3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install nltk\n",
    "# !pip install wordcloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gEOgEkSOG2s3"
   },
   "source": [
    "# Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "kCQMxr852yrE"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from os import path\n",
    "import time\n",
    "import calendar\n",
    "import seaborn as sns\n",
    "sns.set()\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "import csv,warnings\n",
    "  \n",
    "warnings.filterwarnings(action = 'ignore')\n",
    "  \n",
    "import gensim\n",
    "from gensim.models import Word2Vec\n",
    "\n",
    "\n",
    "#from wordcloud import WordCloud, STOPWORDS\n",
    "\n",
    "#NLTK\n",
    "from nltk import ngrams\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.stem import PorterStemmer\n",
    "\n",
    "#Feature Extraction\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "\n",
    "#Model Training\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, recall_score, precision_score, f1_score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wpKD_So7G-FY"
   },
   "source": [
    "# Importing CSV files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LeRSkBPZ28rQ",
    "outputId": "37704462-d8ca-4f39-b7d4-f2f9cd01912b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\VATSALA NEMA\\AppData\\Local\\Temp\\ipykernel_9924\\2517431560.py:1: FutureWarning: The error_bad_lines argument has been deprecated and will be removed in a future version. Use on_bad_lines in the future.\n",
      "\n",
      "\n",
      "  train_df=pd.read_csv(\"C:/Users/VATSALA NEMA/Documents/SEM 7/NLP 2022/Project- Sarcasm detection in Soc med/Dataset/project1/project_training_data_with_class_labels.csv\" , error_bad_lines=False)\n"
     ]
    }
   ],
   "source": [
    "train_df=pd.read_csv(\"C:/Users/VATSALA NEMA/Documents/SEM 7/NLP 2022/Project- Sarcasm detection in Soc med/Dataset/project1/project_training_data_with_class_labels.csv\" , error_bad_lines=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "Hk_ufL1G3Etx",
    "outputId": "05468c46-6398-4018-cf51-53e83126fe38"
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Comments</th>\n",
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       "      <th>Class Labels</th>\n",
       "      <th>Unnamed: 4</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ocxtitan</td>\n",
       "      <td>Central Illinois</td>\n",
       "      <td>Jesus; where do you live?</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LeChuckly</td>\n",
       "      <td>To think - CNN used to be the acronym synonymo...</td>\n",
       "      <td>Even The CNN Staff Is Sick Of The Wall-To-Wall...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>throwitskrub8</td>\n",
       "      <td>But then again; you have to consider that all ...</td>\n",
       "      <td>agree to that part.It can also mean that gujra...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fresherthanyouuu</td>\n",
       "      <td>ughhhhh</td>\n",
       "      <td>If a guy told you he doesn't use social media ...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>_kushagra</td>\n",
       "      <td>I should've put the</td>\n",
       "      <td>No; it's just a programming bug. After all; th...</td>\n",
       "      <td>sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808098</th>\n",
       "      <td>Arectarius</td>\n",
       "      <td>50% hp/def elgif; impiety orb; bourn jewel che...</td>\n",
       "      <td>Hate to break it to you; but Gloomy can't do a...</td>\n",
       "      <td>sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808099</th>\n",
       "      <td>dale1v</td>\n",
       "      <td>Rubicon Mango.</td>\n",
       "      <td>Hey Reddit; if you could only drink one flavor...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808100</th>\n",
       "      <td>Semyonov</td>\n",
       "      <td>Essentially GAP insurance right?</td>\n",
       "      <td>I'm sure you can pay for it. Most insurance pl...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808101</th>\n",
       "      <td>futalover99</td>\n",
       "      <td>Yeah; but sometimes that weird kid keeps tryin...</td>\n",
       "      <td>Ive been subscribed to that for a while; and a...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808102</th>\n",
       "      <td>SuperSpartacus</td>\n",
       "      <td>Petroleum doesn't really require a terroir lik...</td>\n",
       "      <td>It must be very different for oil then. I know...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>808103 rows × 5 columns</p>\n",
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      ],
      "text/plain": [
       "                      ID                                           Comments  \\\n",
       "0               ocxtitan                                   Central Illinois   \n",
       "1              LeChuckly  To think - CNN used to be the acronym synonymo...   \n",
       "2          throwitskrub8  But then again; you have to consider that all ...   \n",
       "3       fresherthanyouuu                                            ughhhhh   \n",
       "4              _kushagra                                I should've put the   \n",
       "...                  ...                                                ...   \n",
       "808098        Arectarius  50% hp/def elgif; impiety orb; bourn jewel che...   \n",
       "808099            dale1v                                     Rubicon Mango.   \n",
       "808100          Semyonov                   Essentially GAP insurance right?   \n",
       "808101       futalover99  Yeah; but sometimes that weird kid keeps tryin...   \n",
       "808102    SuperSpartacus  Petroleum doesn't really require a terroir lik...   \n",
       "\n",
       "                                          Parent Comments  Class Labels   \\\n",
       "0                               Jesus; where do you live?  non-sarcastic   \n",
       "1       Even The CNN Staff Is Sick Of The Wall-To-Wall...  non-sarcastic   \n",
       "2       agree to that part.It can also mean that gujra...  non-sarcastic   \n",
       "3       If a guy told you he doesn't use social media ...  non-sarcastic   \n",
       "4       No; it's just a programming bug. After all; th...      sarcastic   \n",
       "...                                                   ...            ...   \n",
       "808098  Hate to break it to you; but Gloomy can't do a...      sarcastic   \n",
       "808099  Hey Reddit; if you could only drink one flavor...  non-sarcastic   \n",
       "808100  I'm sure you can pay for it. Most insurance pl...  non-sarcastic   \n",
       "808101  Ive been subscribed to that for a while; and a...  non-sarcastic   \n",
       "808102  It must be very different for oil then. I know...  non-sarcastic   \n",
       "\n",
       "       Unnamed: 4  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "...           ...  \n",
       "808098        NaN  \n",
       "808099        NaN  \n",
       "808100        NaN  \n",
       "808101        NaN  \n",
       "808102        NaN  \n",
       "\n",
       "[808103 rows x 5 columns]"
      ]
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "train_df "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "02CAARD7AhJv"
   },
   "source": [
    "# Cleaning the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "D_ytyOe33aui"
   },
   "outputs": [],
   "source": [
    "df1 = train_df[train_df.isna().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 175
    },
    "id": "vGB6UGwDAYsr",
    "outputId": "88d96206-00ac-4605-9a9b-bb51d8141d52"
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       "      <td>ocxtitan</td>\n",
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       "      <td>Jesus; where do you live?</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LeChuckly</td>\n",
       "      <td>To think - CNN used to be the acronym synonymo...</td>\n",
       "      <td>Even The CNN Staff Is Sick Of The Wall-To-Wall...</td>\n",
       "      <td>non-sarcastic</td>\n",
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       "      <th>2</th>\n",
       "      <td>throwitskrub8</td>\n",
       "      <td>But then again; you have to consider that all ...</td>\n",
       "      <td>agree to that part.It can also mean that gujra...</td>\n",
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       "      <td>fresherthanyouuu</td>\n",
       "      <td>ughhhhh</td>\n",
       "      <td>If a guy told you he doesn't use social media ...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>_kushagra</td>\n",
       "      <td>I should've put the</td>\n",
       "      <td>No; it's just a programming bug. After all; th...</td>\n",
       "      <td>sarcastic</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>808098</th>\n",
       "      <td>Arectarius</td>\n",
       "      <td>50% hp/def elgif; impiety orb; bourn jewel che...</td>\n",
       "      <td>Hate to break it to you; but Gloomy can't do a...</td>\n",
       "      <td>sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808099</th>\n",
       "      <td>dale1v</td>\n",
       "      <td>Rubicon Mango.</td>\n",
       "      <td>Hey Reddit; if you could only drink one flavor...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808100</th>\n",
       "      <td>Semyonov</td>\n",
       "      <td>Essentially GAP insurance right?</td>\n",
       "      <td>I'm sure you can pay for it. Most insurance pl...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808101</th>\n",
       "      <td>futalover99</td>\n",
       "      <td>Yeah; but sometimes that weird kid keeps tryin...</td>\n",
       "      <td>Ive been subscribed to that for a while; and a...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>808102</th>\n",
       "      <td>SuperSpartacus</td>\n",
       "      <td>Petroleum doesn't really require a terroir lik...</td>\n",
       "      <td>It must be very different for oil then. I know...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>808093 rows × 5 columns</p>\n",
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      "text/plain": [
       "                      ID                                           Comments  \\\n",
       "0               ocxtitan                                   Central Illinois   \n",
       "1              LeChuckly  To think - CNN used to be the acronym synonymo...   \n",
       "2          throwitskrub8  But then again; you have to consider that all ...   \n",
       "3       fresherthanyouuu                                            ughhhhh   \n",
       "4              _kushagra                                I should've put the   \n",
       "...                  ...                                                ...   \n",
       "808098        Arectarius  50% hp/def elgif; impiety orb; bourn jewel che...   \n",
       "808099            dale1v                                     Rubicon Mango.   \n",
       "808100          Semyonov                   Essentially GAP insurance right?   \n",
       "808101       futalover99  Yeah; but sometimes that weird kid keeps tryin...   \n",
       "808102    SuperSpartacus  Petroleum doesn't really require a terroir lik...   \n",
       "\n",
       "                                          Parent Comments  Class Labels   \\\n",
       "0                               Jesus; where do you live?  non-sarcastic   \n",
       "1       Even The CNN Staff Is Sick Of The Wall-To-Wall...  non-sarcastic   \n",
       "2       agree to that part.It can also mean that gujra...  non-sarcastic   \n",
       "3       If a guy told you he doesn't use social media ...  non-sarcastic   \n",
       "4       No; it's just a programming bug. After all; th...      sarcastic   \n",
       "...                                                   ...            ...   \n",
       "808098  Hate to break it to you; but Gloomy can't do a...      sarcastic   \n",
       "808099  Hey Reddit; if you could only drink one flavor...  non-sarcastic   \n",
       "808100  I'm sure you can pay for it. Most insurance pl...  non-sarcastic   \n",
       "808101  Ive been subscribed to that for a while; and a...  non-sarcastic   \n",
       "808102  It must be very different for oil then. I know...  non-sarcastic   \n",
       "\n",
       "       Unnamed: 4  \n",
       "0             NaN  \n",
       "1             NaN  \n",
       "2             NaN  \n",
       "3             NaN  \n",
       "4             NaN  \n",
       "...           ...  \n",
       "808098        NaN  \n",
       "808099        NaN  \n",
       "808100        NaN  \n",
       "808101        NaN  \n",
       "808102        NaN  \n",
       "\n",
       "[808093 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "dHu7ilBxAl0i"
   },
   "outputs": [],
   "source": [
    "df2= train_df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "6kWDOg6cCLtD"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\VATSALA NEMA\\AppData\\Local\\Temp\\ipykernel_9924\\3756746671.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2.rename(columns = {' Class Labels ':'label'}, inplace = True)\n"
     ]
    }
   ],
   "source": [
    "df2.rename(columns = {' Class Labels ':'label'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "KpWM6NYqBFuW"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\VATSALA NEMA\\AppData\\Local\\Temp\\ipykernel_9924\\1160797125.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df2['label'].replace(['non-sarcastic','sarcastic'],[1,0], inplace = True)\n"
     ]
    }
   ],
   "source": [
    "df2['label'].replace(['non-sarcastic','sarcastic'],[1,0], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explorartory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "bumL72PcDAht",
    "outputId": "dc48720b-00dd-4b03-b081-6d6af75b30b3"
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   "outputs": [
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       "      <th>ID</th>\n",
       "      <th>Comments</th>\n",
       "      <th>Parent Comments</th>\n",
       "      <th>label</th>\n",
       "      <th>Unnamed: 4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>72350</th>\n",
       "      <td>CatLions</td>\n",
       "      <td>heres some MATH for you fat + person = gross</td>\n",
       "      <td>Twice the size of everyone else = 3(2),non-sar...</td>\n",
       "      <td>Chaining help So i was dexnav chaining for a r...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104350</th>\n",
       "      <td>Nextasy</td>\n",
       "      <td>Ah yes; I remember grade seven.</td>\n",
       "      <td>Penis'',non-sarcastic\\nDemon9ne,Yeah; I rememb...</td>\n",
       "      <td>And I assume that's a big f you to all us peop...</td>\n",
       "      <td>sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202371</th>\n",
       "      <td>CTeam19</td>\n",
       "      <td>What is the funniest thing Coach Leech has don...</td>\n",
       "      <td>Guns Up Reddit !! I'm Coach Kliff Kingsbury AM...</td>\n",
       "      <td>When a young moderately attractive gentleman a...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207921</th>\n",
       "      <td>DanBRZ</td>\n",
       "      <td>Future Pro Bowl LB Ramon Humber now.</td>\n",
       "      <td>Patriots have reached a 1-year contract agreem...</td>\n",
       "      <td>Why is this so funny?</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309630</th>\n",
       "      <td>Eternally65</td>\n",
       "      <td>We surrender!</td>\n",
       "      <td>What a lot of scoundrels! That's it!! If they ...</td>\n",
       "      <td>What about some of the other races of the cove...</td>\n",
       "      <td>non-sarcastic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID                                           Comments  \\\n",
       "72350      CatLions       heres some MATH for you fat + person = gross   \n",
       "104350      Nextasy                    Ah yes; I remember grade seven.   \n",
       "202371      CTeam19  What is the funniest thing Coach Leech has don...   \n",
       "207921       DanBRZ               Future Pro Bowl LB Ramon Humber now.   \n",
       "309630  Eternally65                                      We surrender!   \n",
       "\n",
       "                                          Parent Comments  \\\n",
       "72350   Twice the size of everyone else = 3(2),non-sar...   \n",
       "104350  Penis'',non-sarcastic\\nDemon9ne,Yeah; I rememb...   \n",
       "202371  Guns Up Reddit !! I'm Coach Kliff Kingsbury AM...   \n",
       "207921  Patriots have reached a 1-year contract agreem...   \n",
       "309630  What a lot of scoundrels! That's it!! If they ...   \n",
       "\n",
       "                                                    label     Unnamed: 4  \n",
       "72350   Chaining help So i was dexnav chaining for a r...  non-sarcastic  \n",
       "104350  And I assume that's a big f you to all us peop...      sarcastic  \n",
       "202371  When a young moderately attractive gentleman a...  non-sarcastic  \n",
       "207921                              Why is this so funny?  non-sarcastic  \n",
       "309630  What about some of the other races of the cove...  non-sarcastic  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Iv4UuycbUYgL",
    "outputId": "8cb2f14d-0750-4d72-b357-fe855c740f62"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10 entries, 72350 to 790508\n",
      "Data columns (total 5 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   ID               10 non-null     object\n",
      " 1   Comments         10 non-null     object\n",
      " 2   Parent Comments  10 non-null     object\n",
      " 3   label            10 non-null     object\n",
      " 4   Unnamed: 4       10 non-null     object\n",
      "dtypes: object(5)\n",
      "memory usage: 480.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df2.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8yFX6sseGms9"
   },
   "source": [
    "**Exploratory Data Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "jB7HGEorDcvH",
    "outputId": "8409445b-f751-4a81-cb78-f2220d43e05f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Chaining help So i was dexnav chaining for a ralts on r102 and then i got the thing \"cannot be found in the area or whatever\" so then i start walking around to reset it and thrn a wild lillipup shows up so i sneak up to it assuming that if i dont battle it it will brwka my chain. Now im in battle with it right now but its level 17 (it was my 20th chain) does incountering a different wild pokemon reset my chain? thanks.    1\n",
       "And I assume that's a big f you to all us people of color; gays; and women who voted for her instead of Bernie because we don't believe in his phoney promises; he has no history with us; and just joined the Democratic Party to us it to run; trash it and its leader; and leave again.                                                                                                                                                1\n",
       "When a young moderately attractive gentleman appears and says with a quivering voice \"Hey how are you? :}\" (x-post from r/justneckbeardthings)                                                                                                                                                                                                                                                                                            1\n",
       "Why is this so funny?                                                                                                                                                                                                                                                                                                                                                                                                                     1\n",
       "What about some of the other races of the covenant?                                                                                                                                                                                                                                                                                                                                                                                       1\n",
       "Played Skyrim for the first time...pretty sure this wasn't supposed to happen.                                                                                                                                                                                                                                                                                                                                                            1\n",
       "I want that PR manager                                                                                                                                                                                                                                                                                                                                                                                                                    1\n",
       "Israeli naval ship 'bombs Palestinian children on Gaza beach'; killing four... A journalist who saw the incident said some of the children who survived were running away when another shell seemed to be aimed at them.                                                                                                                                                                                                                  1\n",
       "If I made money per line of code; I would use the enter key more.                                                                                                                                                                                                                                                                                                                                                                         1\n",
       "Both Hitler and Stalin had mustaches. Did they fight in the name of mustaches? (Not original)                                                                                                                                                                                                                                                                                                                                             1\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to check number of sarcastic and non-sarcastic comments\n",
    "df2['label'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uylIXRBuBEdt"
   },
   "source": [
    "Almost equal number of sarcastic and non-sarcastic comments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yRYnQalNFYU4",
    "outputId": "1a97f4bf-1365-480f-d030-a864071ae966"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chaining help So i was dexnav chaining for a ralts on r102 and then i got the thing \"cannot be found in the area or whatever\" so then i start walking around to reset it and thrn a wild lillipup shows up so i sneak up to it assuming that if i dont battle it it will brwka my chain. Now im in battle with it right now but its level 17 (it was my 20th chain) does incountering a different wild pokemon reset my chain? thanks.    0.1\n",
      "And I assume that's a big f you to all us people of color; gays; and women who voted for her instead of Bernie because we don't believe in his phoney promises; he has no history with us; and just joined the Democratic Party to us it to run; trash it and its leader; and leave again.                                                                                                                                                0.1\n",
      "When a young moderately attractive gentleman appears and says with a quivering voice \"Hey how are you? :}\" (x-post from r/justneckbeardthings)                                                                                                                                                                                                                                                                                            0.1\n",
      "Why is this so funny?                                                                                                                                                                                                                                                                                                                                                                                                                     0.1\n",
      "What about some of the other races of the covenant?                                                                                                                                                                                                                                                                                                                                                                                       0.1\n",
      "Played Skyrim for the first time...pretty sure this wasn't supposed to happen.                                                                                                                                                                                                                                                                                                                                                            0.1\n",
      "I want that PR manager                                                                                                                                                                                                                                                                                                                                                                                                                    0.1\n",
      "Israeli naval ship 'bombs Palestinian children on Gaza beach'; killing four... A journalist who saw the incident said some of the children who survived were running away when another shell seemed to be aimed at them.                                                                                                                                                                                                                  0.1\n",
      "If I made money per line of code; I would use the enter key more.                                                                                                                                                                                                                                                                                                                                                                         0.1\n",
      "Both Hitler and Stalin had mustaches. Did they fight in the name of mustaches? (Not original)                                                                                                                                                                                                                                                                                                                                             0.1\n",
      "Name: label, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Checking percentage of sarcastic and non-sarcastic comments\n",
    "print(df2['label'].value_counts()/len(df2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 350
    },
    "id": "B_pE94BtBSuP",
    "outputId": "2bdb5535-3d8d-4fca-9597-7e711512739c"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting percentage of sarcastic and non-sarcastic comments\n",
    "plt.figure(figsize=(5,5))\n",
    "ax = sns.countplot(x='label',  data= df2)\n",
    "ax.set(title = \"Distribution of Classes\", xlabel=\"Sarcasm Status\", ylabel = \"Total Count\")\n",
    "total = float(len(train_df))\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.text(p.get_x()+p.get_width()/2.,\n",
    "            height + 3,\n",
    "            '{:1.1f}%'.format((height/total)*100),\n",
    "            ha=\"center\") \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 281
    },
    "id": "hO5UX2J10MMb",
    "outputId": "d33ec26d-d223-4a3b-c602-17e2201577ea"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To check if there is any relation between the length of the comment and its label\n",
    "df2.loc[df2['label']==1,'Comments'].str.len().apply(np.log1p).hist(label='sarcastic', alpha=.5)\n",
    "df2.loc[df2['label']==0,'Comments'].str.len().apply(np.log1p).hist(label='non-sarcastic', alpha=.5)\n",
    "plt.legend()\n",
    "plt.title('Natural Log Length of Comments')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_9o3RzVnd70t"
   },
   "source": [
    "Sarcastic comments are normally distributed in length. Non-sarcastic comments has a right-skew."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 386
    },
    "id": "wOkkCnR6B939",
    "outputId": "fd76ec42-f99d-4da9-f9cd-0d9affd76693"
   },
   "source": [
    "Finding the most common words in the sarcastic comments using wordclous\n",
    "wordcloud = WordCloud(background_color='black', stopwords = STOPWORDS,\n",
    "                max_words = 200, max_font_size = 100, \n",
    "                random_state = 17, width=800, height=400)\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "wordcloud.generate(str(df2.loc[df2['label'] == 1, 'Parent Comments']))\n",
    "plt.grid(b= False)\n",
    "plt.imshow(wordcloud);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "G6sZoOaLCuDl"
   },
   "outputs": [],
   "source": [
    "# Comparing sarcastic comments with Parent comments\n",
    "sarcasm_comm_len = np.array(df2.loc[df2['label'] == 1]['Comments'].str.len())\n",
    "parent_comm_len = np.array(df2.loc[df2['label'] == 1]['Parent Comments'].str.len())\n",
    "ratio_len = np.array((df2.loc[df2['label'] == 1]['Comments'].str.len())/(df2.loc[df2['label'] == 1]['Parent Comments'].str.len()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gRR1wNN9DIFw"
   },
   "outputs": [],
   "source": [
    "d = pd.DataFrame({'Comment Length': sarcasm_comm_len, 'Parent Comment Length': parent_comm_len, 'Ratio Length': ratio_len}, columns=['Comment Length', 'Parent Comment Length', 'Ratio Length'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "c8bNU05FDQeU",
    "outputId": "2ffca103-5cd7-4b6e-8916-762aaba0c5be"
   },
   "outputs": [],
   "source": [
    "ax = plt.axes()\n",
    "sns.scatterplot(data=d, x=\"Comment Length\", y=\"Parent Comment Length\",  size=ratio_len)\n",
    "ax.set_title(\"Comparing Sarcastic Comment Length with Parent Comment\")\n",
    "# control x and y limits\n",
    "plt.ylim(0, 12000)\n",
    "plt.xlim(0, 800)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We find that parent comment length and comment length of sarcastic comments is almost same and there is no new information revealed with parent comments. So, we will use 'Comments' in further analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Bag of Words Model***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To look at the most frequent words in the Comments using bag of words model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer_1 = CountVectorizer(stop_words='english', ngram_range=(1, 1)) #unigram\n",
    "vectorizer_2 = CountVectorizer(stop_words='english', ngram_range=(2, 2)) #bigram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#vectorizer_1 = CountVectorizer(ngram_range=(1, 1)) #unigram\n",
    "#vectorizer_2 = CountVectorizer (ngram_range=(2, 2)) #bigram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to calculate frequency of words in comments\n",
    "def freq_words(vectorizer, data):\n",
    "    X = vectorizer.fit_transform(data)\n",
    "    freqs = zip(vectorizer.get_feature_names(), np.asarray(X.sum(axis=0)).ravel())\n",
    "    return sorted(freqs, key = lambda x: x[1], reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For unigrams from comments\n",
    "l = [freq_words(vectorizer_1, df2[df2['label']==1]['Comments']),\n",
    "     freq_words(vectorizer_1, df2[df2['label']==0]['Comments'])]\n",
    "list(map(list, zip(*l)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the first column shows sarcastic comments and second column shows non-sarcastic comments. Since we have not removed stopwords, we are geeting a lot of stopwords as unigrams here. Also, Unigramms from the sarcastic comments are mostly same as from the non-sarcastic comments, hence these unigramms do not help in seprating sarcastic comments from non-sarcastic comments.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#bigrams\n",
    "l = [freq_words(vectorizer_2, df2[df2['label']==1]['Comments']),\n",
    "     freq_words(vectorizer_2, df2[df2['label']==0]['Comments'])]\n",
    "list(map(list, zip(*l)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see that the  bigramms vectorizer shows us better (more specific) results as compared to unigrams."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "I9Ks4VjwDoFp"
   },
   "outputs": [],
   "source": [
    "df2 = df2[:5000] #RAM crashes if we take more dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "SF7pD1UxEPN_"
   },
   "outputs": [],
   "source": [
    "corpus = df2['Comments'].values.astype('U') # converting to unicode because system unable to identify special characters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vYUakysLvpYJ",
    "outputId": "dec8e1c3-185d-4902-ff3f-bee88e707b87"
   },
   "outputs": [],
   "source": [
    "corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "oNLdJXV83i_r"
   },
   "outputs": [],
   "source": [
    "labels = df2['label'].to_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Tf-idf Vectorizer***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "miyEGChGu7lM",
    "outputId": "e9b89b68-c4cb-40d5-aeb1-4d9c44011d51"
   },
   "outputs": [],
   "source": [
    "#vectorizer = TfidfVectorizer(stop_words='english', ngram_range=(1,1))\n",
    "vectorizer = TfidfVectorizer(ngram_range=(1,1))\n",
    "\n",
    "tfidf = vectorizer.fit_transform(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf = tfidf.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MWkkhY9Ww7N3",
    "outputId": "3b8f9ed3-ea78-460d-d14e-1222668376ac"
   },
   "outputs": [],
   "source": [
    "# cv_dataframe=pd.DataFrame(X.toarray(),columns=vectorizer.get_feature_names())\n",
    "# #print(cv_dataframe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Word Embeddings**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1=open('C:/Users/VATSALA NEMA/Documents/SEM 7/NLP 2022/Project- Sarcasm detection in Soc med/Dataset/project1/project_training_data_with_class_labels.csv')\n",
    "#f1['Class Labels'].replace(['non-sarcastic','sarcastic'],[1,0], inplace = True)\n",
    "reader = list(csv.reader(f1, delimiter='\\t'))\n",
    "text=''\n",
    "labels=[]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ID,Comments,Parent Comments, Class Labels ,']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reader[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Input \u001b[1;32mIn [11]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m      5\u001b[0m         labels\u001b[38;5;241m.\u001b[39mappend(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m----> 6\u001b[0m     text\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39mitem[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m      8\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sentence \u001b[38;5;129;01min\u001b[39;00m sent_tokenize(text):\n\u001b[0;32m      9\u001b[0m     words \u001b[38;5;241m=\u001b[39m []    \n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "for item in reader:\n",
    "    if item[0]=='Sarcastic':\n",
    "        labels.append(1)\n",
    "    else:\n",
    "        labels.append(0)\n",
    "    text+=item[0]+'. '\n",
    "\n",
    "for sentence in sent_tokenize(text):\n",
    "    words = []    \n",
    "    for word in word_tokenize(sentence):  # tokenize the sentence into words\n",
    "        words.append(word.lower()) \n",
    "    data.append(words)\n",
    "\n",
    "model = Word2Vec(words, min_count=1)\n",
    "\n",
    "print(model)\n",
    "\n",
    "words2=list(model.wv.vocab)\n",
    "print(words2)\n",
    "\n",
    "print(model['sentence'])\n",
    "\n",
    "model.save('model.bin')\n",
    "\n",
    "new_model=Word2Vec.load('model.bin')\n",
    "print(new_model)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Visualizing the word vectors\n",
    "X = model[model.wv.vocab]\n",
    "\n",
    "# fit a 2d PCA model to the vectors\n",
    "pca = PCA(n_components=2)\n",
    "result = pca.fit_transform(X)\n",
    "\n",
    "# create a scatter plot of the projection\n",
    "pyplot.scatter(result[:, 0], result[:, 1])\n",
    "\n",
    "for i, word in enumerate(words2):\n",
    "\tplt.annotate(word, xy=(result[i, 0], result[i, 1]))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "not enough values to unpack (expected 2, got 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[1;32mIn [12]\u001b[0m, in \u001b[0;36m<cell line: 4>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgensim\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m KeyedVectors\n\u001b[0;32m      3\u001b[0m filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mGoogleNews-vectors-negative300.bin\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m----> 4\u001b[0m model \u001b[38;5;241m=\u001b[39m \u001b[43mKeyedVectors\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_word2vec_format\u001b[49m\u001b[43m(\u001b[49m\u001b[43mf1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbinary\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\Apps\\Conda\\lib\\site-packages\\gensim\\models\\keyedvectors.py:1723\u001b[0m, in \u001b[0;36mKeyedVectors.load_word2vec_format\u001b[1;34m(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype, no_header)\u001b[0m\n\u001b[0;32m   1676\u001b[0m \u001b[38;5;129m@classmethod\u001b[39m\n\u001b[0;32m   1677\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_word2vec_format\u001b[39m(\n\u001b[0;32m   1678\u001b[0m         \u001b[38;5;28mcls\u001b[39m, fname, fvocab\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, binary\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, encoding\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf8\u001b[39m\u001b[38;5;124m'\u001b[39m, unicode_errors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstrict\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[0;32m   1679\u001b[0m         limit\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, datatype\u001b[38;5;241m=\u001b[39mREAL, no_header\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m   1680\u001b[0m     ):\n\u001b[0;32m   1681\u001b[0m     \u001b[38;5;124;03m\"\"\"Load KeyedVectors from a file produced by the original C word2vec-tool format.\u001b[39;00m\n\u001b[0;32m   1682\u001b[0m \n\u001b[0;32m   1683\u001b[0m \u001b[38;5;124;03m    Warnings\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1721\u001b[0m \n\u001b[0;32m   1722\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1723\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_load_word2vec_format\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1724\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfvocab\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfvocab\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbinary\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbinary\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43municode_errors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43municode_errors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1725\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlimit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlimit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdatatype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdatatype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mno_header\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mno_header\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1726\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mC:\\Apps\\Conda\\lib\\site-packages\\gensim\\models\\keyedvectors.py:2063\u001b[0m, in \u001b[0;36m_load_word2vec_format\u001b[1;34m(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype, no_header, binary_chunk_size)\u001b[0m\n\u001b[0;32m   2061\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   2062\u001b[0m     header \u001b[38;5;241m=\u001b[39m utils\u001b[38;5;241m.\u001b[39mto_unicode(fin\u001b[38;5;241m.\u001b[39mreadline(), encoding\u001b[38;5;241m=\u001b[39mencoding)\n\u001b[1;32m-> 2063\u001b[0m     vocab_size, vector_size \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mint\u001b[39m(x) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m header\u001b[38;5;241m.\u001b[39msplit()]  \u001b[38;5;66;03m# throws for invalid file format\u001b[39;00m\n\u001b[0;32m   2064\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m limit:\n\u001b[0;32m   2065\u001b[0m     vocab_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(vocab_size, limit)\n",
      "\u001b[1;31mValueError\u001b[0m: not enough values to unpack (expected 2, got 0)"
     ]
    }
   ],
   "source": [
    "#Since, training our own word vectors is very time consuming, we train it on Google's pre-trained word2vec model\n",
    "from gensim.models import KeyedVectors\n",
    "filename = 'GoogleNews-vectors-negative300.bin'\n",
    "model = KeyedVectors.load_word2vec_format(words, binary=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Not removing stopwords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Training the model*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Logistic Regression*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "vMMdQu6m07ik"
   },
   "outputs": [],
   "source": [
    "# parameters = {'solver': [ 'lbfgs', 'liblinear', 'saga'], 'verbose': [0, 1, 2]}\n",
    "\n",
    "# # multinomial logistic regression a.k.a softmax classifier\n",
    "# logit = LogisticRegression(random_state= 42)\n",
    "\n",
    "# logit_classifier = GridSearchCV(logit, parameters, cv = 10, n_jobs= 4)\n",
    "\n",
    "# # sklearn's pipeline\n",
    "# tfidf_logit_pipeline = Pipeline([('vectorizer', vectorizer), \n",
    "# ('tfidf', TfidfTransformer(use_idf=True,smooth_idf=True)), ('logit_classifier', logit_classifier)])\n",
    "\n",
    "# start = time.time()\n",
    "# tfidf_logit_pipeline.fit(x_train, y_train)\n",
    "# end = time.time()\n",
    "# print(end - start) \n",
    "\n",
    "# print(logit_classifier.best_params_)\n",
    "# print(logit_classifier.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(corpus, labels, test_size=0.20, random_state=42,stratify=labels)\n",
    "#Classifier\n",
    "clf_lr=LogisticRegression(class_weight='balanced') \n",
    "clf_lr_parameters = {'clf__solver':('newton-cg','lbfgs','liblinear','saga')}    \n",
    "        \n",
    "#Feature Extraction\n",
    "pipeline = Pipeline([\n",
    "('vect', CountVectorizer(token_pattern=r'\\b\\w+\\b')),\n",
    "('tfidf', TfidfTransformer(use_idf=True,smooth_idf=True)),     \n",
    "('clf', clf_lr),]) \n",
    "\n",
    "\n",
    "#Classificaion\n",
    "parameters={**clf_lr_parameters} \n",
    "grid = GridSearchCV(pipeline,parameters,scoring='f1_micro',cv=10)          \n",
    "grid.fit(train_x,train_y)     \n",
    "clf= grid.best_estimator_  \n",
    "print('********* Best Set of Parameters ********* \\n\\n')\n",
    "print(clf)\n",
    "\n",
    "predicted = clf.predict(test_x)\n",
    "predicted =list(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "l5wuGryG7l9F",
    "outputId": "a46b22f1-ade2-4928-947e-b3907fb15214"
   },
   "outputs": [],
   "source": [
    "accuracy_lr1 = accuracy_score(test_y, predicted)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "PvNxigMA7ogp",
    "outputId": "509bffb5-49ad-4861-bb41-a8d0b21eae12"
   },
   "outputs": [],
   "source": [
    "# Getting the accuracy metric\n",
    "acc = accuracy_score(predicted, test_y)\n",
    "pre = precision_score(predicted, test_y, average = 'micro')\n",
    "rec = recall_score(predicted, test_y)\n",
    "f1 = f1_score(predicted, test_y)\n",
    "micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
    "\n",
    "print ('Model Performance Statistic Suite-1 (Logistic Regression): ')\n",
    "print ('Accuracy: ', acc)\n",
    "print ('Precision: ', pre)\n",
    "print ('Recall: ',rec)\n",
    "print ('F1 Score: ', f1)\n",
    "print('Micro f1: ', micro_f1)\n",
    "\n",
    "cm = confusion_matrix(test_y,predicted)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 413
    },
    "id": "lFhbO3zK7rA4",
    "outputId": "08cc04dd-9047-4fa7-9bd9-b8f0ed903b03"
   },
   "outputs": [],
   "source": [
    "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
    "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
    "ax.set_ylabel('Actual',fontsize=14)\n",
    "ax.set_xlabel('Predicted',fontsize=14)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Support Vector Machine*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(corpus, labels, test_size=0.20, random_state=42,stratify=labels)\n",
    "#Classifier\n",
    "\n",
    "clf_svm=SVC()\n",
    "\n",
    "clf_parameters = {'clf__kernel':('linear', 'poly'),#, 'rbf', 'sigmoid'),\n",
    "                  'clf__C' : (0,1),#,3,5,10)}    \n",
    "        \n",
    "#Feature Extraction\n",
    "pipeline = Pipeline([\n",
    "('vect', CountVectorizer(token_pattern=r'\\b\\w+\\b')),\n",
    "('tfidf', TfidfTransformer(use_idf=True,smooth_idf=True)),     \n",
    "('clf', clf_svm),]) \n",
    "    \n",
    "#Classificaion\n",
    "parameters={**clf_parameters} \n",
    "grid = GridSearchCV(pipeline,parameters,scoring='f1_micro',cv=10)          \n",
    "grid.fit(train_x,train_y)     \n",
    "clf= grid.best_estimator_  \n",
    "print('********* Best Set of Parameters ********* \\n\\n')\n",
    "print(clf)\n",
    "\n",
    "predicted = clf.predict(test_x)\n",
    "predicted =list(predicted)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_svm1 = accuracy_score(test_y, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the accuracy metric\n",
    "acc = accuracy_score(predicted, test_y)\n",
    "pre = precision_score(predicted, test_y, average = 'micro')\n",
    "rec = recall_score(predicted, test_y)\n",
    "f1 = f1_score(predicted, test_y)\n",
    "micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
    "\n",
    "print ('Model Performance Statistic Suite-1 (SVM): ')\n",
    "print ('Accuracy: ', acc)\n",
    "print ('Precision: ', pre)\n",
    "print ('Recall: ',rec)\n",
    "print ('F1 Score: ', f1)\n",
    "print('Micro f1: ', micro_f1)\n",
    "\n",
    "cm = confusion_matrix(predicted, test_y)\n",
    "\n",
    "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
    "print('Sensitivity : ', sensitivity )\n",
    "\n",
    "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
    "print('Specificity : ', specificity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
    "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
    "ax.set_ylabel('Actual',fontsize=14)\n",
    "ax.set_xlabel('Predicted',fontsize=14)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Multinomial Naive Bayes*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(corpus, labels, test_size=0.20, random_state=42,stratify=labels)\n",
    "#Classifier\n",
    "\n",
    "clf_mnb=MultinomialNB(fit_prior=True, class_prior=None)\n",
    "\n",
    "\n",
    "clf_parameters = {'clf__alpha':(0,1,2),}   \n",
    "        \n",
    "#Feature Extraction\n",
    "pipeline = Pipeline([\n",
    "('vect', CountVectorizer(token_pattern=r'\\b\\w+\\b')),\n",
    "('tfidf', TfidfTransformer(use_idf=True,smooth_idf=True)),     \n",
    "('clf', clf_mnb),]) \n",
    "    \n",
    "\n",
    "\n",
    "#Classificaion\n",
    "parameters={**clf_parameters} \n",
    "grid = GridSearchCV(pipeline,parameters,scoring='f1_micro',cv=10)          \n",
    "grid.fit(train_x,train_y)     \n",
    "clf= grid.best_estimator_  \n",
    "print('********* Best Set of Parameters ********* \\n\\n')\n",
    "print(clf)\n",
    "\n",
    "predicted = clf.predict(test_x)\n",
    "predicted =list(predicted)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_mnb1 = accuracy_score(test_y, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the accuracy metric\n",
    "acc = accuracy_score(predicted, test_y)\n",
    "pre = precision_score(predicted, test_y, average = 'micro')\n",
    "rec = recall_score(predicted, test_y)\n",
    "f1 = f1_score(predicted, test_y)\n",
    "micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
    "\n",
    "print ('Model Performance Statistic Suite-1 (MNB): ')\n",
    "print ('Accuracy: ', acc)\n",
    "print ('Precision: ', pre)\n",
    "print ('Recall: ',rec)\n",
    "print ('F1 Score: ', f1)\n",
    "print('Micro f1: ', micro_f1)\n",
    "\n",
    "cm = confusion_matrix(predicted, test_y)\n",
    "\n",
    "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
    "print('Sensitivity : ', sensitivity )\n",
    "\n",
    "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
    "print('Specificity : ', specificity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
    "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
    "ax.set_ylabel('Actual',fontsize=14)\n",
    "ax.set_xlabel('Predicted',fontsize=14)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print ('Training Accuracies for comments without removing stop-words : ')\n",
    "print ('Logistic Regression: ', accuracy_lr1)\n",
    "print ('Support Vector Machine: ', accuracy_svm1)\n",
    "print ('Multinomial Naive Bayes ', accuracy_mnb1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With stopwords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Logistic Regression*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer_1 = CountVectorizer(stop_words='english', ngram_range=(1, 1)) #unigram\n",
    "vectorizer_2 = CountVectorizer(stop_words='english', ngram_range=(2, 2)) #bigram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(stop_words='english', ngram_range=(1,1))\n",
    "\n",
    "X = vectorizer.fit_transform(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(corpus, labels, test_size=0.20, random_state=42,stratify=labels)\n",
    "#Classifier\n",
    "clf_lr=LogisticRegression(class_weight='balanced') \n",
    "clf_lr_parameters = {'clf__solver':('newton-cg','lbfgs','liblinear','saga')}    \n",
    "        \n",
    "#Feature Extraction\n",
    "pipeline = Pipeline([\n",
    "('vect', CountVectorizer(token_pattern=r'\\b\\w+\\b')),\n",
    "('tfidf', TfidfTransformer(use_idf=True,smooth_idf=True)),     \n",
    "('clf', clf_lr),]) \n",
    "\n",
    "feature_parameters = {\n",
    "\"vect__stop_words\": (None, 'english'),\n",
    "'vect__ngram_range': ((1,1),(1, 2)),  # Unigrams, Bigrams or Trigrams\n",
    "}\n",
    "\n",
    "#Classificaion\n",
    "parameters={**feature_parameters,**clf_lr_parameters} \n",
    "grid = GridSearchCV(pipeline,parameters,scoring='f1_micro',cv=10)          \n",
    "grid.fit(train_x,train_y)     \n",
    "clf= grid.best_estimator_  \n",
    "print('********* Best Set of Parameters ********* \\n\\n')\n",
    "print(clf)\n",
    "\n",
    "predicted = clf.predict(test_x)\n",
    "predicted =list(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_lr2 = accuracy_score(test_y, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
    "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
    "ax.set_ylabel('Actual',fontsize=14)\n",
    "ax.set_xlabel('Predicted',fontsize=14)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*SVM*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(corpus, labels, test_size=0.20, random_state=42,stratify=labels)\n",
    "#Classifier\n",
    "\n",
    "clf=SVC()\n",
    "\n",
    "clf_parameters = {'clf__kernel':('linear', 'poly'),#, 'rbf', 'sigmoid'),\n",
    "                  'clf__C' : (0,1),#,3,5,10),\n",
    "                  }    \n",
    "        \n",
    "#Feature Extraction\n",
    "pipeline = Pipeline([\n",
    "('vect', CountVectorizer(token_pattern=r'\\b\\w+\\b')),\n",
    "('tfidf', TfidfTransformer(use_idf=True,smooth_idf=True)),     \n",
    "('clf', clf),]) \n",
    "\n",
    "feature_parameters = {\n",
    "\"vect__stop_words\": (None, 'english'),\n",
    "'vect__ngram_range': ((1,1),(1, 2)),  # Unigrams, Bigrams or Trigrams\n",
    "}\n",
    "\n",
    "#Classificaion\n",
    "parameters={**clf_parameters} \n",
    "grid = GridSearchCV(pipeline,parameters,scoring='f1_micro',cv=10)          \n",
    "grid.fit(train_x,train_y)     \n",
    "clf= grid.best_estimator_  \n",
    "print('********* Best Set of Parameters ********* \\n\\n')\n",
    "print(clf)\n",
    "\n",
    "predicted = clf.predict(test_x)\n",
    "predicted =list(predicted)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_svm2 = accuracy_score(test_y, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the accuracy metric\n",
    "acc = accuracy_score(predicted, test_y)\n",
    "pre = precision_score(predicted, test_y, average = 'micro')\n",
    "rec = recall_score(predicted, test_y)\n",
    "f1 = f1_score(predicted, test_y)\n",
    "micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
    "\n",
    "print ('Model Performance Statistic Suite-2 (MNB): ')\n",
    "print ('Accuracy: ', acc)\n",
    "print ('Precision: ', pre)\n",
    "print ('Recall: ',rec)\n",
    "print ('F1 Score: ', f1)\n",
    "print('Micro f1: ', micro_f1)\n",
    "\n",
    "cm = confusion_matrix(predicted, test_y)\n",
    "\n",
    "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
    "print('Sensitivity : ', sensitivity )\n",
    "\n",
    "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
    "print('Specificity : ', specificity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
    "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
    "ax.set_ylabel('Actual',fontsize=14)\n",
    "ax.set_xlabel('Predicted',fontsize=14)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Multinomial Naive Bayes*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(corpus, labels, test_size=0.20, random_state=42,stratify=labels)\n",
    "#Classifier\n",
    "\n",
    "clf=MultinomialNB(fit_prior=True, class_prior=None)\n",
    "\n",
    "\n",
    "clf_parameters = {'clf__alpha':(0,1,2),}   \n",
    "        \n",
    "#Feature Extraction\n",
    "pipeline = Pipeline([\n",
    "('vect', CountVectorizer(token_pattern=r'\\b\\w+\\b')),\n",
    "('tfidf', TfidfTransformer(use_idf=True,smooth_idf=True)),     \n",
    "('clf', clf),]) \n",
    "\n",
    "feature_parameters = {\n",
    "\"vect__stop_words\": (None, 'english'),\n",
    "'vect__ngram_range': ((1,1),(1, 2)),  # Unigrams, Bigrams or Trigrams\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "#Classificaion\n",
    "parameters={**feature_parameters,**clf_parameters} \n",
    "grid = GridSearchCV(pipeline,parameters,scoring='f1_micro',cv=10)          \n",
    "grid.fit(train_x,train_y)     \n",
    "clf= grid.best_estimator_  \n",
    "print('********* Best Set of Parameters ********* \\n\\n')\n",
    "print(clf)\n",
    "\n",
    "predicted = clf.predict(test_x)\n",
    "predicted =list(predicted)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_mnb2 = accuracy_score(test_y, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the accuracy metric\n",
    "acc = accuracy_score(predicted, test_y)\n",
    "pre = precision_score(predicted, test_y, average = 'micro')\n",
    "rec = recall_score(predicted, test_y)\n",
    "f1 = f1_score(predicted, test_y)\n",
    "micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
    "\n",
    "print ('Model Performance Statistic Suite-2 (MNB): ')\n",
    "print ('Accuracy: ', acc)\n",
    "print ('Precision: ', pre)\n",
    "print ('Recall: ',rec)\n",
    "print ('F1 Score: ', f1)\n",
    "print('Micro f1: ', micro_f1)\n",
    "\n",
    "cm = confusion_matrix(predicted, test_y)\n",
    "\n",
    "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
    "print('Sensitivity : ', sensitivity )\n",
    "\n",
    "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
    "print('Specificity : ', specificity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
    "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
    "ax.set_ylabel('Actual',fontsize=14)\n",
    "ax.set_xlabel('Predicted',fontsize=14)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print ('Training Accuracies for comments without removing stop-words : ')\n",
    "print ('Logistic Regression: ', accuracy_lr2)\n",
    "print ('Support Vector Machine: ', accuracy_svm2)\n",
    "print ('Multinomial Naive Bayes ', accuracy_mnb2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we applied stop words, number of words decreased and in most cases the meaning of the comments ight have also changed that's why the accuracy of all the 3 classifiers has decreased on removing stopwords."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for stemming words in comments\n",
    "analyzer = CountVectorizer().build_analyzer()\n",
    "def stemmed_words(doc):\n",
    "  return (PorterStemmer().stem(w) for w in analyzer(doc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Logistic Regression*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(tfidf, labels, test_size=0.20, random_state=42,stratify=labels)\n",
    "\n",
    "#classifier\n",
    "clf=LogisticRegression(random_state=42)  \n",
    "vectorizer=TfidfVectorizer(analyzer = stemmed_words, ngram_range=(1,1), token_pattern=r'\\b\\w+\\b')\n",
    "tfidf = vectorizer.fit_transform(corpus)\n",
    "tfidf = tfidf.toarray()\n",
    "\n",
    "#classification\n",
    "clf.fit(train_x,train_y)\n",
    "predicted = clf.predict(test_x)\n",
    "predicted =list(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_lr3 = accuracy_score(test_y, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the accuracy metric\n",
    "acc = accuracy_score(predicted, test_y)\n",
    "pre = precision_score(predicted, test_y, average = 'micro')\n",
    "rec = recall_score(predicted, test_y)\n",
    "f1 = f1_score(predicted, test_y)\n",
    "#micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
    "\n",
    "print ('Model Performance Statistic Suite-3 (LR): ')\n",
    "print ('Accuracy: ', acc)\n",
    "print ('Precision: ', pre)\n",
    "print ('Recall: ',rec)\n",
    "print ('F1 Score: ', f1)\n",
    "print('Micro f1: ', micro_f1)\n",
    "\n",
    "cm = confusion_matrix(predicted, test_y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
    "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
    "ax.set_ylabel('Actual',fontsize=14)\n",
    "ax.set_xlabel('Predicted',fontsize=14)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*SVM*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(tfidf, labels, test_size=0.20, random_state=42,stratify=labels)\n",
    "\n",
    "#classifier\n",
    "clf=SVC()\n",
    "vectorizer=TfidfVectorizer(analyzer = stemmed_words, ngram_range=(1,1), token_pattern=r'\\b\\w+\\b')\n",
    "tfidf = vectorizer.fit_transform(corpus)\n",
    "tfidf = tfidf.toarray()\n",
    "\n",
    "#classification\n",
    "clf.fit(train_x,train_y)\n",
    "predicted = clf.predict(test_x)\n",
    "predicted =list(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_svm3 = accuracy_score(test_y, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the accuracy metric\n",
    "acc = accuracy_score(predicted, test_y)\n",
    "pre = precision_score(predicted, test_y, average = 'micro')\n",
    "rec = recall_score(predicted, test_y)\n",
    "f1 = f1_score(predicted, test_y)\n",
    "micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
    "\n",
    "print ('Model Performance Statistic Suite-3 (SVM): ')\n",
    "print ('Accuracy: ', acc)\n",
    "print ('Precision: ', pre)\n",
    "print ('Recall: ',rec)\n",
    "print ('F1 Score: ', f1)\n",
    "print('Micro f1: ', micro_f1)\n",
    "\n",
    "cm = confusion_matrix(predicted, test_y)\n",
    "\n",
    "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
    "print('Sensitivity : ', sensitivity )\n",
    "\n",
    "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
    "print('Specificity : ', specificity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
    "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
    "ax.set_ylabel('Actual',fontsize=14)\n",
    "ax.set_xlabel('Predicted',fontsize=14)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Mutlinomial Naive Bayes*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x, test_x, train_y, test_y = train_test_split(tfidf, labels, test_size=0.20, random_state=42,stratify=labels)\n",
    "\n",
    "#classifier\n",
    "clf=MultinomialNB(alpha=0,fit_prior=True, class_prior=None)  \n",
    "vectorizer=TfidfVectorizer(analyzer = stemmed_words, ngram_range=(1,1), token_pattern=r'\\b\\w+\\b')\n",
    "tfidf = vectorizer.fit_transform(corpus)\n",
    "tfidf = tfidf.toarray()\n",
    "\n",
    "#classification\n",
    "clf.fit(train_x,train_y)\n",
    "predicted = clf.predict(test_x)\n",
    "predicted =list(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_mnb3 = accuracy_score(test_y, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the accuracy metric\n",
    "acc = accuracy_score(predicted, test_y)\n",
    "pre = precision_score(predicted, test_y, average = 'micro')\n",
    "rec = recall_score(predicted, test_y)\n",
    "f1 = f1_score(predicted, test_y)\n",
    "micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
    "\n",
    "print ('Model Performance Statistic Suite-3 (MNB): ')\n",
    "print ('Accuracy: ', acc)\n",
    "print ('Precision: ', pre)\n",
    "print ('Recall: ',rec)\n",
    "print ('F1 Score: ', f1)\n",
    "print('Micro f1: ', micro_f1)\n",
    "\n",
    "cm = confusion_matrix(predicted, test_y)\n",
    "\n",
    "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
    "print('Sensitivity : ', sensitivity )\n",
    "\n",
    "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
    "print('Specificity : ', specificity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
    "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
    "ax.set_ylabel('Actual',fontsize=14)\n",
    "ax.set_xlabel('Predicted',fontsize=14)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print ('Training Accuracies for comments without removing stop-words : ')\n",
    "print ('Logistic Regression: ', accuracy_lr3)\n",
    "print ('Support Vector Machine: ', accuracy_svm3)\n",
    "print ('Multinomial Naive Bayes ', accuracy_mnb3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "analyzer = CountVectorizer().build_analyzer()\n",
    "def lemmatized_words(doc):\n",
    "  return (WordNetLemmatizer().lemmatize(w) for w in analyzer(doc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=LogisticRegression()  \n",
    "vectorizer=TfidfVectorizer(analyzer = lemmatized_words,ngram_range=(1,1),token_pattern=r'\\b\\w+\\b')\n",
    "tfidf = vectorizer.fit_transform(corpus)\n",
    "tfidf = tfidf.toarray()\n",
    "# Training and Test Split           \n",
    "  \n",
    "train_x, test_x, train_y, test_y = train_test_split(tfidf, labels, test_size=0.20, random_state=42,stratify=labels)\n",
    "\n",
    "#Classificaion    \n",
    "clf.fit(train_x,train_y)\n",
    "predicted = clf.predict(test_x)\n",
    "predicted =list(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_lr4 = accuracy_score(test_y, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the accuracy metric\n",
    "acc = accuracy_score(predicted, test_y)\n",
    "pre = precision_score(predicted, test_y, average = 'micro')\n",
    "rec = recall_score(predicted, test_y)\n",
    "f1 = f1_score(predicted, test_y)\n",
    "micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
    "\n",
    "print ('Model Performance Statistic Suite-4(LR): ')\n",
    "print ('Accuracy: ', acc)\n",
    "print ('Precision: ', pre)\n",
    "print ('Recall: ',rec)\n",
    "print ('F1 Score: ', f1)\n",
    "print('Micro f1: ', micro_f1)\n",
    "\n",
    "cm = confusion_matrix(predicted, test_y)\n",
    "\n",
    "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
    "print('Sensitivity : ', sensitivity )\n",
    "\n",
    "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
    "print('Specificity : ', specificity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
    "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
    "ax.set_ylabel('Actual',fontsize=14)\n",
    "ax.set_xlabel('Predicted',fontsize=14)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=SVC()  \n",
    "vectorizer=TfidfVectorizer(analyzer = lemmatized_words,ngram_range=(1,1),token_pattern=r'\\b\\w+\\b')\n",
    "tfidf = vectorizer.fit_transform(corpus)\n",
    "tfidf = tfidf.toarray()\n",
    "# Training and Test Split           \n",
    "  \n",
    "train_x, test_x, train_y, test_y = train_test_split(tfidf, labels, test_size=0.20, random_state=42,stratify=labels)\n",
    "\n",
    "#Classificaion    \n",
    "clf.fit(train_x,train_y)\n",
    "predicted = clf.predict(test_x)\n",
    "predicted =list(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_svm4 = accuracy_score(test_y, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the accuracy metric\n",
    "acc = accuracy_score(predicted, test_y)\n",
    "pre = precision_score(predicted, test_y, average = 'micro')\n",
    "rec = recall_score(predicted, test_y)\n",
    "f1 = f1_score(predicted, test_y)\n",
    "micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
    "\n",
    "print ('Model Performance Statistic Suite-4 (SVM): ')\n",
    "print ('Accuracy: ', acc)\n",
    "print ('Precision: ', pre)\n",
    "print ('Recall: ',rec)\n",
    "print ('F1 Score: ', f1)\n",
    "print('Micro f1: ', micro_f1)\n",
    "\n",
    "cm = confusion_matrix(predicted, test_y)\n",
    "\n",
    "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
    "print('Sensitivity : ', sensitivity )\n",
    "\n",
    "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
    "print('Specificity : ', specificity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
    "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
    "ax.set_ylabel('Actual',fontsize=14)\n",
    "ax.set_xlabel('Predicted',fontsize=14)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*MNB*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf=MultinomialNB(alpha=0,fit_prior=True, class_prior=None)  \n",
    "vectorizer=TfidfVectorizer(analyzer = lemmatized_words,ngram_range=(1,1),token_pattern=r'\\b\\w+\\b')\n",
    "tfidf = vectorizer.fit_transform(corpus)\n",
    "tfidf = tfidf.toarray()\n",
    "# Training and Test Split           \n",
    "  \n",
    "train_x, test_x, train_y, test_y = train_test_split(tfidf, labels, test_size=0.20, random_state=42,stratify=labels)\n",
    "\n",
    "#Classificaion    \n",
    "clf.fit(train_x,train_y)\n",
    "predicted = clf.predict(test_x)\n",
    "predicted =list(predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_mnb4 = accuracy_score(test_y, predicted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the accuracy metric\n",
    "acc = accuracy_score(predicted, test_y)\n",
    "pre = precision_score(predicted, test_y, average = 'micro')\n",
    "rec = recall_score(predicted, test_y)\n",
    "f1 = f1_score(predicted, test_y)\n",
    "micro_f1 = f1_score(predicted, test_y, average = 'micro')\n",
    "\n",
    "print ('Model Performance Statistic Suite-5: ')\n",
    "print ('Accuracy: ', acc)\n",
    "print ('Precision: ', pre)\n",
    "print ('Recall: ',rec)\n",
    "print ('F1 Score: ', f1)\n",
    "print('Micro f1: ', micro_f1)\n",
    "\n",
    "cm = confusion_matrix(predicted, test_y)\n",
    "\n",
    "sensitivity = cm[0,0]/(cm[0,0]+cm[1,0])\n",
    "print('Sensitivity : ', sensitivity )\n",
    "\n",
    "specificity = cm[1,1]/(cm[0,1]+cm[1,1])\n",
    "print('Specificity : ', specificity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm =  pd.DataFrame(cm, index=['Non-sarcastic','Sarcastic'],columns=['Non-sarcastic','Sarcastic'])\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = sns.heatmap(cm,annot=True,cbar=False, cmap='Greens',linewidths=0.5,fmt='.0f')\n",
    "ax.set_title('Confusion Matrix',fontsize=16,y=1.25)\n",
    "ax.set_ylabel('Actual',fontsize=14)\n",
    "ax.set_xlabel('Predicted',fontsize=14)\n",
    "ax.xaxis.set_ticks_position('top')\n",
    "ax.xaxis.set_label_position('top')\n",
    "ax.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print ('Training Accuracies for comments without removing stop-words : ')\n",
    "print ('Logistic Regression: ', accuracy_lr4)\n",
    "print ('Support Vector Machine: ', accuracy_svm4)\n",
    "print ('Multinomial Naive Bayes ', accuracy_mnb4)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Y_pred = logit_classifier.predict(X_test)\n",
    "# report = classification_report(Y_pred,Y_test, output_dict=True)\n",
    "# report = pd.DataFrame(report).transpose()\n",
    "# report = report.rename({\"0\": \"Sarcastic Comments\", \"1\": \"Non Sarcastic Comments\"})\n",
    "# report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #pipeline parameters\n",
    "# parameters = \\\n",
    "# [ \\\n",
    "#     {\n",
    "#         'clf': [MultinomialNB()],\n",
    "#         'tf-idf__stop_words': ['english', None],\n",
    "#         'clf__alpha': [0.001, 0.1, 1, 10, 100]\n",
    "#     },\n",
    "\n",
    "#     {\n",
    "#         'clf': [SVC()],\n",
    "#         'tf-idf__stop_words': ['english', None],\n",
    "#         'clf__C': [0.001, 0.1, 1, 10, 100, 10e5],\n",
    "#         'clf__kernel': ['linear', 'rbf'],\n",
    "#         'clf__class_weight': ['balanced'],\n",
    "#         'clf__probability': [True]\n",
    "#     },\n",
    "\n",
    "#     {\n",
    "#         'clf': [LogisticRegression()],\n",
    "#         'tf-idf__stop_words': ['english', None],\n",
    "#         'clf__criterion': ['gini','entropy'],\n",
    "#         'clf__splitter': ['best','random'],\n",
    "#         'clf__class_weight':['balanced', None]\n",
    "#     }\n",
    "# ]\n",
    "\n",
    "# #evaluating multiple classifiers\n",
    "# #based on pipeline parameters\n",
    "# #-------------------------------\n",
    "# result=[]\n",
    "\n",
    "# for params in parameters:\n",
    "\n",
    "# #classifier\n",
    "#     clf = params['clf'][0]\n",
    "\n",
    "# #getting arguments by\n",
    "# #popping out classifier\n",
    "#     params.pop('clf')\n",
    "\n",
    "# #pipeline\n",
    "#     steps = [('tf-idf', TfidfVectorizer()), ('clf',clf)]\n",
    "\n",
    "# #cross validation using\n",
    "# #Grid Search\n",
    "#     grid = GridSearchCV(Pipeline(steps), param_grid=params, cv=10)\n",
    "#     grid.fit(corpus, labels)\n",
    "\n",
    "# #storing result\n",
    "#     result.append\\\n",
    "#     (\n",
    "#         {\n",
    "#             'grid': grid,\n",
    "#             'classifier': grid.best_estimator_,\n",
    "#             'best score': grid.best_score_,\n",
    "#             'best params': grid.best_params_,\n",
    "#             'cv': grid.cv\n",
    "#         }\n",
    "#     )   \n",
    "\n",
    "# #sorting result by best score\n",
    "# result = sorted(result, key=itemgetter('best score'),reverse=True)\n",
    "\n",
    "# #saving best classifier\n",
    "# grid = result[0]['grid']\n",
    "# print(grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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