Social media sentiment analysics-Task-2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f890e70d",
   "metadata": {},
   "source": [
    "**1 Sentimental analysis using NLTK-ML-Task-2**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75e2062f",
   "metadata": {},
   "source": [
    " '''Sentiment analysis, also known as opinion mining, is the process of using␣\n",
    "↪natural language processing (NLP) techniques\n",
    "to determine the sentiment or emotional tone expressed in text data.\n",
    "When applied to social media data, sentiment analysis can provide valuable␣\n",
    "↪insights into public opinion, customer feedback,\n",
    "brand perception, and more. Here's a brief overview of sentiment analysis using␣\n",
    "↪social media data:\n",
    "'''"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f23700d0",
   "metadata": {},
   "source": [
    "'''Use a dataset of tweets or Facebook posts and\n",
    "perform sentiment analysis to determine the\n",
    "overall sentiment of the posts.'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a04efd12",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9a0ef89",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\abhin\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5a359dc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import string\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import PorterStemmer\n",
    "import string\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from wordcloud import WordCloud\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8d1196b",
   "metadata": {},
   "source": [
    "**Read the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24314348",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
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       "      <th>negativereason</th>\n",
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       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_coord</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_location</th>\n",
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      "text/plain": [
       "                 tweet_id airline_sentiment  airline_sentiment_confidence  \\\n",
       "0      570306133677760513           neutral                        1.0000   \n",
       "1      570301130888122368          positive                        0.3486   \n",
       "2      570301083672813571           neutral                        0.6837   \n",
       "3      570301031407624196          negative                        1.0000   \n",
       "4      570300817074462722          negative                        1.0000   \n",
       "...                   ...               ...                           ...   \n",
       "14635  569587686496825344          positive                        0.3487   \n",
       "14636  569587371693355008          negative                        1.0000   \n",
       "14637  569587242672398336           neutral                        1.0000   \n",
       "14638  569587188687634433          negative                        1.0000   \n",
       "14639  569587140490866689           neutral                        0.6771   \n",
       "\n",
       "               negativereason  negativereason_confidence         airline  \\\n",
       "0                         NaN                        NaN  Virgin America   \n",
       "1                         NaN                     0.0000  Virgin America   \n",
       "2                         NaN                        NaN  Virgin America   \n",
       "3                  Bad Flight                     0.7033  Virgin America   \n",
       "4                  Can't Tell                     1.0000  Virgin America   \n",
       "...                       ...                        ...             ...   \n",
       "14635                     NaN                     0.0000        American   \n",
       "14636  Customer Service Issue                     1.0000        American   \n",
       "14637                     NaN                        NaN        American   \n",
       "14638  Customer Service Issue                     0.6659        American   \n",
       "14639                     NaN                     0.0000        American   \n",
       "\n",
       "      airline_sentiment_gold             name negativereason_gold  \\\n",
       "0                        NaN          cairdin                 NaN   \n",
       "1                        NaN         jnardino                 NaN   \n",
       "2                        NaN       yvonnalynn                 NaN   \n",
       "3                        NaN         jnardino                 NaN   \n",
       "4                        NaN         jnardino                 NaN   \n",
       "...                      ...              ...                 ...   \n",
       "14635                    NaN  KristenReenders                 NaN   \n",
       "14636                    NaN         itsropes                 NaN   \n",
       "14637                    NaN         sanyabun                 NaN   \n",
       "14638                    NaN       SraJackson                 NaN   \n",
       "14639                    NaN        daviddtwu                 NaN   \n",
       "\n",
       "       retweet_count                                               text  \\\n",
       "0                  0                @VirginAmerica What @dhepburn said.   \n",
       "1                  0  @VirginAmerica plus you've added commercials t...   \n",
       "2                  0  @VirginAmerica I didn't today... Must mean I n...   \n",
       "3                  0  @VirginAmerica it's really aggressive to blast...   \n",
       "4                  0  @VirginAmerica and it's a really big bad thing...   \n",
       "...              ...                                                ...   \n",
       "14635              0  @AmericanAir thank you we got on a different f...   \n",
       "14636              0  @AmericanAir leaving over 20 minutes Late Flig...   \n",
       "14637              0  @AmericanAir Please bring American Airlines to...   \n",
       "14638              0  @AmericanAir you have my money, you change my ...   \n",
       "14639              0  @AmericanAir we have 8 ppl so we need 2 know h...   \n",
       "\n",
       "      tweet_coord              tweet_created tweet_location  \\\n",
       "0             NaN  2015-02-24 11:35:52 -0800            NaN   \n",
       "1             NaN  2015-02-24 11:15:59 -0800            NaN   \n",
       "2             NaN  2015-02-24 11:15:48 -0800      Lets Play   \n",
       "3             NaN  2015-02-24 11:15:36 -0800            NaN   \n",
       "4             NaN  2015-02-24 11:14:45 -0800            NaN   \n",
       "...           ...                        ...            ...   \n",
       "14635         NaN  2015-02-22 12:01:01 -0800            NaN   \n",
       "14636         NaN  2015-02-22 11:59:46 -0800          Texas   \n",
       "14637         NaN  2015-02-22 11:59:15 -0800  Nigeria,lagos   \n",
       "14638         NaN  2015-02-22 11:59:02 -0800     New Jersey   \n",
       "14639         NaN  2015-02-22 11:58:51 -0800     dallas, TX   \n",
       "\n",
       "                    user_timezone  \n",
       "0      Eastern Time (US & Canada)  \n",
       "1      Pacific Time (US & Canada)  \n",
       "2      Central Time (US & Canada)  \n",
       "3      Pacific Time (US & Canada)  \n",
       "4      Pacific Time (US & Canada)  \n",
       "...                           ...  \n",
       "14635                         NaN  \n",
       "14636                         NaN  \n",
       "14637                         NaN  \n",
       "14638  Eastern Time (US & Canada)  \n",
       "14639                         NaN  \n",
       "\n",
       "[14640 rows x 15 columns]"
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     },
     "execution_count": 4,
     "metadata": {},
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   ],
   "source": [
    "df=pd.read_csv('C:\\\\Users\\\\abhin\\\\Downloads\\\\Tweets.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a0cb6837",
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       "      <td>2015-02-24 11:15:48 -0800</td>\n",
       "      <td>Lets Play</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
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       "      <td>570301031407624196</td>\n",
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       "      <td>Bad Flight</td>\n",
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       "      <td>Virgin America</td>\n",
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       "      <td>jnardino</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:14:45 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>570300767074181121</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>0.6842</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica seriously would pay $30 a fligh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:14:33 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>570300616901320704</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.6745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cjmcginnis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica yes, nearly every time I fly VX...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:13:57 -0800</td>\n",
       "      <td>San Francisco CA</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>570300248553349120</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6340</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pilot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica Really missed a prime opportuni...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:12:29 -0800</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>570299953286942721</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.6559</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dhepburn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@virginamerica Well, I didn't…but NOW I DO! :-D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:11:19 -0800</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>570295459631263746</td>\n",
       "      <td>positive</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</td>\n",
       "      <td>YupitsTate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica it was amazing, and arrived an ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 10:53:27 -0800</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             tweet_id airline_sentiment  airline_sentiment_confidence  \\\n",
       "0  570306133677760513           neutral                        1.0000   \n",
       "1  570301130888122368          positive                        0.3486   \n",
       "2  570301083672813571           neutral                        0.6837   \n",
       "3  570301031407624196          negative                        1.0000   \n",
       "4  570300817074462722          negative                        1.0000   \n",
       "5  570300767074181121          negative                        1.0000   \n",
       "6  570300616901320704          positive                        0.6745   \n",
       "7  570300248553349120           neutral                        0.6340   \n",
       "8  570299953286942721          positive                        0.6559   \n",
       "9  570295459631263746          positive                        1.0000   \n",
       "\n",
       "  negativereason  negativereason_confidence         airline  \\\n",
       "0            NaN                        NaN  Virgin America   \n",
       "1            NaN                     0.0000  Virgin America   \n",
       "2            NaN                        NaN  Virgin America   \n",
       "3     Bad Flight                     0.7033  Virgin America   \n",
       "4     Can't Tell                     1.0000  Virgin America   \n",
       "5     Can't Tell                     0.6842  Virgin America   \n",
       "6            NaN                     0.0000  Virgin America   \n",
       "7            NaN                        NaN  Virgin America   \n",
       "8            NaN                        NaN  Virgin America   \n",
       "9            NaN                        NaN  Virgin America   \n",
       "\n",
       "  airline_sentiment_gold        name negativereason_gold  retweet_count  \\\n",
       "0                    NaN     cairdin                 NaN              0   \n",
       "1                    NaN    jnardino                 NaN              0   \n",
       "2                    NaN  yvonnalynn                 NaN              0   \n",
       "3                    NaN    jnardino                 NaN              0   \n",
       "4                    NaN    jnardino                 NaN              0   \n",
       "5                    NaN    jnardino                 NaN              0   \n",
       "6                    NaN  cjmcginnis                 NaN              0   \n",
       "7                    NaN       pilot                 NaN              0   \n",
       "8                    NaN    dhepburn                 NaN              0   \n",
       "9                    NaN  YupitsTate                 NaN              0   \n",
       "\n",
       "                                                text tweet_coord  \\\n",
       "0                @VirginAmerica What @dhepburn said.         NaN   \n",
       "1  @VirginAmerica plus you've added commercials t...         NaN   \n",
       "2  @VirginAmerica I didn't today... Must mean I n...         NaN   \n",
       "3  @VirginAmerica it's really aggressive to blast...         NaN   \n",
       "4  @VirginAmerica and it's a really big bad thing...         NaN   \n",
       "5  @VirginAmerica seriously would pay $30 a fligh...         NaN   \n",
       "6  @VirginAmerica yes, nearly every time I fly VX...         NaN   \n",
       "7  @VirginAmerica Really missed a prime opportuni...         NaN   \n",
       "8    @virginamerica Well, I didn't…but NOW I DO! :-D         NaN   \n",
       "9  @VirginAmerica it was amazing, and arrived an ...         NaN   \n",
       "\n",
       "               tweet_created    tweet_location               user_timezone  \n",
       "0  2015-02-24 11:35:52 -0800               NaN  Eastern Time (US & Canada)  \n",
       "1  2015-02-24 11:15:59 -0800               NaN  Pacific Time (US & Canada)  \n",
       "2  2015-02-24 11:15:48 -0800         Lets Play  Central Time (US & Canada)  \n",
       "3  2015-02-24 11:15:36 -0800               NaN  Pacific Time (US & Canada)  \n",
       "4  2015-02-24 11:14:45 -0800               NaN  Pacific Time (US & Canada)  \n",
       "5  2015-02-24 11:14:33 -0800               NaN  Pacific Time (US & Canada)  \n",
       "6  2015-02-24 11:13:57 -0800  San Francisco CA  Pacific Time (US & Canada)  \n",
       "7  2015-02-24 11:12:29 -0800       Los Angeles  Pacific Time (US & Canada)  \n",
       "8  2015-02-24 11:11:19 -0800         San Diego  Pacific Time (US & Canada)  \n",
       "9  2015-02-24 10:53:27 -0800       Los Angeles  Eastern Time (US & Canada)  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "66f31c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['tweet_id', 'airline_sentiment', 'airline_sentiment_confidence',\n",
       "       'negativereason', 'negativereason_confidence', 'airline',\n",
       "       'airline_sentiment_gold', 'name', 'negativereason_gold',\n",
       "       'retweet_count', 'text', 'tweet_coord', 'tweet_created',\n",
       "       'tweet_location', 'user_timezone'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "51591879",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14640 entries, 0 to 14639\n",
      "Data columns (total 15 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   tweet_id                      14640 non-null  int64  \n",
      " 1   airline_sentiment             14640 non-null  object \n",
      " 2   airline_sentiment_confidence  14640 non-null  float64\n",
      " 3   negativereason                9178 non-null   object \n",
      " 4   negativereason_confidence     10522 non-null  float64\n",
      " 5   airline                       14640 non-null  object \n",
      " 6   airline_sentiment_gold        40 non-null     object \n",
      " 7   name                          14640 non-null  object \n",
      " 8   negativereason_gold           32 non-null     object \n",
      " 9   retweet_count                 14640 non-null  int64  \n",
      " 10  text                          14640 non-null  object \n",
      " 11  tweet_coord                   1019 non-null   object \n",
      " 12  tweet_created                 14640 non-null  object \n",
      " 13  tweet_location                9907 non-null   object \n",
      " 14  user_timezone                 9820 non-null   object \n",
      "dtypes: float64(2), int64(2), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "580fcfdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                          int64\n",
       "airline_sentiment                object\n",
       "airline_sentiment_confidence    float64\n",
       "negativereason                   object\n",
       "negativereason_confidence       float64\n",
       "airline                          object\n",
       "airline_sentiment_gold           object\n",
       "name                             object\n",
       "negativereason_gold              object\n",
       "retweet_count                     int64\n",
       "text                             object\n",
       "tweet_coord                      object\n",
       "tweet_created                    object\n",
       "tweet_location                   object\n",
       "user_timezone                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5982d9d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14640, 15)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e015f130",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                            0\n",
       "airline_sentiment                   0\n",
       "airline_sentiment_confidence        0\n",
       "negativereason                   5462\n",
       "negativereason_confidence        4118\n",
       "airline                             0\n",
       "airline_sentiment_gold          14600\n",
       "name                                0\n",
       "negativereason_gold             14608\n",
       "retweet_count                       0\n",
       "text                                0\n",
       "tweet_coord                     13621\n",
       "tweet_created                       0\n",
       "tweet_location                   4733\n",
       "user_timezone                    4820\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2603ed51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'negative', 'neutral', 'positive'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['airline_sentiment_gold'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "12c97cd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'Bad Flight', \"Can't Tell\", 'Late Flight',\n",
       "       'Customer Service Issue', 'Flight Booking Problems',\n",
       "       'Lost Luggage', 'Flight Attendant Complaints', 'Cancelled Flight',\n",
       "       'Damaged Luggage', 'longlines'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['negativereason'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "50ad72b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('negativereason',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3ff52a5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['airline_sentiment_gold','tweet_coord','negativereason_confidence']\n",
    "df.drop(cols,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6e2f910b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>airline_sentiment_confidence</th>\n",
       "      <th>airline</th>\n",
       "      <th>name</th>\n",
       "      <th>negativereason_gold</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_location</th>\n",
       "      <th>user_timezone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>570306133677760513</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>cairdin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>2015-02-24 11:35:52 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>570301130888122368</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3486</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>2015-02-24 11:15:59 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>570301083672813571</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>yvonnalynn</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "      <td>2015-02-24 11:15:48 -0800</td>\n",
       "      <td>Lets Play</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>570301031407624196</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "      <td>2015-02-24 11:15:36 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>570300817074462722</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>jnardino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "      <td>2015-02-24 11:14:45 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14635</th>\n",
       "      <td>569587686496825344</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3487</td>\n",
       "      <td>American</td>\n",
       "      <td>KristenReenders</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir thank you we got on a different f...</td>\n",
       "      <td>2015-02-22 12:01:01 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14636</th>\n",
       "      <td>569587371693355008</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>American</td>\n",
       "      <td>itsropes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir leaving over 20 minutes Late Flig...</td>\n",
       "      <td>2015-02-22 11:59:46 -0800</td>\n",
       "      <td>Texas</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14637</th>\n",
       "      <td>569587242672398336</td>\n",
       "      <td>neutral</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>American</td>\n",
       "      <td>sanyabun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir Please bring American Airlines to...</td>\n",
       "      <td>2015-02-22 11:59:15 -0800</td>\n",
       "      <td>Nigeria,lagos</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14638</th>\n",
       "      <td>569587188687634433</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>American</td>\n",
       "      <td>SraJackson</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir you have my money, you change my ...</td>\n",
       "      <td>2015-02-22 11:59:02 -0800</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14639</th>\n",
       "      <td>569587140490866689</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6771</td>\n",
       "      <td>American</td>\n",
       "      <td>daviddtwu</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir we have 8 ppl so we need 2 know h...</td>\n",
       "      <td>2015-02-22 11:58:51 -0800</td>\n",
       "      <td>dallas, TX</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14640 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 tweet_id airline_sentiment  airline_sentiment_confidence  \\\n",
       "0      570306133677760513           neutral                        1.0000   \n",
       "1      570301130888122368          positive                        0.3486   \n",
       "2      570301083672813571           neutral                        0.6837   \n",
       "3      570301031407624196          negative                        1.0000   \n",
       "4      570300817074462722          negative                        1.0000   \n",
       "...                   ...               ...                           ...   \n",
       "14635  569587686496825344          positive                        0.3487   \n",
       "14636  569587371693355008          negative                        1.0000   \n",
       "14637  569587242672398336           neutral                        1.0000   \n",
       "14638  569587188687634433          negative                        1.0000   \n",
       "14639  569587140490866689           neutral                        0.6771   \n",
       "\n",
       "              airline             name negativereason_gold  retweet_count  \\\n",
       "0      Virgin America          cairdin                 NaN              0   \n",
       "1      Virgin America         jnardino                 NaN              0   \n",
       "2      Virgin America       yvonnalynn                 NaN              0   \n",
       "3      Virgin America         jnardino                 NaN              0   \n",
       "4      Virgin America         jnardino                 NaN              0   \n",
       "...               ...              ...                 ...            ...   \n",
       "14635        American  KristenReenders                 NaN              0   \n",
       "14636        American         itsropes                 NaN              0   \n",
       "14637        American         sanyabun                 NaN              0   \n",
       "14638        American       SraJackson                 NaN              0   \n",
       "14639        American        daviddtwu                 NaN              0   \n",
       "\n",
       "                                                    text  \\\n",
       "0                    @VirginAmerica What @dhepburn said.   \n",
       "1      @VirginAmerica plus you've added commercials t...   \n",
       "2      @VirginAmerica I didn't today... Must mean I n...   \n",
       "3      @VirginAmerica it's really aggressive to blast...   \n",
       "4      @VirginAmerica and it's a really big bad thing...   \n",
       "...                                                  ...   \n",
       "14635  @AmericanAir thank you we got on a different f...   \n",
       "14636  @AmericanAir leaving over 20 minutes Late Flig...   \n",
       "14637  @AmericanAir Please bring American Airlines to...   \n",
       "14638  @AmericanAir you have my money, you change my ...   \n",
       "14639  @AmericanAir we have 8 ppl so we need 2 know h...   \n",
       "\n",
       "                   tweet_created tweet_location               user_timezone  \n",
       "0      2015-02-24 11:35:52 -0800            NaN  Eastern Time (US & Canada)  \n",
       "1      2015-02-24 11:15:59 -0800            NaN  Pacific Time (US & Canada)  \n",
       "2      2015-02-24 11:15:48 -0800      Lets Play  Central Time (US & Canada)  \n",
       "3      2015-02-24 11:15:36 -0800            NaN  Pacific Time (US & Canada)  \n",
       "4      2015-02-24 11:14:45 -0800            NaN  Pacific Time (US & Canada)  \n",
       "...                          ...            ...                         ...  \n",
       "14635  2015-02-22 12:01:01 -0800            NaN                         NaN  \n",
       "14636  2015-02-22 11:59:46 -0800          Texas                         NaN  \n",
       "14637  2015-02-22 11:59:15 -0800  Nigeria,lagos                         NaN  \n",
       "14638  2015-02-22 11:59:02 -0800     New Jersey  Eastern Time (US & Canada)  \n",
       "14639  2015-02-22 11:58:51 -0800     dallas, TX                         NaN  \n",
       "\n",
       "[14640 rows x 11 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f48ecbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                            0\n",
       "airline_sentiment                   0\n",
       "airline_sentiment_confidence        0\n",
       "airline                             0\n",
       "name                                0\n",
       "negativereason_gold             14608\n",
       "retweet_count                       0\n",
       "text                                0\n",
       "tweet_created                       0\n",
       "tweet_location                   4733\n",
       "user_timezone                    4820\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ca846147",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('negativereason_gold',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f14b89dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols1=['tweet_location','user_timezone']\n",
    "df.drop(cols1,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d5eeea4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tweet_id                        0\n",
       "airline_sentiment               0\n",
       "airline_sentiment_confidence    0\n",
       "airline                         0\n",
       "name                            0\n",
       "retweet_count                   0\n",
       "text                            0\n",
       "tweet_created                   0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "beb9d1e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14640, 8)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14640 entries, 0 to 14639\n",
      "Data columns (total 8 columns):\n",
      " #   Column                        Non-Null Count  Dtype  \n",
      "---  ------                        --------------  -----  \n",
      " 0   tweet_id                      14640 non-null  int64  \n",
      " 1   airline_sentiment             14640 non-null  object \n",
      " 2   airline_sentiment_confidence  14640 non-null  float64\n",
      " 3   airline                       14640 non-null  object \n",
      " 4   name                          14640 non-null  object \n",
      " 5   retweet_count                 14640 non-null  int64  \n",
      " 6   text                          14640 non-null  object \n",
      " 7   tweet_created                 14640 non-null  object \n",
      "dtypes: float64(1), int64(2), object(5)\n",
      "memory usage: 915.1+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df.shape)\n",
    "display(df.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "00f11d29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neutral</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>positive</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>neutral</td>\n",
       "      <td>@VirginAmerica I didn't today... Must mean I n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>negative</td>\n",
       "      <td>@VirginAmerica it's really aggressive to blast...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>negative</td>\n",
       "      <td>@VirginAmerica and it's a really big bad thing...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14635</th>\n",
       "      <td>positive</td>\n",
       "      <td>@AmericanAir thank you we got on a different f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14636</th>\n",
       "      <td>negative</td>\n",
       "      <td>@AmericanAir leaving over 20 minutes Late Flig...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14637</th>\n",
       "      <td>neutral</td>\n",
       "      <td>@AmericanAir Please bring American Airlines to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14638</th>\n",
       "      <td>negative</td>\n",
       "      <td>@AmericanAir you have my money, you change my ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14639</th>\n",
       "      <td>neutral</td>\n",
       "      <td>@AmericanAir we have 8 ppl so we need 2 know h...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14640 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      airline_sentiment                                               text\n",
       "0               neutral                @VirginAmerica What @dhepburn said.\n",
       "1              positive  @VirginAmerica plus you've added commercials t...\n",
       "2               neutral  @VirginAmerica I didn't today... Must mean I n...\n",
       "3              negative  @VirginAmerica it's really aggressive to blast...\n",
       "4              negative  @VirginAmerica and it's a really big bad thing...\n",
       "...                 ...                                                ...\n",
       "14635          positive  @AmericanAir thank you we got on a different f...\n",
       "14636          negative  @AmericanAir leaving over 20 minutes Late Flig...\n",
       "14637           neutral  @AmericanAir Please bring American Airlines to...\n",
       "14638          negative  @AmericanAir you have my money, you change my ...\n",
       "14639           neutral  @AmericanAir we have 8 ppl so we need 2 know h...\n",
       "\n",
       "[14640 rows x 2 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['airline_sentiment','text']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ecfa1e2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Graph-1-Airline Sentiment Distribution')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='airline_sentiment')\n",
    "plt.title('Graph-1-Airline Sentiment Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cbc0179b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\abhin\\AppData\\Local\\Temp\\ipykernel_14304\\4046151189.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df['count_word'] = df['text'].apply(lambda x : len(x.split(' ')))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['count_word'] = df['text'].apply(lambda x : len(x.split(' ')))\n",
    "sns.histplot(data = df , x='count_word',kde=True)\n",
    "plt.title('Graph-2-Number de Word Distribution without any Cleaning Task')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "dbb9cbe6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = df , x='count_word',hue='airline_sentiment',alpha=0.6,kde=True)\n",
    "plt.title('Graph-3-Number de Word Distribution without any Cleaning Task')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8215c779",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(data = df , y='count_word',x='airline_sentiment')\n",
    "plt.title('Graph-3(1)=Boxplot Number of Word Across Tweets Categories')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c5835758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>airline_sentiment</th>\n",
       "      <th>text</th>\n",
       "      <th>count_word</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>neutral</td>\n",
       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>positive</td>\n",
       "      <td>@VirginAmerica Thanks!</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>positive</td>\n",
       "      <td>I ❤️ flying @VirginAmerica. ☺️👍</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>neutral</td>\n",
       "      <td>@VirginAmerica  DREAM http://t.co/oA2dRfAoQ2 h...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>neutral</td>\n",
       "      <td>@VirginAmerica @ladygaga @carrieunderwood - Ca...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14312</th>\n",
       "      <td>positive</td>\n",
       "      <td>@AmericanAir awesome! Thx</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14314</th>\n",
       "      <td>negative</td>\n",
       "      <td>@AmericanAir yes, and rebooked incorrectly.</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14443</th>\n",
       "      <td>neutral</td>\n",
       "      <td>@AmericanAir hi how are you</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14600</th>\n",
       "      <td>neutral</td>\n",
       "      <td>http://t.co/EIw2sYb8Fu roberts&amp;amp;s=1 @Americ...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14630</th>\n",
       "      <td>positive</td>\n",
       "      <td>@AmericanAir Thanks! He is.</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>817 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      airline_sentiment                                               text  \\\n",
       "0               neutral                @VirginAmerica What @dhepburn said.   \n",
       "14             positive                             @VirginAmerica Thanks!   \n",
       "18             positive                    I ❤️ flying @VirginAmerica. ☺️👍   \n",
       "46              neutral  @VirginAmerica  DREAM http://t.co/oA2dRfAoQ2 h...   \n",
       "58              neutral  @VirginAmerica @ladygaga @carrieunderwood - Ca...   \n",
       "...                 ...                                                ...   \n",
       "14312          positive                          @AmericanAir awesome! Thx   \n",
       "14314          negative        @AmericanAir yes, and rebooked incorrectly.   \n",
       "14443           neutral                        @AmericanAir hi how are you   \n",
       "14600           neutral  http://t.co/EIw2sYb8Fu roberts&amp;s=1 @Americ...   \n",
       "14630          positive                        @AmericanAir Thanks! He is.   \n",
       "\n",
       "       count_word  \n",
       "0               4  \n",
       "14              2  \n",
       "18              5  \n",
       "46              5  \n",
       "58              5  \n",
       "...           ...  \n",
       "14312           3  \n",
       "14314           5  \n",
       "14443           5  \n",
       "14600           3  \n",
       "14630           4  \n",
       "\n",
       "[817 rows x 3 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[np.logical_or(df['count_word']>35,df['count_word']<=5),:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7834ea20",
   "metadata": {},
   "source": [
    "**Preprocessing the Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "899927ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer\n",
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9761d6a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# punctuation Removal\n",
    "def remove_punctuation(text):\n",
    "    return re.sub(r'[^\\w\\s]','',text)\n",
    "#stopword removal\n",
    "def remove_stopwords(text):\n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    tokens = word_tokenize(text)\n",
    "    filter_tokens = [word for word in tokens if word.lower() not in stop_words]\n",
    "    return \" \".join(filter_tokens)\n",
    "#remove numeric\n",
    "def remove_numeric(text):\n",
    "    return re.sub(r'\\d+','',text)\n",
    "#Stemming\n",
    "def apply_stemming(text):\n",
    "    stemmer = PorterStemmer()\n",
    "    tokens = word_tokenize(text)\n",
    "    stemmed_tokens = [stemmer.stem(word) for word in tokens]\n",
    "    return \" \".join(stemmed_tokens)\n",
    "def remove_mentions(text):\n",
    "    return re.sub(r'@\\w+','',text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d6cd4f6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\abhin\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers\\punkt.zip.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "walk throw the street , a passeng walk toward me , talking␣about a walk chicken on the street\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import PorterStemmer\n",
    "def apply_stemming(text):\n",
    "    stemmer = PorterStemmer()\n",
    "    tokens = word_tokenize(text)\n",
    "    stemmed_tokens = [stemmer.stem(word) for word in tokens]\n",
    "    return \" \".join(stemmed_tokens)\n",
    "input_text = \"walking throw the street, a passenger walked toward me, talking␣about a walked chicken on the streets\"\n",
    "stemmed_text = apply_stemming(input_text)\n",
    "print(stemmed_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5894131b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'walk throw the street , a passeng walk toward me talk about a walk chicken on the street'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apply_stemming('walking throw the street , a passenger walked toward me talking about a walked chicken on the streets')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9081520f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def text_preprocessing(text):\n",
    "    sentence = remove_mentions(text)\n",
    "    sentence = remove_punctuation(sentence)\n",
    "    sentence = remove_stopwords(sentence)\n",
    "    sentence = remove_numeric(sentence)\n",
    "    sentence = apply_stemming(sentence)\n",
    "    return sentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "800895ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'walk throw street passeng walk toward metalk walk chicken street'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_preprocessing('walking throw the street , a passenger walked toward me␣,talking about a walked chicken on the streets')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b961faec",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\abhin\\AppData\\Local\\Temp\\ipykernel_14304\\3410990210.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df.loc[:,'new_text'] = df['text'].apply(lambda x : text_preprocessing(x))\n"
     ]
    }
   ],
   "source": [
    "df.loc[:,'new_text'] = df['text'].apply(lambda x : text_preprocessing(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b5a1aadd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\abhin\\AppData\\Local\\Temp\\ipykernel_14304\\1052658891.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\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",
      "  df.loc[:,'new_count_word'] = df['new_text'].apply(lambda x : len(x.split(' ')))\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.loc[:,'new_count_word'] = df['new_text'].apply(lambda x : len(x.split(' ')))\n",
    "sns.histplot(data = df , x='new_count_word',kde=True)\n",
    "plt.title('Graph-5-Number of Word Distribution after Cleaning Task')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "a9184a60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df,x='airline_sentiment')\n",
    "plt.title('Graph-1(a)-Airline Sentiment Distribution-after cleaning the data')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "78d17195",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(data = df , x='count_word',hue='airline_sentiment',alpha=0.9,kde=True)\n",
    "plt.title('Graph-3-Number de Word Distribution after Cleaning Task')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "263fa33c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}