Sentiment_Analysis.ipynb

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  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "## Sentiment Analysis using NLTK-ML"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75ddcf9f-e0a0-40bc-a18c-893380922aec",
   "metadata": {},
   "source": [
    "**Task:-2**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e968a2a6-b3ea-41ae-8749-51cce7623aa7",
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   "source": [
    "Sentiment Analysis, also Known as opinion minning, is the process using\n",
    "- Natural Language Processing(NLP)\n",
    "- Techniques to determine the sentiment or emotional tine expressed in text data.\n",
    "- When applied to social medai data, sentiment analysis can provide valuable insight into public opinion.,customer feedback,brand perception, and more.\n",
    "- Here's a brief overview of sentiment analysis using \"Social Media data\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd157713-58e8-4c55-bcc2-aac147d76f6c",
   "metadata": {},
   "source": [
    "use a dataset of tweets or facebook posts and perform sentiments analysis to determine the overall sentiment of the posts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d5f43d99-0002-4a52-b2f0-353216beeb4e",
   "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\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a14074e-e997-4bba-83b0-d7cc51ab23da",
   "metadata": {},
   "source": [
    "**Read the data**\n"
   ]
  },
  {
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   "execution_count": 2,
   "id": "1b78e71e-8897-4d97-ab30-0cb0dcfb2626",
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       "  <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>negativereason</th>\n",
       "      <th>negativereason_confidence</th>\n",
       "      <th>airline</th>\n",
       "      <th>airline_sentiment_gold</th>\n",
       "      <th>name</th>\n",
       "      <th>negativereason_gold</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_coord</th>\n",
       "      <th>tweet_created</th>\n",
       "      <th>tweet_location</th>\n",
       "      <th>user_timezone</th>\n",
       "    </tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
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       "      <td>cairdin</td>\n",
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       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
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       "      <td>2015-02-24 11:35:52 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
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       "      <td>jnardino</td>\n",
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       "      <td>0</td>\n",
       "      <td>@VirginAmerica plus you've added commercials t...</td>\n",
       "      <td>NaN</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",
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       "      <th>2</th>\n",
       "      <td>570301083672813571</td>\n",
       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</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>NaN</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>Bad Flight</td>\n",
       "      <td>0.7033</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 it's really aggressive to blast...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 11:15:36 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>570300817074462722</td>\n",
       "      <td>negative</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Can't Tell</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>Virgin America</td>\n",
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       "      <td>jnardino</td>\n",
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       "      <td>2015-02-24 11:14:45 -0800</td>\n",
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       "      <td>Pacific Time (US &amp; Canada)</td>\n",
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       "      <th>...</th>\n",
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       "      <th>14635</th>\n",
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       "      <td>0.0000</td>\n",
       "      <td>American</td>\n",
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       "      <td>KristenReenders</td>\n",
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       "      <td>0</td>\n",
       "      <td>@AmericanAir thank you we got on a different f...</td>\n",
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       "      <td>itsropes</td>\n",
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       "      <td>2015-02-22 11:59:46 -0800</td>\n",
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       "      <td>sanyabun</td>\n",
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       "      <td>0</td>\n",
       "      <td>@AmericanAir Please bring American Airlines to...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-22 11:59:15 -0800</td>\n",
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       "      <td>American</td>\n",
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       "      <td>SraJackson</td>\n",
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       "      <td>@AmericanAir you have my money, you change my ...</td>\n",
       "      <td>NaN</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",
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       "      <td>0.0000</td>\n",
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       "      <td>daviddtwu</td>\n",
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       "      <td>@AmericanAir we have 8 ppl so we need 2 know h...</td>\n",
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       "      <td>2015-02-22 11:58:51 -0800</td>\n",
       "      <td>dallas, TX</td>\n",
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       "<p>14640 rows × 15 columns</p>\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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   "source": [
    "dff='C:\\\\Users\\\\hp\\\\OneDrive\\\\Desktop\\Dataset\\\\Tweets.csv'\n",
    "df=pd.read_csv(dff)\n",
    "df"
   ]
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       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
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       "      <td>neutral</td>\n",
       "      <td>0.6837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Virgin America</td>\n",
       "      <td>NaN</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>NaN</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>Bad Flight</td>\n",
       "      <td>0.7033</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 it's really aggressive to blast...</td>\n",
       "      <td>NaN</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>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",
       "  </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",
       "\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",
       "  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",
       "\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",
       "\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)  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "de95bb77-04d2-4b93-8a6e-cca7dbec0a3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>negativereason</th>\n",
       "      <th>negativereason_confidence</th>\n",
       "      <th>airline</th>\n",
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       "      <th>name</th>\n",
       "      <th>negativereason_gold</th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "      <th>tweet_coord</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>14635</th>\n",
       "      <td>569587686496825344</td>\n",
       "      <td>positive</td>\n",
       "      <td>0.3487</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</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>NaN</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>Customer Service Issue</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</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>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sanyabun</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir Please bring American Airlines to...</td>\n",
       "      <td>NaN</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>Customer Service Issue</td>\n",
       "      <td>0.6659</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</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>NaN</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>NaN</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</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>NaN</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",
       "</div>"
      ],
      "text/plain": [
       "                 tweet_id airline_sentiment  airline_sentiment_confidence  \\\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",
       "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",
       "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",
       "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",
       "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",
       "14635                         NaN  \n",
       "14636                         NaN  \n",
       "14637                         NaN  \n",
       "14638  Eastern Time (US & Canada)  \n",
       "14639                         NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b7c7bf20-23b7-45ee-9266-9646dd94dfdf",
   "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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4d0511d2-cb71-4d1b-934a-102852363158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14640, 15)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2de724d6-2ee9-4e40-832d-05e96d909dc2",
   "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": "654f1381-4c05-4836-a1b6-646ef6a56d41",
   "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": "3afad1cd-7e62-45f9-891b-c617a71d0c7b",
   "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "359a7aa1-b8d2-4251-bdfb-8ddde0a240a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'negative', 'neutral', 'positive'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['airline_sentiment_gold'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "781cc67e-6496-4f3a-bd37-d8c3d9700fd3",
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['negativereason'].unique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "40b201c9-199d-47b5-a8f7-9e89548c4525",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('negativereason',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7323cb33-050d-4008-925a-b53a204fa31a",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols=['airline_sentiment_gold','tweet_coord','negativereason_confidence']\n",
    "df.drop(cols,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e777b373-4f29-4de2-9f9c-efc46f103d40",
   "metadata": {},
   "outputs": [
    {
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       "      <td>@VirginAmerica What @dhepburn said.</td>\n",
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       "      <td>yvonnalynn</td>\n",
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       "      <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",
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       "      <th>3</th>\n",
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       "      <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",
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       "      <td>jnardino</td>\n",
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       "      <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",
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       "    <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": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "491ef2c0-1d7d-449b-9865-e869664dbc29",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('negativereason_gold',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0595dc5f-becf-45d2-b26f-88c002ba5933",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols1=['tweet_location','user_timezone']\n",
    "df.drop(cols1,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ac650d92-e844-4cb0-89ca-57746dbe1f63",
   "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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9eae5b06-81ac-48ac-b7b9-ea56e3ad0e29",
   "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": 19,
   "id": "7bb4a04a-d6ec-4237-866d-8a6aa13dcfee",
   "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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['airline_sentiment','text']]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0651e767-6d14-4228-8f5b-40c1bb76e752",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Graph-1-Airline Sentiment Distribution')"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "id": "710b7cde-d5b2-435c-89c0-9af31256baed",
   "metadata": {},
   "outputs": [
    {
     "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": 22,
   "id": "f257e5f8-7b6a-4fdb-ad91-8c3970c3f235",
   "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": 23,
   "id": "b3921d12-4282-4538-a325-b2ca5404ba5d",
   "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": 24,
   "id": "d1c082d6-24bf-4a2d-984c-9dbbcfeb1f9c",
   "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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[np.logical_or(df['count_word']>35,df['count_word']<=5),:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f933139b-2de5-4265-a91b-ac2f15e7f07e",
   "metadata": {},
   "source": [
    "**Preprocessing the data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "805118b3-f3f8-4720-8c40-8d2bf22521d3",
   "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": 26,
   "id": "bb6abf0b-d42d-469c-8ca7-1d4f41e6c647",
   "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": 27,
   "id": "de8546c1-d16e-4c46-be42-3b40375919ed",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "walk throw the street , a passeng walk toward me , talking␣about a walk chicken on the street\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\hp\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\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": 28,
   "id": "f03d5474-9c46-46f9-baef-df77649ec574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'walk throw the street , a passeng walk toward me talk about a walk chicken on the street'"
      ]
     },
     "execution_count": 28,
     "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": 29,
   "id": "f8bb7179-d442-4afd-aa31-5c7e0ddb89b8",
   "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": 30,
   "id": "2e97d84c-0d1c-4a0f-97f3-d0f6d3e2be90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'walk throw street passeng walk toward metalk walk chicken street'"
      ]
     },
     "execution_count": 30,
     "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": 31,
   "id": "8a7a4961-c247-4927-81d6-8a91d8c905b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[:,'new_text'] = df['text'].apply(lambda x : text_preprocessing(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "134bbc6a-39fc-4ef3-9462-16ef8a87ade3",
   "metadata": {},
   "outputs": [
    {
     "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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "4dd544d0-0587-43b8-a9bb-422e7bda53cb",
   "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": 34,
   "id": "71a6fe17-5568-4f47-bb76-1f77d8c27b20",
   "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()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3221df52-3c48-441a-a4fa-7fd213650aee",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}