implement.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy  as np\n",
    "import nltk\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.corpus import wordnet\n",
    "from nltk import pos_tag\n",
    "lemmatizer = WordNetLemmatizer()\n",
    "from nltk.tokenize import word_tokenize\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_documents = pd.read_csv(r'C:\\Users\\Dell\\Desktop\\ML\\Twitter Sentimental Analysis\\0000000000002747_training_twitter_x_y_train (1).csv')\n",
    "testing_documents = pd.read_csv(r'C:\\Users\\Dell\\Desktop\\ML\\Twitter Sentimental Analysis\\0000000000002747_test_twitter_x_test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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</th>\n",
       "      <th>airline_sentiment_gold</th>\n",
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       "      <td>ColeyGirouard</td>\n",
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       "      <td>0</td>\n",
       "      <td>@SouthwestAir I am scheduled for the morning, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-17 20:16:29 -0800</td>\n",
       "      <td>Washington D.C.</td>\n",
       "      <td>Atlantic Time (Canada)</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>569989168903819264</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>WalterFaddoul</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@SouthwestAir seeing your workers time in and ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-23 14:36:22 -0800</td>\n",
       "      <td>Indianapolis, Indiana; USA</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>568089179520954368</td>\n",
       "      <td>positive</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LocalKyle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@united Flew ORD to Miami and back and  had gr...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-18 08:46:29 -0800</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>568928195581513728</td>\n",
       "      <td>negative</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>amccarthy19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@SouthwestAir @dultch97 that's horse radish 😤🐴</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-20 16:20:26 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Atlantic Time (Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>568594180014014464</td>\n",
       "      <td>negative</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>J_Okayy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@united so our flight into ORD was delayed bec...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-19 18:13:11 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>569677636613439488</td>\n",
       "      <td>negative</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bobgiolito</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@united Why did you load us in this flying sar...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-22 17:58:27 -0800</td>\n",
       "      <td>Los Angeles, CA</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>569658903044218880</td>\n",
       "      <td>negative</td>\n",
       "      <td>Delta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>aaronkinnari</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@JetBlue that is a stock response. Delays not ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-22 16:44:00 -0800</td>\n",
       "      <td>Gotham</td>\n",
       "      <td>Quito</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>568542766860541952</td>\n",
       "      <td>positive</td>\n",
       "      <td>Delta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TimothySays</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@JetBlue That'd be nice! Hoping to rack up eno...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-19 14:48:53 -0800</td>\n",
       "      <td>Burlington, MA</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>570116209263427584</td>\n",
       "      <td>negative</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>lindaSWC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>@united frankly worse customer service ever. P...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-23 23:01:11 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>568870144891600896</td>\n",
       "      <td>positive</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>amyums</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@SouthwestAir yeah haha. Never been in one. It...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-20 12:29:46 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>567793635892174848</td>\n",
       "      <td>negative</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Faultie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@SouthwestAir MCO-&amp;gt;DCA flight almost full o...</td>\n",
       "      <td>[38.73693556, -90.35367062]</td>\n",
       "      <td>2015-02-17 13:12:06 -0800</td>\n",
       "      <td>D.C.</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>569879497891303424</td>\n",
       "      <td>neutral</td>\n",
       "      <td>Delta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>tonymbove</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@JetBlue what's the easiest way to get a ticke...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-23 07:20:34 -0800</td>\n",
       "      <td>Cambridge, MA</td>\n",
       "      <td>Quito</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>568517827482685440</td>\n",
       "      <td>positive</td>\n",
       "      <td>US Airways</td>\n",
       "      <td>NaN</td>\n",
       "      <td>sherrylweir</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@USAirways - love the changes in the lounge - ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-19 13:09:47 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Quito</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>568212893012860928</td>\n",
       "      <td>negative</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mselite25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@SouthwestAir I receive bad customer service a...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-18 16:58:05 -0800</td>\n",
       "      <td>Rite behind my $$</td>\n",
       "      <td>Mountain Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>569270187725160449</td>\n",
       "      <td>negative</td>\n",
       "      <td>US Airways</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KennethBracy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@USAirways you're killing me sche a flight to ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-21 14:59:23 -0800</td>\n",
       "      <td>Where ever God puts me</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>567816651900583936</td>\n",
       "      <td>negative</td>\n",
       "      <td>US Airways</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SyncSummit</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@USAirways 8 weeks to refund this ticket - 037...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-17 14:43:33 -0800</td>\n",
       "      <td>Where Music meets Visual Media</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>569690131235340288</td>\n",
       "      <td>negative</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lioninflorida</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir no private msg me and will provid...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-22 18:48:06 -0800</td>\n",
       "      <td>Fort Myers, Florida</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>569932678688055296</td>\n",
       "      <td>negative</td>\n",
       "      <td>Delta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TheBuie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22</td>\n",
       "      <td>can you not? RT @JetBlue Our fleet's on fleek....</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-23 10:51:54 -0800</td>\n",
       "      <td>Raleigh, NC</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>569939494465462272</td>\n",
       "      <td>negative</td>\n",
       "      <td>Delta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TheRealOx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@JetBlue any info on why flight 704 from SJU t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-23 11:18:59 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>569958224968982528</td>\n",
       "      <td>negative</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rizzilient</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir, I've tried no less than 8 times ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-23 12:33:24 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>568361346464497664</td>\n",
       "      <td>negative</td>\n",
       "      <td>US Airways</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TomFutureforall</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@USAirways new problem...had wrong last name o...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-19 02:47:59 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>568126682336534529</td>\n",
       "      <td>neutral</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DoctorFox007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@SouthwestAir Any possible chance of SWA bring...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-18 11:15:31 -0800</td>\n",
       "      <td>ÜT: 40.819919,-73.864869</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>568883826333777920</td>\n",
       "      <td>negative</td>\n",
       "      <td>US Airways</td>\n",
       "      <td>NaN</td>\n",
       "      <td>chrisseifert83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@USAirways @AmericanAir who runs the show at t...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-20 13:24:08 -0800</td>\n",
       "      <td>Stamford, Ct</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>570055700790931456</td>\n",
       "      <td>negative</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JeffPKirkland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@united computers are down but you stopped giv...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-23 19:00:44 -0800</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Pacific Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>568804026789138433</td>\n",
       "      <td>positive</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jonatzzValdez</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@united good fly!! #United #businessFirst</td>\n",
       "      <td>[29.98445184, -95.33224657]</td>\n",
       "      <td>2015-02-20 08:07:02 -0800</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>567925054853496832</td>\n",
       "      <td>neutral</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MusseZam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@united I am flying from msp to dxb, my child ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-17 21:54:19 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>568434301496381441</td>\n",
       "      <td>negative</td>\n",
       "      <td>Delta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Old_bauer</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@JetBlue loves Cancelled Flightling tickets an...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-19 07:37:53 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>570083397873373184</td>\n",
       "      <td>negative</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KeenanPam</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@united In ORD, waited 20 min after crew membe...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-23 20:50:48 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>568585356586586113</td>\n",
       "      <td>positive</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Salem_Eways</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@SouthwestAir Jason (108639) at Gate #3 in SAN...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-19 17:38:07 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>570022265343123456</td>\n",
       "      <td>positive</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MarkMan23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir everything for sorted out. Thanks...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-23 16:47:53 -0800</td>\n",
       "      <td>San Diego, CA</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10950</th>\n",
       "      <td>569125697530101760</td>\n",
       "      <td>positive</td>\n",
       "      <td>Delta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dress4YesConJax</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@JetBlue Even though this flight #226 didn't h...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-21 05:25:14 -0800</td>\n",
       "      <td>NYC</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10951</th>\n",
       "      <td>569682747712770048</td>\n",
       "      <td>negative</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>horacioguapo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@SouthwestAir very disappointed in your handli...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-22 18:18:45 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10952</th>\n",
       "      <td>569372333267394560</td>\n",
       "      <td>negative</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Angela_Meyers</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@SouthwestAir flight Cancelled Flightled out o...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-21 21:45:17 -0800</td>\n",
       "      <td>Hershey, PA</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10953</th>\n",
       "      <td>569333972452265985</td>\n",
       "      <td>negative</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jo_Frost</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@united when I read it say ' in some cases' ca...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-21 19:12:51 -0800</td>\n",
       "      <td>Global</td>\n",
       "      <td>Quito</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10954</th>\n",
       "      <td>568426579874996224</td>\n",
       "      <td>negative</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>thirty_lives</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@SouthwestAir you're giving everyone tix for #...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-19 07:07:12 -0800</td>\n",
       "      <td>ʞᴚoʎ   ʍǝu</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10955</th>\n",
       "      <td>570018254271463424</td>\n",
       "      <td>negative</td>\n",
       "      <td>Delta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4TONEMENT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@JetBlue Am I the only one who had to look tha...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-23 16:31:56 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10956</th>\n",
       "      <td>568118177537744897</td>\n",
       "      <td>neutral</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BobbiStorm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@SouthwestAir go south everyone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-18 10:41:43 -0800</td>\n",
       "      <td>Miami</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10957</th>\n",
       "      <td>568522861205266432</td>\n",
       "      <td>negative</td>\n",
       "      <td>US Airways</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VirginiaRmz</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@USAirways 4 hours Late Flightr And i lost my ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-19 13:29:47 -0800</td>\n",
       "      <td>Mexico, D.F.</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10958</th>\n",
       "      <td>570290993339244547</td>\n",
       "      <td>negative</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nate2482</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@united I sure did.   I had to drive a total o...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 10:35:42 -0800</td>\n",
       "      <td>Parkersburg, WV</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10959</th>\n",
       "      <td>569624009354170369</td>\n",
       "      <td>negative</td>\n",
       "      <td>US Airways</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dave_cavan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@USAirways his name is Rett Cavan, he has a go...</td>\n",
       "      <td>[43.19825137, -70.87335749]</td>\n",
       "      <td>2015-02-22 14:25:21 -0800</td>\n",
       "      <td>Livonia, MI</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10960</th>\n",
       "      <td>568841631585021952</td>\n",
       "      <td>negative</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>iamawarplane</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>@united 3 times my flight has been delayed and...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-20 10:36:28 -0800</td>\n",
       "      <td>Austin, Texas</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10961</th>\n",
       "      <td>569262534232039427</td>\n",
       "      <td>negative</td>\n",
       "      <td>Delta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>rosenrj</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@JetBlue there is no supervisor available, so ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-21 14:28:59 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10962</th>\n",
       "      <td>569308255966666753</td>\n",
       "      <td>neutral</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>gwen1013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@united following</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-21 17:30:40 -0800</td>\n",
       "      <td>New Haven, CT</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10963</th>\n",
       "      <td>569369008580874240</td>\n",
       "      <td>negative</td>\n",
       "      <td>US Airways</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dailylaurel</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@USAirways even alternate options are allready...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-21 21:32:04 -0800</td>\n",
       "      <td>Bruxelle-Paris-Marseille-(BPM)</td>\n",
       "      <td>Athens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10964</th>\n",
       "      <td>569008708174262272</td>\n",
       "      <td>negative</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>johnwoodRTR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@united Also your at-gate monitor showed 23 em...</td>\n",
       "      <td>[39.83426941, -104.69960636]</td>\n",
       "      <td>2015-02-20 21:40:22 -0800</td>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10965</th>\n",
       "      <td>570164810144481280</td>\n",
       "      <td>neutral</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8629Fissile</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@united Okay thank you</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 02:14:18 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10966</th>\n",
       "      <td>568417151176060929</td>\n",
       "      <td>negative</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brennej</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@united - vacation days: relevant, Ritz in PR ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-19 06:29:44 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10967</th>\n",
       "      <td>567617081336950784</td>\n",
       "      <td>negative</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mrshossruns</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@SouthwestAir you guys there? Are we on hour 2...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-17 01:30:32 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10968</th>\n",
       "      <td>569664787946164224</td>\n",
       "      <td>negative</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>vickie_frisbie</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir we are through. I can quit you. I...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-22 17:07:23 -0800</td>\n",
       "      <td>Dallas, Texas</td>\n",
       "      <td>Mountain Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10969</th>\n",
       "      <td>568531020267458561</td>\n",
       "      <td>positive</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>stefughknee</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@SouthwestAir thank you! ❤️❤️❤️ you guys!</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-19 14:02:12 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10970</th>\n",
       "      <td>570283248301043712</td>\n",
       "      <td>negative</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>slobotski</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@SouthwestAir went to purchase a flight that I...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-24 10:04:56 -0800</td>\n",
       "      <td>Midwest + Airplanes</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10971</th>\n",
       "      <td>569087442092826624</td>\n",
       "      <td>neutral</td>\n",
       "      <td>Delta</td>\n",
       "      <td>NaN</td>\n",
       "      <td>mutantgatto</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@JetBlue by any chance do u offer fresh guacam...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-21 02:53:13 -0800</td>\n",
       "      <td>ravioli</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10972</th>\n",
       "      <td>570051488875020288</td>\n",
       "      <td>negative</td>\n",
       "      <td>US Airways</td>\n",
       "      <td>NaN</td>\n",
       "      <td>m2gris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@USAirways - 53 minutes on hold for a reservat...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-23 18:44:00 -0800</td>\n",
       "      <td>Irvine, Ca</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10973</th>\n",
       "      <td>569676702030262272</td>\n",
       "      <td>negative</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>realscooterward</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir the worst air experience I have e...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-22 17:54:44 -0800</td>\n",
       "      <td>washington, dc</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10974</th>\n",
       "      <td>569676404893163520</td>\n",
       "      <td>positive</td>\n",
       "      <td>Southwest</td>\n",
       "      <td>NaN</td>\n",
       "      <td>happygirlvicki</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@SouthwestAir beautiful view flying into San J...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-22 17:53:33 -0800</td>\n",
       "      <td>Omaha, Nebraska</td>\n",
       "      <td>Central Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10975</th>\n",
       "      <td>569934458364813313</td>\n",
       "      <td>neutral</td>\n",
       "      <td>American</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cottopanama85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@AmericanAir followback</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-23 10:58:58 -0800</td>\n",
       "      <td>ohio,panama</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10976</th>\n",
       "      <td>568564006329434113</td>\n",
       "      <td>positive</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PaulBEsteves</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@united thanks for the help. Wish the phone re...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-19 16:13:17 -0800</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>Eastern Time (US &amp; Canada)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10977</th>\n",
       "      <td>569643648910028801</td>\n",
       "      <td>negative</td>\n",
       "      <td>US Airways</td>\n",
       "      <td>NaN</td>\n",
       "      <td>runfixsteve</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@usairways the. Worst. Ever. #dca #customerser...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-22 15:43:24 -0800</td>\n",
       "      <td>St. Augustine, Florida</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10978</th>\n",
       "      <td>568864981917110272</td>\n",
       "      <td>negative</td>\n",
       "      <td>US Airways</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CLChicosky</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>@nrhodes85: look! Another apology. DO NOT FLY ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-20 12:09:15 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10979</th>\n",
       "      <td>568929299350179840</td>\n",
       "      <td>negative</td>\n",
       "      <td>United</td>\n",
       "      <td>NaN</td>\n",
       "      <td>JW_Blocker</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>@united you are by far the worst airline. 4 pl...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-02-20 16:24:49 -0800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10980 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 tweet_id airline_sentiment     airline  \\\n",
       "0      567900433542488064          negative   Southwest   \n",
       "1      569989168903819264          positive   Southwest   \n",
       "2      568089179520954368          positive      United   \n",
       "3      568928195581513728          negative   Southwest   \n",
       "4      568594180014014464          negative      United   \n",
       "5      569677636613439488          negative      United   \n",
       "6      569658903044218880          negative       Delta   \n",
       "7      568542766860541952          positive       Delta   \n",
       "8      570116209263427584          negative      United   \n",
       "9      568870144891600896          positive   Southwest   \n",
       "10     567793635892174848          negative   Southwest   \n",
       "11     569879497891303424           neutral       Delta   \n",
       "12     568517827482685440          positive  US Airways   \n",
       "13     568212893012860928          negative   Southwest   \n",
       "14     569270187725160449          negative  US Airways   \n",
       "15     567816651900583936          negative  US Airways   \n",
       "16     569690131235340288          negative    American   \n",
       "17     569932678688055296          negative       Delta   \n",
       "18     569939494465462272          negative       Delta   \n",
       "19     569958224968982528          negative    American   \n",
       "20     568361346464497664          negative  US Airways   \n",
       "21     568126682336534529           neutral   Southwest   \n",
       "22     568883826333777920          negative  US Airways   \n",
       "23     570055700790931456          negative      United   \n",
       "24     568804026789138433          positive      United   \n",
       "25     567925054853496832           neutral      United   \n",
       "26     568434301496381441          negative       Delta   \n",
       "27     570083397873373184          negative      United   \n",
       "28     568585356586586113          positive   Southwest   \n",
       "29     570022265343123456          positive    American   \n",
       "...                   ...               ...         ...   \n",
       "10950  569125697530101760          positive       Delta   \n",
       "10951  569682747712770048          negative   Southwest   \n",
       "10952  569372333267394560          negative   Southwest   \n",
       "10953  569333972452265985          negative      United   \n",
       "10954  568426579874996224          negative   Southwest   \n",
       "10955  570018254271463424          negative       Delta   \n",
       "10956  568118177537744897           neutral   Southwest   \n",
       "10957  568522861205266432          negative  US Airways   \n",
       "10958  570290993339244547          negative      United   \n",
       "10959  569624009354170369          negative  US Airways   \n",
       "10960  568841631585021952          negative      United   \n",
       "10961  569262534232039427          negative       Delta   \n",
       "10962  569308255966666753           neutral      United   \n",
       "10963  569369008580874240          negative  US Airways   \n",
       "10964  569008708174262272          negative      United   \n",
       "10965  570164810144481280           neutral      United   \n",
       "10966  568417151176060929          negative      United   \n",
       "10967  567617081336950784          negative   Southwest   \n",
       "10968  569664787946164224          negative    American   \n",
       "10969  568531020267458561          positive   Southwest   \n",
       "10970  570283248301043712          negative   Southwest   \n",
       "10971  569087442092826624           neutral       Delta   \n",
       "10972  570051488875020288          negative  US Airways   \n",
       "10973  569676702030262272          negative    American   \n",
       "10974  569676404893163520          positive   Southwest   \n",
       "10975  569934458364813313           neutral    American   \n",
       "10976  568564006329434113          positive      United   \n",
       "10977  569643648910028801          negative  US Airways   \n",
       "10978  568864981917110272          negative  US Airways   \n",
       "10979  568929299350179840          negative      United   \n",
       "\n",
       "      airline_sentiment_gold             name negativereason_gold  \\\n",
       "0                        NaN    ColeyGirouard                 NaN   \n",
       "1                        NaN    WalterFaddoul                 NaN   \n",
       "2                        NaN        LocalKyle                 NaN   \n",
       "3                        NaN      amccarthy19                 NaN   \n",
       "4                        NaN          J_Okayy                 NaN   \n",
       "5                        NaN       bobgiolito                 NaN   \n",
       "6                        NaN     aaronkinnari                 NaN   \n",
       "7                        NaN      TimothySays                 NaN   \n",
       "8                        NaN         lindaSWC                 NaN   \n",
       "9                        NaN           amyums                 NaN   \n",
       "10                       NaN          Faultie                 NaN   \n",
       "11                       NaN        tonymbove                 NaN   \n",
       "12                       NaN      sherrylweir                 NaN   \n",
       "13                       NaN        Mselite25                 NaN   \n",
       "14                       NaN     KennethBracy                 NaN   \n",
       "15                       NaN       SyncSummit                 NaN   \n",
       "16                       NaN    Lioninflorida                 NaN   \n",
       "17                       NaN          TheBuie                 NaN   \n",
       "18                       NaN        TheRealOx                 NaN   \n",
       "19                       NaN       Rizzilient                 NaN   \n",
       "20                       NaN  TomFutureforall                 NaN   \n",
       "21                       NaN     DoctorFox007                 NaN   \n",
       "22                       NaN   chrisseifert83                 NaN   \n",
       "23                       NaN    JeffPKirkland                 NaN   \n",
       "24                       NaN    jonatzzValdez                 NaN   \n",
       "25                       NaN         MusseZam                 NaN   \n",
       "26                       NaN        Old_bauer                 NaN   \n",
       "27                       NaN        KeenanPam                 NaN   \n",
       "28                       NaN      Salem_Eways                 NaN   \n",
       "29                       NaN        MarkMan23                 NaN   \n",
       "...                      ...              ...                 ...   \n",
       "10950                    NaN  Dress4YesConJax                 NaN   \n",
       "10951                    NaN     horacioguapo                 NaN   \n",
       "10952                    NaN    Angela_Meyers                 NaN   \n",
       "10953                    NaN         Jo_Frost                 NaN   \n",
       "10954                    NaN     thirty_lives                 NaN   \n",
       "10955                    NaN        4TONEMENT                 NaN   \n",
       "10956                    NaN       BobbiStorm                 NaN   \n",
       "10957                    NaN      VirginiaRmz                 NaN   \n",
       "10958                    NaN         nate2482                 NaN   \n",
       "10959                    NaN       dave_cavan                 NaN   \n",
       "10960                    NaN     iamawarplane                 NaN   \n",
       "10961                    NaN          rosenrj                 NaN   \n",
       "10962                    NaN         gwen1013                 NaN   \n",
       "10963                    NaN      dailylaurel                 NaN   \n",
       "10964                    NaN      johnwoodRTR                 NaN   \n",
       "10965                    NaN      8629Fissile                 NaN   \n",
       "10966                    NaN          Brennej                 NaN   \n",
       "10967                    NaN      mrshossruns                 NaN   \n",
       "10968                    NaN   vickie_frisbie                 NaN   \n",
       "10969                    NaN      stefughknee                 NaN   \n",
       "10970                    NaN        slobotski                 NaN   \n",
       "10971                    NaN      mutantgatto                 NaN   \n",
       "10972                    NaN           m2gris                 NaN   \n",
       "10973                    NaN  realscooterward                 NaN   \n",
       "10974                    NaN   happygirlvicki                 NaN   \n",
       "10975                    NaN    Cottopanama85                 NaN   \n",
       "10976                    NaN     PaulBEsteves                 NaN   \n",
       "10977                    NaN      runfixsteve                 NaN   \n",
       "10978                    NaN       CLChicosky                 NaN   \n",
       "10979                    NaN       JW_Blocker                 NaN   \n",
       "\n",
       "       retweet_count                                               text  \\\n",
       "0                  0  @SouthwestAir I am scheduled for the morning, ...   \n",
       "1                  0  @SouthwestAir seeing your workers time in and ...   \n",
       "2                  0  @united Flew ORD to Miami and back and  had gr...   \n",
       "3                  0     @SouthwestAir @dultch97 that's horse radish 😤🐴   \n",
       "4                  0  @united so our flight into ORD was delayed bec...   \n",
       "5                  0  @united Why did you load us in this flying sar...   \n",
       "6                  0  @JetBlue that is a stock response. Delays not ...   \n",
       "7                  0  @JetBlue That'd be nice! Hoping to rack up eno...   \n",
       "8                  1  @united frankly worse customer service ever. P...   \n",
       "9                  0  @SouthwestAir yeah haha. Never been in one. It...   \n",
       "10                 0  @SouthwestAir MCO-&gt;DCA flight almost full o...   \n",
       "11                 0  @JetBlue what's the easiest way to get a ticke...   \n",
       "12                 0  @USAirways - love the changes in the lounge - ...   \n",
       "13                 0  @SouthwestAir I receive bad customer service a...   \n",
       "14                 0  @USAirways you're killing me sche a flight to ...   \n",
       "15                 0  @USAirways 8 weeks to refund this ticket - 037...   \n",
       "16                 0  @AmericanAir no private msg me and will provid...   \n",
       "17                22  can you not? RT @JetBlue Our fleet's on fleek....   \n",
       "18                 0  @JetBlue any info on why flight 704 from SJU t...   \n",
       "19                 0  @AmericanAir, I've tried no less than 8 times ...   \n",
       "20                 0  @USAirways new problem...had wrong last name o...   \n",
       "21                 0  @SouthwestAir Any possible chance of SWA bring...   \n",
       "22                 0  @USAirways @AmericanAir who runs the show at t...   \n",
       "23                 0  @united computers are down but you stopped giv...   \n",
       "24                 0          @united good fly!! #United #businessFirst   \n",
       "25                 0  @united I am flying from msp to dxb, my child ...   \n",
       "26                 0  @JetBlue loves Cancelled Flightling tickets an...   \n",
       "27                 0  @united In ORD, waited 20 min after crew membe...   \n",
       "28                 0  @SouthwestAir Jason (108639) at Gate #3 in SAN...   \n",
       "29                 0  @AmericanAir everything for sorted out. Thanks...   \n",
       "...              ...                                                ...   \n",
       "10950              0  @JetBlue Even though this flight #226 didn't h...   \n",
       "10951              0  @SouthwestAir very disappointed in your handli...   \n",
       "10952              0  @SouthwestAir flight Cancelled Flightled out o...   \n",
       "10953              0  @united when I read it say ' in some cases' ca...   \n",
       "10954              0  @SouthwestAir you're giving everyone tix for #...   \n",
       "10955              0  @JetBlue Am I the only one who had to look tha...   \n",
       "10956              0                    @SouthwestAir go south everyone   \n",
       "10957              0  @USAirways 4 hours Late Flightr And i lost my ...   \n",
       "10958              0  @united I sure did.   I had to drive a total o...   \n",
       "10959              0  @USAirways his name is Rett Cavan, he has a go...   \n",
       "10960              1  @united 3 times my flight has been delayed and...   \n",
       "10961              0  @JetBlue there is no supervisor available, so ...   \n",
       "10962              0                                  @united following   \n",
       "10963              0  @USAirways even alternate options are allready...   \n",
       "10964              0  @united Also your at-gate monitor showed 23 em...   \n",
       "10965              0                             @united Okay thank you   \n",
       "10966              0  @united - vacation days: relevant, Ritz in PR ...   \n",
       "10967              0  @SouthwestAir you guys there? Are we on hour 2...   \n",
       "10968              0  @AmericanAir we are through. I can quit you. I...   \n",
       "10969              0          @SouthwestAir thank you! ❤️❤️❤️ you guys!   \n",
       "10970              0  @SouthwestAir went to purchase a flight that I...   \n",
       "10971              0  @JetBlue by any chance do u offer fresh guacam...   \n",
       "10972              0  @USAirways - 53 minutes on hold for a reservat...   \n",
       "10973              0  @AmericanAir the worst air experience I have e...   \n",
       "10974              0  @SouthwestAir beautiful view flying into San J...   \n",
       "10975              0                            @AmericanAir followback   \n",
       "10976              0  @united thanks for the help. Wish the phone re...   \n",
       "10977              0  @usairways the. Worst. Ever. #dca #customerser...   \n",
       "10978              0  @nrhodes85: look! Another apology. DO NOT FLY ...   \n",
       "10979              1  @united you are by far the worst airline. 4 pl...   \n",
       "\n",
       "                        tweet_coord              tweet_created  \\\n",
       "0                               NaN  2015-02-17 20:16:29 -0800   \n",
       "1                               NaN  2015-02-23 14:36:22 -0800   \n",
       "2                               NaN  2015-02-18 08:46:29 -0800   \n",
       "3                               NaN  2015-02-20 16:20:26 -0800   \n",
       "4                               NaN  2015-02-19 18:13:11 -0800   \n",
       "5                               NaN  2015-02-22 17:58:27 -0800   \n",
       "6                               NaN  2015-02-22 16:44:00 -0800   \n",
       "7                               NaN  2015-02-19 14:48:53 -0800   \n",
       "8                               NaN  2015-02-23 23:01:11 -0800   \n",
       "9                               NaN  2015-02-20 12:29:46 -0800   \n",
       "10      [38.73693556, -90.35367062]  2015-02-17 13:12:06 -0800   \n",
       "11                              NaN  2015-02-23 07:20:34 -0800   \n",
       "12                              NaN  2015-02-19 13:09:47 -0800   \n",
       "13                              NaN  2015-02-18 16:58:05 -0800   \n",
       "14                              NaN  2015-02-21 14:59:23 -0800   \n",
       "15                              NaN  2015-02-17 14:43:33 -0800   \n",
       "16                              NaN  2015-02-22 18:48:06 -0800   \n",
       "17                              NaN  2015-02-23 10:51:54 -0800   \n",
       "18                              NaN  2015-02-23 11:18:59 -0800   \n",
       "19                              NaN  2015-02-23 12:33:24 -0800   \n",
       "20                              NaN  2015-02-19 02:47:59 -0800   \n",
       "21                              NaN  2015-02-18 11:15:31 -0800   \n",
       "22                              NaN  2015-02-20 13:24:08 -0800   \n",
       "23                              NaN  2015-02-23 19:00:44 -0800   \n",
       "24      [29.98445184, -95.33224657]  2015-02-20 08:07:02 -0800   \n",
       "25                              NaN  2015-02-17 21:54:19 -0800   \n",
       "26                              NaN  2015-02-19 07:37:53 -0800   \n",
       "27                              NaN  2015-02-23 20:50:48 -0800   \n",
       "28                              NaN  2015-02-19 17:38:07 -0800   \n",
       "29                              NaN  2015-02-23 16:47:53 -0800   \n",
       "...                             ...                        ...   \n",
       "10950                           NaN  2015-02-21 05:25:14 -0800   \n",
       "10951                           NaN  2015-02-22 18:18:45 -0800   \n",
       "10952                           NaN  2015-02-21 21:45:17 -0800   \n",
       "10953                           NaN  2015-02-21 19:12:51 -0800   \n",
       "10954                           NaN  2015-02-19 07:07:12 -0800   \n",
       "10955                           NaN  2015-02-23 16:31:56 -0800   \n",
       "10956                           NaN  2015-02-18 10:41:43 -0800   \n",
       "10957                           NaN  2015-02-19 13:29:47 -0800   \n",
       "10958                           NaN  2015-02-24 10:35:42 -0800   \n",
       "10959   [43.19825137, -70.87335749]  2015-02-22 14:25:21 -0800   \n",
       "10960                           NaN  2015-02-20 10:36:28 -0800   \n",
       "10961                           NaN  2015-02-21 14:28:59 -0800   \n",
       "10962                           NaN  2015-02-21 17:30:40 -0800   \n",
       "10963                           NaN  2015-02-21 21:32:04 -0800   \n",
       "10964  [39.83426941, -104.69960636]  2015-02-20 21:40:22 -0800   \n",
       "10965                           NaN  2015-02-24 02:14:18 -0800   \n",
       "10966                           NaN  2015-02-19 06:29:44 -0800   \n",
       "10967                           NaN  2015-02-17 01:30:32 -0800   \n",
       "10968                           NaN  2015-02-22 17:07:23 -0800   \n",
       "10969                           NaN  2015-02-19 14:02:12 -0800   \n",
       "10970                           NaN  2015-02-24 10:04:56 -0800   \n",
       "10971                           NaN  2015-02-21 02:53:13 -0800   \n",
       "10972                           NaN  2015-02-23 18:44:00 -0800   \n",
       "10973                           NaN  2015-02-22 17:54:44 -0800   \n",
       "10974                           NaN  2015-02-22 17:53:33 -0800   \n",
       "10975                           NaN  2015-02-23 10:58:58 -0800   \n",
       "10976                           NaN  2015-02-19 16:13:17 -0800   \n",
       "10977                           NaN  2015-02-22 15:43:24 -0800   \n",
       "10978                           NaN  2015-02-20 12:09:15 -0800   \n",
       "10979                           NaN  2015-02-20 16:24:49 -0800   \n",
       "\n",
       "                       tweet_location                user_timezone  \n",
       "0                     Washington D.C.       Atlantic Time (Canada)  \n",
       "1          Indianapolis, Indiana; USA   Central Time (US & Canada)  \n",
       "2                            Illinois   Central Time (US & Canada)  \n",
       "3                                 NaN       Atlantic Time (Canada)  \n",
       "4                                 NaN   Eastern Time (US & Canada)  \n",
       "5                     Los Angeles, CA   Pacific Time (US & Canada)  \n",
       "6                              Gotham                        Quito  \n",
       "7                      Burlington, MA   Eastern Time (US & Canada)  \n",
       "8                                 NaN                          NaN  \n",
       "9                                 NaN   Central Time (US & Canada)  \n",
       "10                               D.C.                          NaN  \n",
       "11                      Cambridge, MA                        Quito  \n",
       "12                                NaN                        Quito  \n",
       "13                  Rite behind my $$  Mountain Time (US & Canada)  \n",
       "14             Where ever God puts me                          NaN  \n",
       "15     Where Music meets Visual Media   Eastern Time (US & Canada)  \n",
       "16                Fort Myers, Florida   Eastern Time (US & Canada)  \n",
       "17                        Raleigh, NC   Eastern Time (US & Canada)  \n",
       "18                                NaN   Eastern Time (US & Canada)  \n",
       "19                                NaN                          NaN  \n",
       "20                                NaN                          NaN  \n",
       "21           ÜT: 40.819919,-73.864869   Eastern Time (US & Canada)  \n",
       "22                       Stamford, Ct   Eastern Time (US & Canada)  \n",
       "23                             Oregon   Pacific Time (US & Canada)  \n",
       "24                             Mexico   Central Time (US & Canada)  \n",
       "25                                NaN                          NaN  \n",
       "26                                NaN   Eastern Time (US & Canada)  \n",
       "27                                NaN                          NaN  \n",
       "28                                NaN                          NaN  \n",
       "29                      San Diego, CA   Pacific Time (US & Canada)  \n",
       "...                               ...                          ...  \n",
       "10950                            NYC                           NaN  \n",
       "10951                             NaN                          NaN  \n",
       "10952                     Hershey, PA   Eastern Time (US & Canada)  \n",
       "10953                          Global                        Quito  \n",
       "10954                      ʞᴚoʎ   ʍǝu   Eastern Time (US & Canada)  \n",
       "10955                             NaN   Eastern Time (US & Canada)  \n",
       "10956                           Miami                          NaN  \n",
       "10957                    Mexico, D.F.   Central Time (US & Canada)  \n",
       "10958                 Parkersburg, WV   Eastern Time (US & Canada)  \n",
       "10959                     Livonia, MI                          NaN  \n",
       "10960                   Austin, Texas                          NaN  \n",
       "10961                             NaN                          NaN  \n",
       "10962                   New Haven, CT                          NaN  \n",
       "10963  Bruxelle-Paris-Marseille-(BPM)                       Athens  \n",
       "10964                       Hong Kong   Eastern Time (US & Canada)  \n",
       "10965                             NaN                       London  \n",
       "10966                             NaN                          NaN  \n",
       "10967                             NaN   Eastern Time (US & Canada)  \n",
       "10968                   Dallas, Texas  Mountain Time (US & Canada)  \n",
       "10969                             NaN                          NaN  \n",
       "10970             Midwest + Airplanes   Central Time (US & Canada)  \n",
       "10971                         ravioli   Eastern Time (US & Canada)  \n",
       "10972                      Irvine, Ca                          NaN  \n",
       "10973                  washington, dc   Eastern Time (US & Canada)  \n",
       "10974                 Omaha, Nebraska   Central Time (US & Canada)  \n",
       "10975                     ohio,panama                          NaN  \n",
       "10976                        Brooklyn   Eastern Time (US & Canada)  \n",
       "10977          St. Augustine, Florida                          NaN  \n",
       "10978                             NaN                          NaN  \n",
       "10979                             NaN                          NaN  \n",
       "\n",
       "[10980 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets = training_documents.iloc[:,7]\n",
    "test_tweets = testing_documents.iloc[:,6]\n",
    "type(tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = training_documents.iloc[:,1:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['negative'],\n",
       "       ['positive'],\n",
       "       ['positive'],\n",
       "       ...,\n",
       "       ['negative'],\n",
       "       ['negative'],\n",
       "       ['negative']], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_simple_pos(tag) :\n",
    "    if tag.startswith('J') :\n",
    "        return wordnet.ADJ\n",
    "    elif tag.startswith('V') :\n",
    "        return wordnet.VERB\n",
    "    elif tag.startswith('N') :\n",
    "        return wordnet.NOUN\n",
    "    elif tag.startswith('R') :\n",
    "        return wordnet.ADV\n",
    "    else:\n",
    "        return wordnet.NOUN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'!',\n",
       " '\"',\n",
       " '#',\n",
       " '$',\n",
       " '%',\n",
       " '&',\n",
       " \"'\",\n",
       " '(',\n",
       " ')',\n",
       " '*',\n",
       " '+',\n",
       " ',',\n",
       " '-',\n",
       " '.',\n",
       " '/',\n",
       " ':',\n",
       " ';',\n",
       " '<',\n",
       " '=',\n",
       " '>',\n",
       " '?',\n",
       " '@',\n",
       " '[',\n",
       " '\\\\',\n",
       " ']',\n",
       " '^',\n",
       " '_',\n",
       " '`',\n",
       " 'a',\n",
       " 'about',\n",
       " 'above',\n",
       " 'after',\n",
       " 'again',\n",
       " 'against',\n",
       " 'ain',\n",
       " 'all',\n",
       " 'am',\n",
       " 'an',\n",
       " 'and',\n",
       " 'any',\n",
       " 'are',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'as',\n",
       " 'at',\n",
       " 'be',\n",
       " 'because',\n",
       " 'been',\n",
       " 'before',\n",
       " 'being',\n",
       " 'below',\n",
       " 'between',\n",
       " 'both',\n",
       " 'but',\n",
       " 'by',\n",
       " 'can',\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'd',\n",
       " 'did',\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'do',\n",
       " 'does',\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'doing',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'down',\n",
       " 'during',\n",
       " 'each',\n",
       " 'few',\n",
       " 'for',\n",
       " 'from',\n",
       " 'further',\n",
       " 'had',\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'has',\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'have',\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'having',\n",
       " 'he',\n",
       " 'her',\n",
       " 'here',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'him',\n",
       " 'himself',\n",
       " 'his',\n",
       " 'how',\n",
       " 'i',\n",
       " 'if',\n",
       " 'in',\n",
       " 'into',\n",
       " 'is',\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'just',\n",
       " 'll',\n",
       " 'm',\n",
       " 'ma',\n",
       " 'me',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'more',\n",
       " 'most',\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'my',\n",
       " 'myself',\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'now',\n",
       " 'o',\n",
       " 'of',\n",
       " 'off',\n",
       " 'on',\n",
       " 'once',\n",
       " 'only',\n",
       " 'or',\n",
       " 'other',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'out',\n",
       " 'over',\n",
       " 'own',\n",
       " 're',\n",
       " 's',\n",
       " 'same',\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'so',\n",
       " 'some',\n",
       " 'such',\n",
       " 't',\n",
       " 'than',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'the',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'them',\n",
       " 'themselves',\n",
       " 'then',\n",
       " 'there',\n",
       " 'these',\n",
       " 'they',\n",
       " 'this',\n",
       " 'those',\n",
       " 'through',\n",
       " 'to',\n",
       " 'too',\n",
       " 'under',\n",
       " 'until',\n",
       " 'up',\n",
       " 've',\n",
       " 'very',\n",
       " 'was',\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'we',\n",
       " 'were',\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'what',\n",
       " 'when',\n",
       " 'where',\n",
       " 'which',\n",
       " 'while',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'why',\n",
       " 'will',\n",
       " 'with',\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\",\n",
       " 'y',\n",
       " 'you',\n",
       " \"you'd\",\n",
       " \"you'll\",\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves',\n",
       " '{',\n",
       " '|',\n",
       " '}',\n",
       " '~'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stops = set(stopwords.words('english'))\n",
    "import string\n",
    "punctuations = list(string.punctuation)\n",
    "stops.update(punctuations)\n",
    "stops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cleaning\n",
    "def clean_review(review) :\n",
    "    words = word_tokenize(review)\n",
    "    output_words = []\n",
    "    for w in words :\n",
    "        if w.lower() not in stops :\n",
    "            pos = pos_tag([w])\n",
    "            clean_word = lemmatizer.lemmatize(w,pos = get_simple_pos(pos[0][1]))\n",
    "            output_words.append(clean_word.lower())\n",
    "    #print(output_words)\n",
    "    return \" \".join(output_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'southwestair schedule morning 2 day fact yes..not sure even flight one cancelled flightled'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets = tweets.values\n",
    "tweets = [(clean_review(review)) for review in tweets]\n",
    "tweets[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"americanair car gng dfw pulled 1hr ago icy road on-hold aa since 1hr ca n't reach arpt aa2450 wat 2\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_tweets = test_tweets.values\n",
    "test_tweets = [(clean_review(review)) for review in test_tweets]\n",
    "test_tweets[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_vec = CountVectorizer(max_df = 0.8,min_df = 0.0005)\n",
    "x_train = count_vec.fit_transform(tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        ...,\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0],\n",
       "        [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_split,x_test_split,y_train_split,y_test_split = train_test_split(x_train,y,random_state = 0,test_size = 0.2)\n",
    "x_train.todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1917"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(count_vec.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = count_vec.transform(test_tweets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
      "  \"avoid this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6293260473588342\n"
     ]
    }
   ],
   "source": [
    "#SVC classifier\n",
    "= SVC(kernel = 'rbf')\n",
    "svc.fit(x_train_split,y_train_split)\n",
    "print(svc.score(x_test_split,y_test_split))\n",
    "y_pred = svc.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GridSearchCV(cv='warn', error_score='raise-deprecating',\n",
      "             estimator=SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "                           decision_function_shape='ovr', degree=3,\n",
      "                           gamma='auto_deprecated', kernel='rbf', max_iter=-1,\n",
      "                           probability=False, random_state=None, shrinking=True,\n",
      "                           tol=0.001, verbose=False),\n",
      "             iid='warn', n_jobs=None,\n",
      "             param_grid={'C': [100.0, 1000.0, 5000.0, 10000.0, 5, 50000.0,\n",
      "                               100000.0],\n",
      "                         'gamma': [0.001, 0.0005, 0.0001, 0.005]},\n",
      "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
      "             scoring=None, verbose=0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7636612021857924\n"
     ]
    }
   ],
   "source": [
    "#SVC using gridSearch\n",
    "grid = {'C' : [1e2,1e3,5e3,1e4,5,5e4,1e5],'gamma':[1e-3,5e-4,1e-4,5e-3]}\n",
    "abc  = GridSearchCV(svc,grid)\n",
    "print(abc)\n",
    "abc.fit(x_train_split , y_train_split)\n",
    "print(abc.score(x_test_split,y_test_split))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "abc.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#random forest\n",
    "rf = RandomForestClassifier()\n",
    "rf.fit(x_train_split,y_train_split)\n",
    "print(rf.score(x_test_split,y_test_split))\n",
    "y_pred1 = rf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='warn', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='warn', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#logisitc Regression\n",
    "lr = LogisticRegression()\n",
    "lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:724: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
      "  \"this warning.\", FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7714025500910747\n"
     ]
    }
   ],
   "source": [
    "lr.fit(x_train_split,y_train_split)\n",
    "print(lr.score(x_test_split,y_test_split))\n",
    "y_pred2 = lr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Naive Bayes\n",
    "nb = MultinomialNB()\n",
    "nb.fit(x_train_split,y_train_split)\n",
    "print(nb.score(x_test_split,y_test_split))\n",
    "y_pred3 = nb.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cross validation\n",
    "#tf/idf vectorization\n",
    "#KNN\n",
    "#check if grid search is applicable in vectorization\n",
    "#check if anything else is possible or not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " np.savetxt(r\"C:\\Users\\Dell\\Desktop\\ML\\Twitter Sentimental Analysis\\pred1.csv\",y_pred2,delimiter = \",\", fmt='%s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.511384335154827\n"
     ]
    }
   ],
   "source": [
    "#KNN\n",
    "clf = KNeighborsClassifier()\n",
    "clf.fit(x_train_split , y_train_split)\n",
    "print(clf.score(x_test_split,y_test_split))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:514: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  estimator.fit(X_train, y_train, **fit_params)\n",
      "E:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_search.py:714: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  self.best_estimator_.fit(X, y, **fit_params)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.476775956284153\n"
     ]
    }
   ],
   "source": [
    "grid = {'n_neighbors' : [5,10,50,100,200,500,1000,1250,1500,2000]}\n",
    "bcd  = GridSearchCV(clf,grid)\n",
    "bcd.fit(x_train_split , y_train_split)\n",
    "print(bcd.score(x_test_split,y_test_split))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "                     metric_params=None, n_jobs=None, n_neighbors=1500, p=2,\n",
       "                     weights='uniform')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bcd.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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