Sample analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from nltk.tokenize import word_tokenize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('OnlineNewsPopularity.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_colwidth',-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns',None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>url</th>\n",
       "      <th>timedelta</th>\n",
       "      <th>n_tokens_title</th>\n",
       "      <th>n_tokens_content</th>\n",
       "      <th>n_unique_tokens</th>\n",
       "      <th>n_non_stop_words</th>\n",
       "      <th>n_non_stop_unique_tokens</th>\n",
       "      <th>num_hrefs</th>\n",
       "      <th>num_self_hrefs</th>\n",
       "      <th>num_imgs</th>\n",
       "      <th>num_videos</th>\n",
       "      <th>average_token_length</th>\n",
       "      <th>num_keywords</th>\n",
       "      <th>data_channel_is_lifestyle</th>\n",
       "      <th>data_channel_is_entertainment</th>\n",
       "      <th>data_channel_is_bus</th>\n",
       "      <th>data_channel_is_socmed</th>\n",
       "      <th>data_channel_is_tech</th>\n",
       "      <th>data_channel_is_world</th>\n",
       "      <th>kw_min_min</th>\n",
       "      <th>kw_max_min</th>\n",
       "      <th>kw_avg_min</th>\n",
       "      <th>kw_min_max</th>\n",
       "      <th>kw_max_max</th>\n",
       "      <th>kw_avg_max</th>\n",
       "      <th>kw_min_avg</th>\n",
       "      <th>kw_max_avg</th>\n",
       "      <th>kw_avg_avg</th>\n",
       "      <th>self_reference_min_shares</th>\n",
       "      <th>self_reference_max_shares</th>\n",
       "      <th>self_reference_avg_sharess</th>\n",
       "      <th>weekday_is_monday</th>\n",
       "      <th>weekday_is_tuesday</th>\n",
       "      <th>weekday_is_wednesday</th>\n",
       "      <th>weekday_is_thursday</th>\n",
       "      <th>weekday_is_friday</th>\n",
       "      <th>weekday_is_saturday</th>\n",
       "      <th>weekday_is_sunday</th>\n",
       "      <th>is_weekend</th>\n",
       "      <th>LDA_00</th>\n",
       "      <th>LDA_01</th>\n",
       "      <th>LDA_02</th>\n",
       "      <th>LDA_03</th>\n",
       "      <th>LDA_04</th>\n",
       "      <th>global_subjectivity</th>\n",
       "      <th>global_sentiment_polarity</th>\n",
       "      <th>global_rate_positive_words</th>\n",
       "      <th>global_rate_negative_words</th>\n",
       "      <th>rate_positive_words</th>\n",
       "      <th>rate_negative_words</th>\n",
       "      <th>avg_positive_polarity</th>\n",
       "      <th>min_positive_polarity</th>\n",
       "      <th>max_positive_polarity</th>\n",
       "      <th>avg_negative_polarity</th>\n",
       "      <th>min_negative_polarity</th>\n",
       "      <th>max_negative_polarity</th>\n",
       "      <th>title_subjectivity</th>\n",
       "      <th>title_sentiment_polarity</th>\n",
       "      <th>abs_title_subjectivity</th>\n",
       "      <th>abs_title_sentiment_polarity</th>\n",
       "      <th>shares</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>http://mashable.com/2013/01/07/amazon-instant-video-browser/</td>\n",
       "      <td>731.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>0.663594</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.815385</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.680365</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>496.0</td>\n",
       "      <td>496.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500331</td>\n",
       "      <td>0.378279</td>\n",
       "      <td>0.040005</td>\n",
       "      <td>0.041263</td>\n",
       "      <td>0.040123</td>\n",
       "      <td>0.521617</td>\n",
       "      <td>0.092562</td>\n",
       "      <td>0.045662</td>\n",
       "      <td>0.013699</td>\n",
       "      <td>0.769231</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.378636</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-0.350000</td>\n",
       "      <td>-0.600</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.187500</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.187500</td>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>http://mashable.com/2013/01/07/ap-samsung-sponsored-tweets/</td>\n",
       "      <td>731.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>255.0</td>\n",
       "      <td>0.604743</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.791946</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.913725</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.799756</td>\n",
       "      <td>0.050047</td>\n",
       "      <td>0.050096</td>\n",
       "      <td>0.050101</td>\n",
       "      <td>0.050001</td>\n",
       "      <td>0.341246</td>\n",
       "      <td>0.148948</td>\n",
       "      <td>0.043137</td>\n",
       "      <td>0.015686</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.286915</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-0.118750</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-0.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>http://mashable.com/2013/01/07/apple-40-billion-app-downloads/</td>\n",
       "      <td>731.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>0.575130</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.663866</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.393365</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>918.0</td>\n",
       "      <td>918.0</td>\n",
       "      <td>918.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.217792</td>\n",
       "      <td>0.033334</td>\n",
       "      <td>0.033351</td>\n",
       "      <td>0.033334</td>\n",
       "      <td>0.682188</td>\n",
       "      <td>0.702222</td>\n",
       "      <td>0.323333</td>\n",
       "      <td>0.056872</td>\n",
       "      <td>0.009479</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.495833</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.466667</td>\n",
       "      <td>-0.800</td>\n",
       "      <td>-0.133333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>http://mashable.com/2013/01/07/astronaut-notre-dame-bcs/</td>\n",
       "      <td>731.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>531.0</td>\n",
       "      <td>0.503788</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.665635</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.404896</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.028573</td>\n",
       "      <td>0.419300</td>\n",
       "      <td>0.494651</td>\n",
       "      <td>0.028905</td>\n",
       "      <td>0.028572</td>\n",
       "      <td>0.429850</td>\n",
       "      <td>0.100705</td>\n",
       "      <td>0.041431</td>\n",
       "      <td>0.020716</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.385965</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.8</td>\n",
       "      <td>-0.369697</td>\n",
       "      <td>-0.600</td>\n",
       "      <td>-0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>http://mashable.com/2013/01/07/att-u-verse-apps/</td>\n",
       "      <td>731.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1072.0</td>\n",
       "      <td>0.415646</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.540890</td>\n",
       "      <td>19.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.682836</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>545.0</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>3151.157895</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.028633</td>\n",
       "      <td>0.028794</td>\n",
       "      <td>0.028575</td>\n",
       "      <td>0.028572</td>\n",
       "      <td>0.885427</td>\n",
       "      <td>0.513502</td>\n",
       "      <td>0.281003</td>\n",
       "      <td>0.074627</td>\n",
       "      <td>0.012127</td>\n",
       "      <td>0.860215</td>\n",
       "      <td>0.139785</td>\n",
       "      <td>0.411127</td>\n",
       "      <td>0.033333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.220192</td>\n",
       "      <td>-0.500</td>\n",
       "      <td>-0.050000</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              url   timedelta  \\\n",
       "0  http://mashable.com/2013/01/07/amazon-instant-video-browser/    731.0        \n",
       "1  http://mashable.com/2013/01/07/ap-samsung-sponsored-tweets/     731.0        \n",
       "2  http://mashable.com/2013/01/07/apple-40-billion-app-downloads/  731.0        \n",
       "3  http://mashable.com/2013/01/07/astronaut-notre-dame-bcs/        731.0        \n",
       "4  http://mashable.com/2013/01/07/att-u-verse-apps/                731.0        \n",
       "\n",
       "    n_tokens_title   n_tokens_content   n_unique_tokens   n_non_stop_words  \\\n",
       "0  12.0             219.0              0.663594          1.0                 \n",
       "1  9.0              255.0              0.604743          1.0                 \n",
       "2  9.0              211.0              0.575130          1.0                 \n",
       "3  9.0              531.0              0.503788          1.0                 \n",
       "4  13.0             1072.0             0.415646          1.0                 \n",
       "\n",
       "    n_non_stop_unique_tokens   num_hrefs   num_self_hrefs   num_imgs  \\\n",
       "0  0.815385                   4.0         2.0              1.0         \n",
       "1  0.791946                   3.0         1.0              1.0         \n",
       "2  0.663866                   3.0         1.0              1.0         \n",
       "3  0.665635                   9.0         0.0              1.0         \n",
       "4  0.540890                   19.0        19.0             20.0        \n",
       "\n",
       "    num_videos   average_token_length   num_keywords  \\\n",
       "0  0.0          4.680365               5.0             \n",
       "1  0.0          4.913725               4.0             \n",
       "2  0.0          4.393365               6.0             \n",
       "3  0.0          4.404896               7.0             \n",
       "4  0.0          4.682836               7.0             \n",
       "\n",
       "    data_channel_is_lifestyle   data_channel_is_entertainment  \\\n",
       "0  0.0                         1.0                              \n",
       "1  0.0                         0.0                              \n",
       "2  0.0                         0.0                              \n",
       "3  0.0                         1.0                              \n",
       "4  0.0                         0.0                              \n",
       "\n",
       "    data_channel_is_bus   data_channel_is_socmed   data_channel_is_tech  \\\n",
       "0  0.0                   0.0                      0.0                     \n",
       "1  1.0                   0.0                      0.0                     \n",
       "2  1.0                   0.0                      0.0                     \n",
       "3  0.0                   0.0                      0.0                     \n",
       "4  0.0                   0.0                      1.0                     \n",
       "\n",
       "    data_channel_is_world   kw_min_min   kw_max_min   kw_avg_min   kw_min_max  \\\n",
       "0  0.0                     0.0          0.0          0.0          0.0           \n",
       "1  0.0                     0.0          0.0          0.0          0.0           \n",
       "2  0.0                     0.0          0.0          0.0          0.0           \n",
       "3  0.0                     0.0          0.0          0.0          0.0           \n",
       "4  0.0                     0.0          0.0          0.0          0.0           \n",
       "\n",
       "    kw_max_max   kw_avg_max   kw_min_avg   kw_max_avg   kw_avg_avg  \\\n",
       "0  0.0          0.0          0.0          0.0          0.0           \n",
       "1  0.0          0.0          0.0          0.0          0.0           \n",
       "2  0.0          0.0          0.0          0.0          0.0           \n",
       "3  0.0          0.0          0.0          0.0          0.0           \n",
       "4  0.0          0.0          0.0          0.0          0.0           \n",
       "\n",
       "    self_reference_min_shares   self_reference_max_shares  \\\n",
       "0  496.0                       496.0                        \n",
       "1  0.0                         0.0                          \n",
       "2  918.0                       918.0                        \n",
       "3  0.0                         0.0                          \n",
       "4  545.0                       16000.0                      \n",
       "\n",
       "    self_reference_avg_sharess   weekday_is_monday   weekday_is_tuesday  \\\n",
       "0  496.000000                   1.0                 0.0                   \n",
       "1  0.000000                     1.0                 0.0                   \n",
       "2  918.000000                   1.0                 0.0                   \n",
       "3  0.000000                     1.0                 0.0                   \n",
       "4  3151.157895                  1.0                 0.0                   \n",
       "\n",
       "    weekday_is_wednesday   weekday_is_thursday   weekday_is_friday  \\\n",
       "0  0.0                    0.0                   0.0                  \n",
       "1  0.0                    0.0                   0.0                  \n",
       "2  0.0                    0.0                   0.0                  \n",
       "3  0.0                    0.0                   0.0                  \n",
       "4  0.0                    0.0                   0.0                  \n",
       "\n",
       "    weekday_is_saturday   weekday_is_sunday   is_weekend    LDA_00    LDA_01  \\\n",
       "0  0.0                   0.0                 0.0          0.500331  0.378279   \n",
       "1  0.0                   0.0                 0.0          0.799756  0.050047   \n",
       "2  0.0                   0.0                 0.0          0.217792  0.033334   \n",
       "3  0.0                   0.0                 0.0          0.028573  0.419300   \n",
       "4  0.0                   0.0                 0.0          0.028633  0.028794   \n",
       "\n",
       "     LDA_02    LDA_03    LDA_04   global_subjectivity  \\\n",
       "0  0.040005  0.041263  0.040123  0.521617               \n",
       "1  0.050096  0.050101  0.050001  0.341246               \n",
       "2  0.033351  0.033334  0.682188  0.702222               \n",
       "3  0.494651  0.028905  0.028572  0.429850               \n",
       "4  0.028575  0.028572  0.885427  0.513502               \n",
       "\n",
       "    global_sentiment_polarity   global_rate_positive_words  \\\n",
       "0  0.092562                    0.045662                      \n",
       "1  0.148948                    0.043137                      \n",
       "2  0.323333                    0.056872                      \n",
       "3  0.100705                    0.041431                      \n",
       "4  0.281003                    0.074627                      \n",
       "\n",
       "    global_rate_negative_words   rate_positive_words   rate_negative_words  \\\n",
       "0  0.013699                     0.769231              0.230769               \n",
       "1  0.015686                     0.733333              0.266667               \n",
       "2  0.009479                     0.857143              0.142857               \n",
       "3  0.020716                     0.666667              0.333333               \n",
       "4  0.012127                     0.860215              0.139785               \n",
       "\n",
       "    avg_positive_polarity   min_positive_polarity   max_positive_polarity  \\\n",
       "0  0.378636                0.100000                0.7                      \n",
       "1  0.286915                0.033333                0.7                      \n",
       "2  0.495833                0.100000                1.0                      \n",
       "3  0.385965                0.136364                0.8                      \n",
       "4  0.411127                0.033333                1.0                      \n",
       "\n",
       "    avg_negative_polarity   min_negative_polarity   max_negative_polarity  \\\n",
       "0 -0.350000               -0.600                  -0.200000                 \n",
       "1 -0.118750               -0.125                  -0.100000                 \n",
       "2 -0.466667               -0.800                  -0.133333                 \n",
       "3 -0.369697               -0.600                  -0.166667                 \n",
       "4 -0.220192               -0.500                  -0.050000                 \n",
       "\n",
       "    title_subjectivity   title_sentiment_polarity   abs_title_subjectivity  \\\n",
       "0  0.500000            -0.187500                   0.000000                  \n",
       "1  0.000000             0.000000                   0.500000                  \n",
       "2  0.000000             0.000000                   0.500000                  \n",
       "3  0.000000             0.000000                   0.500000                  \n",
       "4  0.454545             0.136364                   0.045455                  \n",
       "\n",
       "    abs_title_sentiment_polarity   shares  \n",
       "0  0.187500                       593      \n",
       "1  0.000000                       711      \n",
       "2  0.000000                       1500     \n",
       "3  0.000000                       1200     \n",
       "4  0.136364                       505      "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "url                               http://mashable.com/2013/01/07/amazon-instant-video-browser/\n",
       " timedelta                        731                                                         \n",
       " n_tokens_title                   12                                                          \n",
       " n_tokens_content                 219                                                         \n",
       " n_unique_tokens                  0.663594                                                    \n",
       " n_non_stop_words                 1                                                           \n",
       " n_non_stop_unique_tokens         0.815385                                                    \n",
       " num_hrefs                        4                                                           \n",
       " num_self_hrefs                   2                                                           \n",
       " num_imgs                         1                                                           \n",
       " num_videos                       0                                                           \n",
       " average_token_length             4.68037                                                     \n",
       " num_keywords                     5                                                           \n",
       " data_channel_is_lifestyle        0                                                           \n",
       " data_channel_is_entertainment    1                                                           \n",
       " data_channel_is_bus              0                                                           \n",
       " data_channel_is_socmed           0                                                           \n",
       " data_channel_is_tech             0                                                           \n",
       " data_channel_is_world            0                                                           \n",
       " kw_min_min                       0                                                           \n",
       " kw_max_min                       0                                                           \n",
       " kw_avg_min                       0                                                           \n",
       " kw_min_max                       0                                                           \n",
       " kw_max_max                       0                                                           \n",
       " kw_avg_max                       0                                                           \n",
       " kw_min_avg                       0                                                           \n",
       " kw_max_avg                       0                                                           \n",
       " kw_avg_avg                       0                                                           \n",
       " self_reference_min_shares        496                                                         \n",
       " self_reference_max_shares        496                                                         \n",
       "                                 ...                                                          \n",
       " weekday_is_monday                1                                                           \n",
       " weekday_is_tuesday               0                                                           \n",
       " weekday_is_wednesday             0                                                           \n",
       " weekday_is_thursday              0                                                           \n",
       " weekday_is_friday                0                                                           \n",
       " weekday_is_saturday              0                                                           \n",
       " weekday_is_sunday                0                                                           \n",
       " is_weekend                       0                                                           \n",
       " LDA_00                           0.500331                                                    \n",
       " LDA_01                           0.378279                                                    \n",
       " LDA_02                           0.0400047                                                   \n",
       " LDA_03                           0.0412626                                                   \n",
       " LDA_04                           0.0401225                                                   \n",
       " global_subjectivity              0.521617                                                    \n",
       " global_sentiment_polarity        0.092562                                                    \n",
       " global_rate_positive_words       0.0456621                                                   \n",
       " global_rate_negative_words       0.0136986                                                   \n",
       " rate_positive_words              0.769231                                                    \n",
       " rate_negative_words              0.230769                                                    \n",
       " avg_positive_polarity            0.378636                                                    \n",
       " min_positive_polarity            0.1                                                         \n",
       " max_positive_polarity            0.7                                                         \n",
       " avg_negative_polarity           -0.35                                                        \n",
       " min_negative_polarity           -0.6                                                         \n",
       " max_negative_polarity           -0.2                                                         \n",
       " title_subjectivity               0.5                                                         \n",
       " title_sentiment_polarity        -0.1875                                                      \n",
       " abs_title_subjectivity           0                                                           \n",
       " abs_title_sentiment_polarity     0.1875                                                      \n",
       " shares                           593                                                         \n",
       "Name: 0, Length: 61, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Web scrapping using Beautiful Soup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bs4 import BeautifulSoup\n",
    "import requests\n",
    "url= 'http://mashable.com/2013/01/07/amazon-instant-video-browser/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response=requests.get(url)\n",
    "\n",
    "response.status_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "soup=BeautifulSoup(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "response.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding tokens in title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=soup.find('h1',{'class':'title'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "title=a.text[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Amazon',\n",
       " \"'s\",\n",
       " 'Streaming',\n",
       " 'Video',\n",
       " 'Library',\n",
       " 'Now',\n",
       " 'a',\n",
       " 'Little',\n",
       " 'Easier',\n",
       " 'to',\n",
       " 'Navigate']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_tokenize(title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_tokens_title 11\n"
     ]
    }
   ],
   "source": [
    "n_tokens_title=len(word_tokenize(title))\n",
    "print(f'n_tokens_title {n_tokens_title}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding words in content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=soup.find_all('p')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "links=[]\n",
    "content=[]\n",
    "for i in b:\n",
    "    content.append(i.text[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_tokens_content 204\n"
     ]
    }
   ],
   "source": [
    "n_tokens_content=len(word_tokenize(cleaned_content))\n",
    "print(f'n_tokens_content {n_tokens_content}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Text\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string\n",
    "import re\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "def clean_text(text):\n",
    "    # lower text\n",
    "    text = text.lower()\n",
    "\n",
    "    # remove puncutation\n",
    "    text = text.translate(str.maketrans('', '', string.punctuation))\n",
    "\n",
    "    # tokenize_text\n",
    "    text = [word for word in word_tokenize(text)]\n",
    "\n",
    "    # remove words that contain numbers\n",
    "    text = [word for word in text if not any(c.isdigit() for c in word)]\n",
    "    \n",
    "    # remove empty tokens\n",
    "    text = [t for t in text if len(t) > 0]\n",
    "\n",
    "    # remove words with only one letter\n",
    "    text = [t for t in text if len(t) > 1]\n",
    "\n",
    "    # join all\n",
    "    text = \" \".join(text)\n",
    "    return(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_content=clean_text(str(content))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'having trouble finding something to watch on amazon instant video the retailer launched monday an experimental browsing tool that lets users discover movies and tv shows based on their genre preferences or simply the mood theyre in movies and shows are divided up into categories some of which bear the names of genres ie comedy and mysterythriller and others which are labeled by mood such as feelgood and exciting users can toggle between tv shows and movies and apply filters to show only videos that are available for free viewing to prime subscribers or ones that bear or pg ratings its pretty basic but it sure beats the haphazard organization of amazons current instant video page which mixes rows of new releases with bestsellers and personal recommendations the move is the latest in series of investments amazon is making in its streaming video platform last week the company inked licensing agreement with ae networks to bring past seasons of shows from ae history lifetime and bio to its streaming library shows it should be noted that netflix subscribers lost access to in the fall amazon claims its streaming prime catalog now numbers more than double what it was year ago screenshot courtesy of amazoncom'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_content"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rate of unique words in the content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "l1=[i for i in cleaned_content.split()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "204"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "s={i for i in cleaned_content.split()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_unique_tokens=len(s)/len(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6764705882352942"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_unique_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Rate of non stop words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "non_stop=[j for j in cleaned_content.split() if j not in stopwords.words() ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "122"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(non_stop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "204"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cleaned_content.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_non_stop_words=122/204"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5980392156862745"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_non_stop_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rate of unique non-stop words in the content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "u={i for i in cleaned_content.split() if i not in stopwords.words()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_non_stop_unique_tokens=len(u)/len(non_stop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8360655737704918"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_non_stop_unique_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "c=soup.find_all(class_=\"article-content\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "links=[]\n",
    "for i in c:\n",
    "    d=i.find_all('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "for j in d:\n",
    "    links.append(j['href'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['https://mashable.com/category/amazon/',\n",
       " 'http://www.amazon.com/gp/videofinder/ref=aiv_vf_p1_fil_tv?ie=UTF8&mv=0&tv=0',\n",
       " 'http://www.amazon.com/Instant-Video/b/ref=topnav_storetab_mov_aiv?ie=UTF8&node=2858778011',\n",
       " 'https://mashable.com/2013/01/04/amazon-video-ae-history-lifetime/']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "links"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_hrefs=len(links)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_hrefs 4\n"
     ]
    }
   ],
   "source": [
    "print(f'num_hrefs {num_hrefs}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### No of images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "i=soup.find_all('img')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average length of the words in the content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "length=[]\n",
    "for i in cleaned_content.split():\n",
    "    length.append(len(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "average_token_length=np.mean(length)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average_token_length 4.980392156862745\n"
     ]
    }
   ],
   "source": [
    "print(f'average_token_length {average_token_length}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of keywords in the metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "keywords=soup.find_all('footer',{'class':\"article-topics\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[]\n",
    "for i in keywords:\n",
    "    a=i.find_all('a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys=[]\n",
    "for i in a:\n",
    "    keys.append(i.text[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['amazon', 'Entertainment', 'Media', 'Video']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_keywords=len(keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_keywords 4\n"
     ]
    }
   ],
   "source": [
    "print(f'num_keywords {num_keywords}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "channel=['Lifestyle','Entertainment','Business','Social Media','Tech']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "channel=[i.lower() for i in channel]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys=[i.lower() for i in keys]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in channel:\n",
    "    if i in keys:\n",
    "        data_channel=i\n",
    "        break\n",
    "    else:\n",
    "        data_channel='Others'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'entertainment'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data_channel entertainment\n"
     ]
    }
   ],
   "source": [
    "print(f'data_channel {data_channel}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding weekday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "day=soup.find('time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "day=day['datetime'][:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(day)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "day=day.replace('Mon','Monday')\n",
    "#,'Tue':'Tuesday','Wed':'Wednesday','Thu':'Thursday','Fri':'Friday','Sat':'Saturday','Sun':'Sunday'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "day is Monday\n"
     ]
    }
   ],
   "source": [
    "print(f'day is {day}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## global_subjectivity & global_sentiment_polarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from textblob import TextBlob as tb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentiment(polarity=0.09256198347107437, subjectivity=0.5216171454807819)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb(cleaned_content).sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "global_subjectivity=tb(cleaned_content).sentiment[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "global_sentiment_polarity=tb(cleaned_content).sentiment[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## global_rate_positive_words & global_rate_negative_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## title_subjectivity & title_sentiment_polarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sentiment(polarity=-0.1875, subjectivity=0.5)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tb(title).sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_subjectivity=tb(title).sentiment[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "title_sentiment_polarity=tb(title).sentiment[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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