfABQBm9omkUcCnQD5wbdiDA+Bqdnaze5MybhBCHAnazEp+XYRzzkVREhO0mV1Ywqpi36hhZncDRTqpm9lM4JBEzp3wTUJJTYjpUmdmGfiYknMu0tLkUe9ERrPrLelzYAkwmaDbSJlVdOec29MkxT1FWSIfM38CugGLzKwtQfW+aG9755yrdCnvxbFHJBLdNjP7HsiSlGVmE4FOqQnLOecqIPW9OPaIRNqg10qqA0wBRkpaRXCX0jnnoiXiiTdeidSg+wCbgZuAccD/gHNSEZRzzlWIsuOfIiyR0exiRyovfRxE55yrTGlSg447Qe/2wEo1IAfYWPiginPORUe0b/7FK5Ea9C4PrEg6F+ia7ICcc67C0qQGXe6PGTN7DTgpeaE451ySZNpLYyWdF7OYBXQmjuHynHNuj4t44o1XIt3sYnts5BM8SdgnqdE451wyZGCCHmpmuzw5KKk7O8dBdc65aMjANujH4ixzzrlKpgSm6IpnPOhjgGOBxpJujlmVC0S7l7dzLjNlUBNHNaBOuG1sV7v1QL9UBOWccxUT7ZpxvOIZsH8yMFnScDPz970756IvTWrQiVzFUEn1CxckNZD0VvJDcs65CkqT0ewSSdCNzGxt4YKZrQGaJD0i55xzQGIJerukfQsXJLXBH1RxzkVRmtSgE+kH/VtgqqTJ4XIPYFDyQ3LOuYqKduKNVyKDJY2T1JkgKc8BXicYH9o556Il4jXjeCUyFscVwA1AS4IE3Q2Yhg+Y5JyLmgzsxXED0AX4ysxOBI4Avk1JVM45VyEZ8iRhjC1mtiV8VXl1M1so6cCUReacc+WVaU0cQF7YD/o1YLykNcCyVATlnHMVk2EJ2sz6hrODJU0E6hG8PNY556IlA2vQO4SPfzvnXERlcIJ2zrkoUybXoJ1zLto8QTvnXDR5Ddo556LKE7RzzkWT16Cdcy6i0uRRb0/Qzrk05DVo55yLJm/icM65qPIE7Zxz0ZQe+dkTtHMuDflNQueci6r0qEJ7gnbOpR+/Seicc1GVHglaZlbZMWQUSYPMbEhlx+GixX8vXHHSoyW9ahlU2QG4SPLfC1eEJ2jnnIsoT9DOORdRnqD3PG9ndMXx3wtXhN8kdM65iPIatHPORZQnaOeciyhP0M45F1EZn6Al9ZQ0NpzvLem2UratL+macpxjsKRfVyTOipJ0maR9YpaHSupQmTFVBeny+yFpH0mvhPOdJPWKWVfqdbnKk7YJWlJ2ovuY2Rgzu7eUTeoDCf8BRsRlwI4EbWZXmNmnlRdO5cq03w8zW2Zm/cLFTkCvmHVlXZerJFUyQUtqI2mhpBGSPpb0iqRakr6UdKekqcAFkk6TNE3SR5JellQn3P+McP+pwHkxx71M0uPhfFNJr0qaG07HAvcC7STNkfRAuN3/SfowjOMPMcf6raTPJL0DHFjG9UySdJ+kGZIWSTo+LM+W9EDM8a8Ky7MkPSHpE0ljJf1HUr9w3Z3h9vMlDVGgH9AZGBnGXjM8Z2dJV0u6f7fvwWPh/MVhTHMkPV2epFYZ0vT342FJ74c/165heUNJr4XHni7psLD8hDCGOZJmS6obfk/mS6oG/BH4abj+p4XXJale+D3KCo9TS9JSSTmS2kkaJ2mWpP9KOihpPzBXMjOrchPQBjCge7j8LPBr4EvglrCsETAFqB0u3wrcCdQAlgLtCUZUGQWMDbe5DHg8nH8JuDGczwbqheedHxPHaQT9V0XwYTcW6AEcBcwDagG5wGLg16VczyTgoXC+F/BOOD8IuCOcrw7MBNoC/YD/hOdsBqwB+oXbNYw57vPAOTHn6LzbOTsDjYHFMeVvAscBBwNvADlh+RPApZX9s8/g349nwvkehecAHgN+H86fBMwJ59+IufY6BIOitYnZb8d1FHNdrwMnhvM/BYaG8xOA9uH80cC7lf1zzoSpKo9mt9TM3gvnXwCuD+dfCr92AzoA7ykYerAaMA04CFhiZp8DSHqB4sdBOAm4FMDMCoB1khrsts1p4TQ7XK5D8IddF3jVzDaF5xgTx/WMDr/OIvhjKjz+YYW1Y4Ik0J4ggb5sZtuBFZImxhznREm3EPzxNwQ+IfiDLZaZfSvpC0ndgM8JanPvAdcSJJIPw+9fTWBVHNcRFen2+/FieK4pknIl1Sf4PTg/LH9X0t6S6hH8/P4iaSQw2szyFP/wmy8RJOaJQH/gifA/i2OBl2OOUz3eA7ryq8oJevcnbAqXN4ZfBYw3swtjN5LUqZh9y0vAn83s6d3OcWM5zrE1/FrAzp+LgOvM7K3djn9WscFINQhqup3NbKmkwQQ1wrK8BPwEWEiQOEzBX+IIM7s9weuIinT7/SjueorLumZm90r6N8F/Y9MlnQJsifM8Y4A/S2pI8AH9LlAbWGtmnRKM2VVQlWyDDu0r6Zhw/kJg6m7rpwPdJe0PO9rTDiBIQm0ltYvZtzgTgKvDfbMl5QIbCGo/hd4CLo9pu2whqQnBv859FbT11gXOKec1vgVcLSknPP4BkmqH13q+grbopkDPcPvCZPxdGFO/mGPtHnus0cC5BN+LwhrmBKBfeD2F7Z2ty3kdlSHdfj9+Gh7jOGCdma0Lj/OzsLwn8J2ZrZfUzszmmdl9BM1iu7cXl/i7YGY/ADOARwiadgrMbD2wRNIF4bkk6fA4YnYVVJUT9AJggKSPCf6VfzJ2pZl9S9C29mK4zXTgIDPbQvAv678V3AT6qoTj30DQXDCPoNmho5l9T/Av8XxJD5jZ28A/gGnhdq8Adc3sI4JENwf4F/Dfcl7jUOBT4CNJ84GnCWrX/wLygMKyDwj+aNcCzxC0b74GfBhzrOHAU+GNoZqxJzGzNeF5WpvZjLDsU+AO4O3w+zceaF7O66gM6fb7sUbS+8BTwMCwbDDQOYz/XmBAWH5jGMNcYDPBfYVYE4EOhTcJiznXS8DF7PywhuCDYGB4zE+APnHE7CqoSo7FIakNwaf7IZUdS2WRVMfMfpC0N0GNp7uZrajsuKIg3X4/JE0iuIk4s7JjcXtWVW6DznRjwxtF1YA/eXJ2Lv1UyRp0VSXpb0D33YofMbNhlRGPixb//XC78wTtnHMRVZVvEjrnXFrzBO2ccxHlCdo55yLKE7RzzkXU/wPEug/lp17g5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here's the heat map for the second Naive Bayes model\n",
    "cm4 = conf_matrix(y_test, y_pred_tfidf2_nb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2a1d65fd",
   "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>Naive1-TFIDF</th>\n",
       "      <th>Naive2-TFIDF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.832</td>\n",
       "      <td>0.829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.831</td>\n",
       "      <td>0.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1 Score</th>\n",
       "      <td>0.908</td>\n",
       "      <td>0.906</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Naive1-TFIDF  Naive2-TFIDF\n",
       "Accuracy          0.832         0.829\n",
       "Precision         0.831         0.830\n",
       "Recall            1.000         0.997\n",
       "F1 Score          0.908         0.906"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compile all of the error metrics into a dataframe for comparison\n",
    "results = pd.DataFrame(list(zip(cm3, cm4)))\n",
    "results = results.set_index([['Accuracy', 'Precision', 'Recall', 'F1 Score']])\n",
    "results.columns = ['Naive1-TFIDF', 'Naive2-TFIDF']\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec1b906e",
   "metadata": {},
   "source": [
    "### 3 - Ada Boost Classifier  With TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "55c53c70",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fit the first Ada Boost model\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "abc = AdaBoostClassifier()\n",
    "# Train Adaboost Classifer\n",
    "abc.fit(X_train_tfidf1, y_train)\n",
    "\n",
    "y_pred_tfidf1_ab = abc.predict(X_test_tfidf1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b1c5027f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here's the heat map for the first Ada Boost model\n",
    "cm5 = conf_matrix(y_test, y_pred_tfidf1_ab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "47d50b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fit the second Ada Boost  model\n",
    "abc = AdaBoostClassifier()\n",
    "# Train Adaboost Classifer\n",
    "abc.fit(X_train_tfidf2, y_train)\n",
    "\n",
    "y_pred_tfidf2_ab = abc.predict(X_test_tfidf2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "72f186a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here's the heat map for the first Ada Boost model\n",
    "cm6 = conf_matrix(y_test, y_pred_tfidf2_ab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "32c28ac2",
   "metadata": {
    "scrolled": true
   },
   "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>AdaBoost1-TFIDF</th>\n",
       "      <th>AdaBoost2-TFIDF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.914</td>\n",
       "      <td>0.910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.930</td>\n",
       "      <td>0.925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.970</td>\n",
       "      <td>0.970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1 Score</th>\n",
       "      <td>0.950</td>\n",
       "      <td>0.947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           AdaBoost1-TFIDF  AdaBoost2-TFIDF\n",
       "Accuracy             0.914            0.910\n",
       "Precision            0.930            0.925\n",
       "Recall               0.970            0.970\n",
       "F1 Score             0.950            0.947"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compile all of the error metrics into a dataframe for comparison\n",
    "results = pd.DataFrame(list(zip(cm5, cm6)))\n",
    "results = results.set_index([['Accuracy', 'Precision', 'Recall', 'F1 Score']])\n",
    "results.columns = ['AdaBoost1-TFIDF', 'AdaBoost2-TFIDF']\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08b4ae3b",
   "metadata": {},
   "source": [
    "### 4 - SVM With TF-IDF Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cb45517b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Support Vector Machines with Scikit-learn for first model\n",
    "from sklearn import svm\n",
    "svm = svm.SVC(probability=True)\n",
    "\n",
    "svm.fit(X_train_tfidf1, y_train)\n",
    "\n",
    "y_pred_tfidf1_sv= svm.predict(X_test_tfidf1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bf509741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here's the heat map for the first Ada Boost model\n",
    "cm7 = conf_matrix(y_test, y_pred_tfidf1_sv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "876ff381",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Support Vector Machines with Scikit-learn for 2nd model\n",
    "from sklearn import svm\n",
    "svm = svm.SVC(probability=True)\n",
    "\n",
    "svm.fit(X_train_tfidf2, y_train)\n",
    "\n",
    "y_pred_tfidf2_sv= svm.predict(X_test_tfidf2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fc3ff128",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here's the heat map for the 2nd Ada Boost model\n",
    "cm8 = conf_matrix(y_test, y_pred_tfidf2_sv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "420c49d7",
   "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>SVM1-TFIDF</th>\n",
       "      <th>SVM2-TFIDF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.949</td>\n",
       "      <td>0.922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.952</td>\n",
       "      <td>0.916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.988</td>\n",
       "      <td>0.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1 Score</th>\n",
       "      <td>0.970</td>\n",
       "      <td>0.955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           SVM1-TFIDF  SVM2-TFIDF\n",
       "Accuracy        0.949       0.922\n",
       "Precision       0.952       0.916\n",
       "Recall          0.988       0.997\n",
       "F1 Score        0.970       0.955"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compile all of the error metrics into a dataframe for comparison\n",
    "results = pd.DataFrame(list(zip(cm7, cm8)))\n",
    "results = results.set_index([['Accuracy', 'Precision', 'Recall', 'F1 Score']])\n",
    "results.columns = ['SVM1-TFIDF', 'SVM2-TFIDF']\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3e515ece",
   "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>LR1-TFIDF</th>\n",
       "      <th>LR2-TFIDF</th>\n",
       "      <th>Naive1-TFIDF</th>\n",
       "      <th>Naive2-TFIDF</th>\n",
       "      <th>AdaBoost1-TFIDF</th>\n",
       "      <th>AdaBoost2-TFIDF</th>\n",
       "      <th>SVM1-TFIDF</th>\n",
       "      <th>SVM2-TFIDF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.944</td>\n",
       "      <td>0.905</td>\n",
       "      <td>0.832</td>\n",
       "      <td>0.829</td>\n",
       "      <td>0.914</td>\n",
       "      <td>0.910</td>\n",
       "      <td>0.949</td>\n",
       "      <td>0.922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.943</td>\n",
       "      <td>0.897</td>\n",
       "      <td>0.831</td>\n",
       "      <td>0.830</td>\n",
       "      <td>0.930</td>\n",
       "      <td>0.925</td>\n",
       "      <td>0.952</td>\n",
       "      <td>0.916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.991</td>\n",
       "      <td>0.999</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.997</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.988</td>\n",
       "      <td>0.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1 Score</th>\n",
       "      <td>0.966</td>\n",
       "      <td>0.945</td>\n",
       "      <td>0.908</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.950</td>\n",
       "      <td>0.947</td>\n",
       "      <td>0.970</td>\n",
       "      <td>0.955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           LR1-TFIDF  LR2-TFIDF  Naive1-TFIDF  Naive2-TFIDF  AdaBoost1-TFIDF  \\\n",
       "Accuracy       0.944      0.905         0.832         0.829            0.914   \n",
       "Precision      0.943      0.897         0.831         0.830            0.930   \n",
       "Recall         0.991      0.999         1.000         0.997            0.970   \n",
       "F1 Score       0.966      0.945         0.908         0.906            0.950   \n",
       "\n",
       "           AdaBoost2-TFIDF  SVM1-TFIDF  SVM2-TFIDF  \n",
       "Accuracy             0.910       0.949       0.922  \n",
       "Precision            0.925       0.952       0.916  \n",
       "Recall               0.970       0.988       0.997  \n",
       "F1 Score             0.947       0.970       0.955  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compile all of the error metrics into a dataframe for comparison\n",
    "results_tf = pd.DataFrame(list(zip(cm1, cm2, cm3, cm4,cm5, cm6, cm7, cm8)))\n",
    "results_tf = results_tf.set_index([['Accuracy', 'Precision', 'Recall', 'F1 Score']])\n",
    "results_tf.columns = ['LR1-TFIDF', 'LR2-TFIDF','Naive1-TFIDF', 'Naive2-TFIDF', 'AdaBoost1-TFIDF', 'AdaBoost2-TFIDF','SVM1-TFIDF', 'SVM2-TFIDF']\n",
    "results_tf\n",
    "\n",
    "results = pd.concat([results_tf], axis=1)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b347d02",
   "metadata": {},
   "source": [
    "By using TF-IDF we able to see that the recall and precision of the first SVM model still outperforms the other models.\n",
    "\n",
    "Overall, the first SVM model (using stop words) seems to best classify positive and negative Trip Advisor reviews."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "54da1151",
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving the data\n",
    "data.to_csv('Trip_Advisor_2nd.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ee53a089",
   "metadata": {},
   "outputs": [],
   "source": [
    "#saving df1\n",
    "df1.to_csv('Trip_df1.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "222f94c5",
   "metadata": {},
   "source": [
    "Now, we will start Topic Modeling in new jupyter"
   ]
  }
 ],
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