EDA_01.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "02C_Auto_Text_Feature_Creation.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPsQy0ri86x02ad9GNdkCZp",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/The-Gupta/TED-Talks-Analysis/blob/main/EDA_01.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IUEBixBL9J3m"
      },
      "source": [
        "# 14-DEC-20  0245."
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C2c7oIfM9RGa",
        "outputId": "ce0357a0-85b0-401a-dd04-1d2bb5c611bc"
      },
      "source": [
        "import requests, bs4, sys, os, time, json, re, random, ast, itertools\r\n",
        "from multiprocessing import Process, Manager\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import urllib.request\r\n",
        "from bs4 import BeautifulSoup\r\n",
        "from datetime import datetime\r\n",
        "from nltk.corpus import words\r\n",
        "import spacy, nltk, re, unicodedata, string\r\n",
        "from textblob import TextBlob\r\n",
        "from collections import Counter\r\n",
        "\r\n",
        "\r\n",
        "nltk.download('all')\r\n",
        "\r\n",
        "ENGLISH_WORDS = set(words.words())\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "'''  IGNORING WARNINGS  '''\r\n",
        "import warnings\r\n",
        "warnings.filterwarnings('ignore')\r\n",
        "\r\n",
        "\r\n",
        "'''  NOTEBOOK DISPLAY SETTINGS  '''\r\n",
        "pd.options.display.max_rows = 100    # None\r\n",
        "pd.options.display.max_columns = 300  # \r\n",
        "\r\n",
        "pd.set_option('display.max_colwidth', 300)  # -1\r\n",
        "pd.set_option('display.expand_frame_repr', False)\r\n"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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      "metadata": {
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        ""
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          "base_uri": "https://localhost:8080/"
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      },
      "source": [
        "# Using Google Drive for storage.\r\n",
        "from google.colab import drive\r\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0BHVvpdg9RPd",
        "outputId": "fd4d081d-4794-4698-9797-3fdb6a32767d"
      },
      "source": [
        "cd \"/content/gdrive/My Drive/Vishal/PersonalProject/TED/7_EDA_1/\""
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/Vishal/PersonalProject/TED/7_EDA_1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BJ-SHkSb9RSH"
      },
      "source": [
        ""
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g30ffR1e9RVG",
        "outputId": "78aa58ef-a5e8-48b3-fc8c-53d540d3e603"
      },
      "source": [
        "# Starting to explore with transcript at the core.\r\n",
        "df  =  pd.read_csv('TED_Talk.csv')\r\n",
        "ORIGINAL_COLUMNS  =  df.columns.tolist()\r\n",
        "\r\n",
        "print(df.shape)\r\n",
        "df  =  df[~df['transcript'].isna()]    # Remove talks w/o transcripts.\r\n",
        "print(df.shape)\r\n",
        "df  =  df[df['language']=='en']    # Removing 3 talks that are not in English.\r\n",
        "print(df.shape)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(4883, 52)\n",
            "(4315, 52)\n",
            "(4312, 52)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YzBHEWgj9RYB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "outputId": "2b76181c-5254-46cb-dc05-48b63c6c84e6"
      },
      "source": [
        "df.sample(1)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>2992</th>\n",
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              "      <td>A plane you can drive</td>\n",
              "      <td>A flying car -- it's an iconic image of the future. But after 100 years of flight and automotive engineering, no one has really cracked the problem. Pilot Anna Mracek Dietrich and her team flipped the question, asking: Why not build a plane that you can drive?</td>\n",
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              "      <td>What is it about flying cars? We've wanted to do this for about a hundred years. And there are historic attempts that have had some level of technical success. But we haven't yet gotten to the point where on your way here this morning you see something that really, truly seamlessly integrates th...</td>\n",
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              "      <td>Anna Mracek Dietrich</td>\n",
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              "      <td>Anna Mracek Dietrich is one of the creators of the Transition, the \"plane you can drive.\"</td>\n",
              "      <td>Anna Mracek Dietrich is a private pilot trying to making aviation accessible, safe -- and a reasonable way to get to work. With husband Carl and their team at Terrafugia, in Massachusetts, they are building a driveable aircraft (or, as they also term it, the Roadable Aircraft), which is designed...</td>\n",
              "      <td>The first flight was remarkably unremarkable. I've flown several thousand hours in everything from Piper Cubs to F-16s, and the Transition flew like a really nice airplane.</td>\n",
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              "      <td>[{'id': '1034', 'slug': 'anna_mracek_dietrichs', 'is_published': True, 'firstname': 'Anna Mracek', 'lastname': 'Dietrich', 'middleinitial': '', 'title': '', 'description': 'Inventor', 'photo_url': 'https://pe.tedcdn.com/images/ted/b7f192c4110ec3e48cafae91aec9f41161afc131_254x191.jpg', 'whatother...</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>2011-07-15</td>\n",
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              "      <td>['cars', 'design', 'flight', 'invention', 'transportation']</td>\n",
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              "      <td>en</td>\n",
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              "      <td>False</td>\n",
              "      <td>https://www.ted.com/talks/anna_mracek_dietrich_a_plane_you_can_drive</td>\n",
              "      <td>https://download.ted.com/talks/AnnaMracekDietrich_2011G.mp3?apikey=acme-roadrunner</td>\n",
              "      <td>{'low': 'https://download.ted.com/talks/AnnaMracekDietrich_2011G-light.mp4?apikey=acme-roadrunner', 'medium': 'https://download.ted.com/talks/AnnaMracekDietrich_2011G.mp4?apikey=acme-roadrunner', 'high': 'https://download.ted.com/talks/AnnaMracekDietrich_2011G-480p.mp4?apikey=acme-roadrunner'}</td>\n",
              "      <td>https://s3.amazonaws.com/talkstar-photos/uploads/1f7546a1-12f0-4822-97e9-087516324333/AnnaMracekDietrich_2011G-embed.jpg</td>\n",
              "      <td>https://pe.tedcdn.com/images/ted/b7f192c4110ec3e48cafae91aec9f41161afc131_254x191.jpg</td>\n",
              "      <td>{'ja': {'name': 'Japanese', 'high': 'https://download.ted.com/talks/AnnaMracekDietrich_2011G-480p-ja.mp4?apikey=acme-roadrunner', 'low': 'https://download.ted.com/talks/AnnaMracekDietrich_2011G-low-ja.mp4?apikey=acme-roadrunner'}, 'zh-tw': {'name': 'Chinese, Traditional', 'high': 'https://downlo...</td>\n",
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            "text/plain": [
              "      talk__id             talk__name                                                                                                                                                                                                                                                     talk__description  view_count  comment_count  duration                                                                                                                                                                                                                                                                                                   transcript video_type_name           event  number_of__speakers  speaker__id         speaker__name speaker__description                                                                         speaker__who_he_is                                                                                                                                                                                                                                                                                          speaker__why_listen                                                                                                                                                      speaker__what_others_say speaker__is_published                                                                                                                                                                                                                                                                                         all_speakers_details  is_talk_featured has_talk_citation recording_date  published_timestamp                                                  talks__tags  number_of__tags language native_language  language_swap  is_subtitle_required                                                          url__webpage                                                                          url__audio                                                                                                                                                                                                                                                                                              url__video                                                                                                          url__photo__talk                                                                    url__photo__speaker                                                                                                                                                                                                                                                                                        url__subtitled_videos  number_of__subtitled_videos                                                                                                                                                                                                                                                                                     talk__download_languages  number_of__talk__download_languages talk__more_resources  number_of__talk__more_resources talk__recommendations__blurb talk__recommendations  number_of__talk__recommendations                                                                                                                                                                                                                                                                                                related_talks  number_of__related_talks  intro_duration  ad_duration  post_ad_duration  external__duration  external__start_time  talks__player_talks__resources__h264__00__bitrate talks__take_action  number_of__talks__take_actions\n",
              "2992      1260  A plane you can drive  A flying car -- it's an iconic image of the future. But after 100 years of flight and automotive engineering, no one has really cracked the problem. Pilot Anna Mracek Dietrich and her team flipped the question, asking: Why not build a plane that you can drive?     1012447            NaN       578  What is it about flying cars? We've wanted to do this for about a hundred years. And there are historic attempts that have had some level of technical success. But we haven't yet gotten to the point where on your way here this morning you see something that really, truly seamlessly integrates th...  TED Stage Talk  TEDGlobal 2011                    1       1034.0  Anna Mracek Dietrich             Inventor  Anna Mracek Dietrich is one of the creators of the Transition, the \"plane you can drive.\"  Anna Mracek Dietrich is a private pilot trying to making aviation accessible, safe -- and a reasonable way to get to work. With husband Carl and their team at Terrafugia, in Massachusetts, they are building a driveable aircraft (or, as they also term it, the Roadable Aircraft), which is designed...  The first flight was remarkably unremarkable. I've flown several thousand hours in everything from Piper Cubs to F-16s, and the Transition flew like a really nice airplane.                  True  [{'id': '1034', 'slug': 'anna_mracek_dietrichs', 'is_published': True, 'firstname': 'Anna Mracek', 'lastname': 'Dietrich', 'middleinitial': '', 'title': '', 'description': 'Inventor', 'photo_url': 'https://pe.tedcdn.com/images/ted/b7f192c4110ec3e48cafae91aec9f41161afc131_254x191.jpg', 'whatother...              True               NaN     2011-07-15  2011-11-02 15:22:05  ['cars', 'design', 'flight', 'invention', 'transportation']                5       en              en          False                 False  https://www.ted.com/talks/anna_mracek_dietrich_a_plane_you_can_drive  https://download.ted.com/talks/AnnaMracekDietrich_2011G.mp3?apikey=acme-roadrunner  {'low': 'https://download.ted.com/talks/AnnaMracekDietrich_2011G-light.mp4?apikey=acme-roadrunner', 'medium': 'https://download.ted.com/talks/AnnaMracekDietrich_2011G.mp4?apikey=acme-roadrunner', 'high': 'https://download.ted.com/talks/AnnaMracekDietrich_2011G-480p.mp4?apikey=acme-roadrunner'}  https://s3.amazonaws.com/talkstar-photos/uploads/1f7546a1-12f0-4822-97e9-087516324333/AnnaMracekDietrich_2011G-embed.jpg  https://pe.tedcdn.com/images/ted/b7f192c4110ec3e48cafae91aec9f41161afc131_254x191.jpg  {'ja': {'name': 'Japanese', 'high': 'https://download.ted.com/talks/AnnaMracekDietrich_2011G-480p-ja.mp4?apikey=acme-roadrunner', 'low': 'https://download.ted.com/talks/AnnaMracekDietrich_2011G-low-ja.mp4?apikey=acme-roadrunner'}, 'zh-tw': {'name': 'Chinese, Traditional', 'high': 'https://downlo...                           31  [{'languageName': 'Greek', 'endonym': 'Ελληνικά', 'languageCode': 'el', 'ianaCode': 'el', 'isRtl': False}, {'languageName': 'English', 'endonym': 'English', 'languageCode': 'en', 'ianaCode': 'en', 'isRtl': False}, {'languageName': 'Vietnamese', 'endonym': 'Tiếng Việt', 'languageCode': 'vi', 'ian...                                   31                  NaN                                0                          NaN                   NaN                                 0  [{'id': '5', 'hero': 'https://s3.amazonaws.com/talkstar-photos/uploads/d4721fba-341c-4a94-98d7-5ccc36074768/ChrisBangle_2002-embed.jpg', 'speaker': 'Chris Bangle', 'title': 'Great cars are great art', 'duration': 1204, 'slug': 'chris_bangle_great_cars_are_great_art', 'viewed_count': 993166}, {'i...                         6           11.82         3.33              0.83               579.0                   0.0                                              180.0                NaN                               0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t7g2v_vpFvlu"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s7VZV0MmO-ow"
      },
      "source": [
        "# Functions, Global Variables"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aTaCn80CQj27"
      },
      "source": [
        "# Stopwords and Bad words.\r\n",
        "STOP_WORDS = nltk.corpus.stopwords.words('english')\r\n",
        "\r\n",
        "with open('most_common_english_words_1000.txt') as f:    \r\n",
        "    COMMON_WORDS__TOP_1000_1 = [line.rstrip() for line in f]    # https://gist.github.com/deekayen/4148741\r\n",
        "with open('most_common_english_words_1000__ef_com.txt') as f:    \r\n",
        "    COMMON_WORDS__TOP_1000_2 = [line.rstrip() for line in f]    # https://www.ef.com/wwen/english-resources/english-vocabulary/top-1000-words/\r\n",
        "with open('most_common_english_words_3000__ef_com.txt') as f:    \r\n",
        "    COMMON_WORDS__TOP_3000   = [line.rstrip() for line in f]    # https://www.ef.com/wwen/english-resources/english-vocabulary/top-3000-words/\r\n",
        "\r\n",
        "with open('bad_words.txt') as f:    \r\n",
        "    BAD_WORDS = [line.rstrip() for line in f]    # https://www.kaggle.com/shaz13/feature-engineering-for-nlp-classification\r\n",
        "\r\n",
        "PUNCTUATION = string.punctuation    # !\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "# Source:  https://github.com/dipanjanS/text-analytics-with-python/blob/master/Old-First-Edition/Ch04_Text_Classification/normalization.py#L22\r\n",
        "from contractions import CONTRACTION_MAP\r\n",
        "def expand_contractions(text):    # Expand Short Forms. Ex: I'm -> I am.\r\n",
        "    if text:\r\n",
        "        global CONTRACTION_MAP\r\n",
        "        \r\n",
        "        text  =  re.sub(' +',   ' ',   text.replace('(', ' (').replace(')', ') ').replace('!', '! ').replace('?', '? '))    # Fixing spacing issues in the Transcript.\r\n",
        "\r\n",
        "        contraction_mapping=CONTRACTION_MAP\r\n",
        "\r\n",
        "        contractions_pattern = re.compile('({})'.format('|'.join(contraction_mapping.keys())), \r\n",
        "                                        flags=re.IGNORECASE|re.DOTALL)\r\n",
        "        def expand_match(contraction):\r\n",
        "            match = contraction.group(0)\r\n",
        "            first_char = match[0]\r\n",
        "            expanded_contraction = contraction_mapping.get(match)\\\r\n",
        "                                    if contraction_mapping.get(match)\\\r\n",
        "                                    else contraction_mapping.get(match.lower())                       \r\n",
        "            expanded_contraction = first_char+expanded_contraction[1:]\r\n",
        "            return expanded_contraction\r\n",
        "            \r\n",
        "        expanded_text = contractions_pattern.sub(expand_match, text)\r\n",
        "        expanded_text = re.sub(\"'\", \"\", expanded_text)\r\n",
        "        return expanded_text\r\n",
        "    else:    return ''\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "# Lemmatization\r\n",
        "nlp = spacy.load('en', parse=True, tag=True, entity=True)\r\n",
        "def lemmatize_text(text):\r\n",
        "    if text:\r\n",
        "        text = nlp(text)\r\n",
        "        return ' '.join([word.lemma_   if word.lemma_ != '-PRON-' else word.text   for word in text])\r\n",
        "    else:    return ''\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "def remove_accented_chars(text):    \r\n",
        "    # Convert Non-ASCII Characters to equivalent ASCII Characters.\r\n",
        "    # Ex:  'Sómě Áccěntěd těxt' -> 'Some Accented text'\r\n",
        "    # Source:  https://github.com/dipanjanS/nlp_essentials/blob/master/notebooks/01_Text_Wrangling_Examples.ipynb\r\n",
        "    text = unicodedata.normalize('NFKD', text).encode('ascii', 'ignore').decode('utf-8', 'ignore')\r\n",
        "    return text\r\n",
        "\r\n",
        "\r\n",
        "# Spelling Correction\r\n",
        "# Source:  https://www.kaggle.com/sudalairajkumar/getting-started-with-text-preprocessing\r\n",
        "try:\r\n",
        "    from spellchecker import SpellChecker\r\n",
        "except:\r\n",
        "    !pip install pyspellchecker\r\n",
        "    from spellchecker import SpellChecker\r\n",
        "\r\n",
        "spell = SpellChecker()\r\n",
        "def correct_spellings(text):\r\n",
        "    corrected_text = []\r\n",
        "    misspelled_words = spell.unknown(text.split())\r\n",
        "    for word in text.split():\r\n",
        "        if word in misspelled_words:\r\n",
        "            corrected_text.append(spell.correction(word))\r\n",
        "        else:\r\n",
        "            corrected_text.append(word)\r\n",
        "    return \" \".join(corrected_text)\r\n",
        "\r\n",
        "\r\n",
        "# Text Normalization\r\n",
        "def normalize_text(text):\r\n",
        "    if text:\r\n",
        "        # text = remove_accented_chars(text)\r\n",
        "        # text = correct_spellings(text)\r\n",
        "        text = re.sub(r'[^a-zA-Z0-9\\s]', ' ', text)\r\n",
        "        text = re.sub(r'\\s+', ' ', text)\r\n",
        "        text = text.lower().strip()\r\n",
        "\r\n",
        "        tokens = nltk.word_tokenize(text)\r\n",
        "        filtered_tokens  =  [token   for token in tokens   if token not in STOP_WORDS]\r\n",
        "        # filtered_tokens  =  [token   for token in tokens   if token not in CUSTOM_STOP_WORDS]\r\n",
        "        # filtered_tokens  =  [token   for token in tokens   if token not in BAD_WORDS]\r\n",
        "        text = ' '.join(filtered_tokens)\r\n",
        "\r\n",
        "        return text\r\n",
        "    else:    return ''\r\n",
        "\r\n",
        "\r\n",
        "def normalize_text2(text):\r\n",
        "    # Keeping only mainstream english words excluding 1000 most common words.\r\n",
        "    if text:\r\n",
        "        tokens = nltk.word_tokenize(text)\r\n",
        "        filtered_tokens  =  [token   for token in tokens   if ((token in ENGLISH_WORDS) and (token not in COMMON_WORDS__TOP_1000_2))]\r\n",
        "        text = ' '.join(filtered_tokens)\r\n",
        "        return text\r\n",
        "    else:    return ''\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "# Sentiment with TextBlob.\r\n",
        "def get_polarity(text):\r\n",
        "    # Polarity:  Range: [-1, 1]  --  negative to positive\r\n",
        "    return TextBlob(text).sentiment.polarity\r\n",
        "\r\n",
        "def get_subjectivity(text):\r\n",
        "    # Subjectivity:  Range: [0, 1]  --  objective to subjective\r\n",
        "    return TextBlob(text).sentiment.subjectivity\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "# POS Tag Count.\r\n",
        "pos_dic  =  {    'noun' : ['NN', 'NNS', 'NNP', 'NNPS'],\r\n",
        "                 'pron' : ['PRP', 'PRP$', 'WP', 'WP$'],\r\n",
        "                 'verb' : ['VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ'],\r\n",
        "                 'adj' :  ['JJ', 'JJR', 'JJS'],\r\n",
        "                 'adv' : ['RB', 'RBR', 'RBS', 'WRB']\r\n",
        "            }\r\n",
        "\r\n",
        "# function to check and get the part of speech tag count of a words in a given sentence\r\n",
        "def pos_check(x, flag):\r\n",
        "    cnt = 0\r\n",
        "    \r\n",
        "    try:\r\n",
        "        wiki = TextBlob(x)\r\n",
        "        for tup in wiki.tags:\r\n",
        "            ppo = list(tup)[1]\r\n",
        "            if ppo in pos_dic[flag]:\r\n",
        "                cnt += 1\r\n",
        "    except:\r\n",
        "        pass\r\n",
        "    \r\n",
        "    return cnt"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zC4Nx04FliqU"
      },
      "source": [
        "# Creating some more columns with Text using basic NLP"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R_bg_MVMQj6y",
        "outputId": "163a863c-e42c-4632-adca-5c64e8769b54"
      },
      "source": [
        "%%time\r\n",
        "\r\n",
        "columns  =  ['talk__name', 'talk__description',  'speaker__who_he_is', 'speaker__why_listen', 'speaker__what_others_say']\r\n",
        "\r\n",
        "for  col  in columns:\r\n",
        "    print(col)\r\n",
        "    df[col + '__2']   =   df[col].fillna('').apply(expand_contractions)\r\n",
        "    df[col + '__3']   =   df[col + '__2'].fillna('').apply(lemmatize_text)\r\n",
        "    df[col + '__4']   =   df[col + '__3'].fillna('').apply(normalize_text)\r\n",
        "\r\n",
        "\r\n",
        "    df[col + '__sentiment']       =   df[col + '__2'].fillna('').apply(get_polarity)\r\n",
        "    df[col + '__subjectivity']   =   df[col + '__2'].fillna('').apply(get_subjectivity)\r\n",
        "\r\n",
        "\r\n",
        "    df[col + '__char_count']     =   df[col].fillna('').apply(len)\r\n",
        "    df[col + '__word_count']     =   df[col].fillna('').apply(lambda x:  len(x.split()))\r\n",
        "    df[col + '__word_density']   =   df[col + '__char_count'] / (df[col + '__word_count']+1)\r\n",
        "\r\n",
        "    df[col + '__punctuation_count']       =  df[col].fillna('').apply(lambda x:  len(\"\".join(_ for _ in x if _ in PUNCTUATION)))\r\n",
        "    df[col + '__title_word_count']        =  df[col].fillna('').apply(lambda x:  len([wrd  for wrd in x.split()  if wrd.istitle()]))\r\n",
        "    df[col + '__upper_case_word_count']   =  df[col].fillna('').apply(lambda x:  len([wrd  for wrd in x.split()  if wrd.isupper()]))\r\n",
        "    df[col + '__stopword_count']          =  df[col].fillna('').apply(lambda x:  len([wrd  for wrd in x.split()  if wrd.lower() in STOP_WORDS]))\r\n",
        "\r\n",
        "\r\n",
        "    df[col + '__noun_count']  =  df[col].fillna('').apply(lambda x: pos_check(x, 'noun'))\r\n",
        "    df[col + '__verb_count']  =  df[col].fillna('').apply(lambda x: pos_check(x, 'verb'))\r\n",
        "    df[col + '__adj_count']   =  df[col].fillna('').apply(lambda x: pos_check(x, 'adj'))\r\n",
        "    df[col + '__adv_count']   =  df[col].fillna('').apply(lambda x: pos_check(x, 'adv'))\r\n",
        "    df[col + '__pron_count']  =  df[col].fillna('').apply(lambda x: pos_check(x, 'pron'))\r\n",
        "\r\n",
        "\r\n",
        "    # Normalized columns\r\n",
        "    df[col + '__punctuation_count__norm']       =  df[col + '__punctuation_count']      /  (df[col + '__word_count']+1)\r\n",
        "    df[col + '__title_word_count__norm']        =  df[col + '__title_word_count']       /  (df[col + '__word_count']+1)\r\n",
        "    df[col + '__upper_case_word_count__norm']   =  df[col + '__upper_case_word_count']  /  (df[col + '__word_count']+1)\r\n",
        "    df[col + '__stopword_count__norm']          =  df[col + '__stopword_count']         /  (df[col + '__word_count']+1)\r\n",
        "\r\n",
        "    df[col + '__noun_count__norm']   =  df[col + '__noun_count']  /  (df[col + '__word_count']+1)\r\n",
        "    df[col + '__verb_count__norm']   =  df[col + '__verb_count']  /  (df[col + '__word_count']+1)\r\n",
        "    df[col + '__adj_count__norm']    =  df[col + '__adj_count']   /  (df[col + '__word_count']+1)\r\n",
        "    df[col + '__adv_count__norm']    =  df[col + '__adv_count']   /  (df[col + '__word_count']+1)\r\n",
        "    df[col + '__pron_count__norm']   =  df[col + '__pron_count']  /  (df[col + '__word_count']+1)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "talk__name\n",
            "talk__description\n",
            "speaker__who_he_is\n",
            "speaker__why_listen\n",
            "speaker__what_others_say\n",
            "CPU times: user 11min 26s, sys: 3.6 s, total: 11min 30s\n",
            "Wall time: 11min 31s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9nEy92WKzAaT"
      },
      "source": [
        "# Transcript Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MlzW3l0fKxAp",
        "outputId": "42ea9094-9f91-4ed3-c7b4-ce43d262727c"
      },
      "source": [
        "# Requires too much RAM, so creating in chunks of 100 rows.\r\n",
        "\r\n",
        "chunk_size = 100\r\n",
        "for  i  in range((len(df)-1)//chunk_size + 1):\r\n",
        "        start_time  =  time.time()\r\n",
        "\r\n",
        "        df_  =  df.iloc[i*chunk_size:(i+1)*chunk_size]\r\n",
        "\r\n",
        "\r\n",
        "        df_['transcript__2']   =   df_['transcript']   .apply(expand_contractions)\r\n",
        "        df_['transcript__3']   =   df_['transcript__2'].apply(lemmatize_text)\r\n",
        "        df_['transcript__4']   =   df_['transcript__3'].apply(normalize_text)\r\n",
        "        df_['transcript__5']   =   df_['transcript__4'].apply(normalize_text2)\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "        df_['transcript__sentiment']       =   df_['transcript__2'].apply(get_polarity)\r\n",
        "        df_['transcript__subjectivity']    =   df_['transcript__2'].apply(get_subjectivity)\r\n",
        "\r\n",
        "\r\n",
        "        nlp  =  spacy.load('en_core_web_sm')    # NLTK Sentence Tokenizer is worse.\r\n",
        "        df_['transcript__spacy_nlp']     =    df_['transcript__2'].apply(lambda x: nlp(x))\r\n",
        "\r\n",
        "\r\n",
        "        df_['transcript__char_count']         =   df_['transcript__2'].apply(len)\r\n",
        "        df_['transcript__word_count']         =   df_['transcript__2'].apply(lambda x:  len(x.split()))\r\n",
        "        df_['transcript__sentence_count']     =   df_['transcript__spacy_nlp'].apply(lambda x:  len(list(x.sents)))\r\n",
        "\r\n",
        "\r\n",
        "        df_['transcript__word_density']                =   df_['transcript__char_count'] / (df_['transcript__word_count']+1)  # TODO: If min is never 1, I can remove +1s.\r\n",
        "        df_['transcript__sentence_density_wrt_char']   =   df_['transcript__char_count'] / (df_['transcript__sentence_count']+1)\r\n",
        "        df_['transcript__sentence_density_wrt_word']   =   df_['transcript__word_count'] / (df_['transcript__sentence_count']+1)\r\n",
        "\r\n",
        "        df['transcript__char_per_second']         =   df['transcript__char_count']      /  df['duration']\r\n",
        "        df['transcript__word_per_second']         =   df['transcript__word_count']      /  df['duration']\r\n",
        "        df['transcript__sentence_per_second']     =   df['transcript__sentence_count']  /  df['duration']\r\n",
        "\r\n",
        "        df_['transcript__Laughter_count']     =  df_['transcript__2'].apply(lambda x:  x.count('(Laughter)'))\r\n",
        "        df_['transcript__Applause_count']     =  df_['transcript__2'].apply(lambda x:  x.count('(Applause)'))\r\n",
        "\r\n",
        "        df_['transcript__exclamation_mark_count']  =  df_['transcript__2'].apply(lambda x:  x.count('!'))\r\n",
        "        df_['transcript__question_mark_count']     =  df_['transcript__2'].apply(lambda x:  x.count('?'))\r\n",
        "        df_['transcript__punctuation_count']       =  df_['transcript__2'].apply(lambda x:  len(\"\".join(_ for _ in x if _ in PUNCTUATION)))\r\n",
        "\r\n",
        "        df_['transcript__title_word_count']        =  df_['transcript__2'].apply(lambda x:  len([wrd  for wrd in x.split()  if wrd.istitle()]))\r\n",
        "        df_['transcript__upper_case_word_count']   =  df_['transcript__2'].apply(lambda x:  len([wrd  for wrd in x.split()  if wrd.isupper()]))\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "        df_['transcript__stopword_count']          =  df_['transcript__2'].apply(lambda x:  len([wrd  for wrd in x.split()  if wrd.lower() in STOP_WORDS]))\r\n",
        "        df_['transcript__top_1000_count']          =  df_['transcript__4'].apply(lambda x:  len([wrd  for wrd in x.split()  if wrd.lower() in COMMON_WORDS__TOP_1000_2 ]))\r\n",
        "        df_['transcript__top_3000_count']          =  df_['transcript__4'].apply(lambda x:  len([wrd  for wrd in x.split()  if wrd.lower() in COMMON_WORDS__TOP_3000   ]))\r\n",
        "        df_['transcript__rare_word_count']         =  df_['transcript__4'].apply(lambda x:  len([wrd  for wrd in x.split()  if wrd.lower() in ENGLISH_WORDS]))  -  df_['transcript__top_3000_count']\r\n",
        "        # df_['transcript__badword_count']           =  df_['transcript__4'].apply(lambda x:  len([wrd  for wrd in x.split()  if wrd.lower() in BAD_WORDS]))    # Curse words - the list needs to be customized.\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "        # POS\r\n",
        "        df_['transcript__noun_count']  =  df_['transcript__2'].apply(lambda x: pos_check(x, 'noun'))\r\n",
        "        df_['transcript__verb_count']  =  df_['transcript__2'].apply(lambda x: pos_check(x, 'verb'))\r\n",
        "        df_['transcript__adj_count']   =  df_['transcript__2'].apply(lambda x: pos_check(x, 'adj'))\r\n",
        "        df_['transcript__adv_count']   =  df_['transcript__2'].apply(lambda x: pos_check(x, 'adv'))\r\n",
        "        df_['transcript__pron_count']  =  df_['transcript__2'].apply(lambda x: pos_check(x, 'pron'))\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "        # NER**\r\n",
        "        df_['transcript__ner_person__count']        =  df_['transcript__spacy_nlp'].apply(lambda x: len([e.text  for e in x.ents  if e.label_ in ['PERSON']]))\r\n",
        "        df_['transcript__ner_gpe_loc__count']       =  df_['transcript__spacy_nlp'].apply(lambda x: len([e.text  for e in x.ents  if e.label_ in ['GPE', 'LOC']]))\r\n",
        "        df_['transcript__ner_org_product__count']   =  df_['transcript__spacy_nlp'].apply(lambda x: len([e.text  for e in x.ents  if e.label_ in ['ORG', 'FAC', 'PRODUCT']]))\r\n",
        "        df_['transcript__ner_event__count']         =  df_['transcript__spacy_nlp'].apply(lambda x: len([e.text  for e in x.ents  if e.label_ in ['EVENT']]))\r\n",
        "        df_['transcript__ner_work_of_art__count']   =  df_['transcript__spacy_nlp'].apply(lambda x: len([e.text  for e in x.ents  if e.label_ in ['WORK_OF_ART']]))\r\n",
        "        df_['transcript__ner_language__count']      =  df_['transcript__spacy_nlp'].apply(lambda x: len([e.text  for e in x.ents  if e.label_ in ['LANGUAGE']]))\r\n",
        "        df_['transcript__ner_date_time__count']     =  df_['transcript__spacy_nlp'].apply(lambda x: len([e.text  for e in x.ents  if e.label_ in ['DATE', 'TIME']]))\r\n",
        "        df_['transcript__ner_quantity__count']      =  df_['transcript__spacy_nlp'].apply(lambda x: len([e.text  for e in x.ents  if e.label_ in ['ORDINAL', 'PERCENT', 'CARDINAL', 'QUANTITY']]))\r\n",
        "        df_['transcript__ner_money__count']         =  df_['transcript__spacy_nlp'].apply(lambda x: len([e.text  for e in x.ents  if e.label_ in ['MONEY']]))\r\n",
        "\r\n",
        "        del df_['transcript__spacy_nlp']\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "        # Normalized columns with Word Count.\r\n",
        "        for  col    in ['transcript__Laughter_count', 'transcript__Applause_count',\r\n",
        "                        'transcript__exclamation_mark_count', 'transcript__question_mark_count', 'transcript__punctuation_count', \r\n",
        "                        'transcript__title_word_count', 'transcript__upper_case_word_count', \r\n",
        "                        'transcript__stopword_count', 'transcript__top_1000_count', 'transcript__top_3000_count', 'transcript__rare_word_count',\r\n",
        "                        'transcript__noun_count', 'transcript__verb_count', 'transcript__adj_count', 'transcript__adv_count', 'transcript__pron_count',\r\n",
        "                        'transcript__ner_person__count', 'transcript__ner_gpe_loc__count', 'transcript__ner_org_product__count',\r\n",
        "                        'transcript__ner_event__count', 'transcript__ner_work_of_art__count', 'transcript__ner_language__count',\r\n",
        "                        'transcript__ner_date_time__count', 'transcript__ner_quantity__count', 'transcript__ner_money__count']:\r\n",
        "            df_[col + '__norm']   =  df_[col]  /  (df_['transcript__word_count']+1)\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "        df_.to_pickle('temp/' + str(i*chunk_size) + '.pkl')\r\n",
        "\r\n",
        "        print(i*chunk_size, (i+1)*chunk_size,   df_.shape,   round(time.time() - start_time, 2))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 100 (100, 247) 153.92\n",
            "100 200 (100, 247) 141.0\n",
            "200 300 (100, 247) 138.93\n",
            "300 400 (100, 247) 141.06\n",
            "400 500 (100, 247) 128.85\n",
            "500 600 (100, 247) 126.52\n",
            "600 700 (100, 247) 138.39\n",
            "700 800 (100, 247) 125.81\n",
            "800 900 (100, 247) 133.07\n",
            "900 1000 (100, 247) 133.03\n",
            "1000 1100 (100, 247) 130.7\n",
            "1100 1200 (100, 247) 153.47\n",
            "1200 1300 (100, 247) 140.87\n",
            "1300 1400 (100, 247) 140.01\n",
            "1400 1500 (100, 247) 126.06\n",
            "1500 1600 (100, 247) 131.92\n",
            "1600 1700 (100, 247) 136.24\n",
            "1700 1800 (100, 247) 141.67\n",
            "1800 1900 (100, 247) 130.34\n",
            "1900 2000 (100, 247) 133.68\n",
            "2000 2100 (100, 247) 125.23\n",
            "2100 2200 (100, 247) 128.66\n",
            "2200 2300 (100, 247) 130.24\n",
            "2300 2400 (100, 247) 134.32\n",
            "2400 2500 (100, 247) 124.41\n",
            "2500 2600 (100, 247) 120.74\n",
            "2600 2700 (100, 247) 127.12\n",
            "2700 2800 (100, 247) 137.4\n",
            "2800 2900 (100, 247) 131.55\n",
            "2900 3000 (100, 247) 115.99\n",
            "3000 3100 (100, 247) 126.34\n",
            "3100 3200 (100, 247) 126.34\n",
            "3200 3300 (100, 247) 135.09\n",
            "3300 3400 (100, 247) 141.97\n",
            "3400 3500 (100, 247) 128.52\n",
            "3500 3600 (100, 247) 120.31\n",
            "3600 3700 (100, 247) 140.48\n",
            "3700 3800 (100, 247) 115.44\n",
            "3800 3900 (100, 247) 111.25\n",
            "3900 4000 (100, 247) 105.4\n",
            "4000 4100 (100, 247) 113.43\n",
            "4100 4200 (100, 247) 107.06\n",
            "4200 4300 (100, 247) 107.44\n",
            "4300 4400 (12, 247) 14.69\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "thnr68oMKxDp"
      },
      "source": [
        "df  =  pd.concat([pd.read_pickle('temp/'+file)  for file in sorted(os.listdir('temp'))])"
      ],
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qL5IR7t5wW3Z"
      },
      "source": [
        "# **https://spacy.io/api/annotation#named-entities  --  Grouping NER Tags together.\r\n",
        "\r\n",
        "# PERSON\t    People, including fictional.\r\n",
        "\r\n",
        "# GPE\t        Countries, cities, states.\r\n",
        "# LOC       \tNon-GPE locations, mountain ranges, bodies of water.\r\n",
        "\r\n",
        "# ORG\t        Companies, agencies, institutions, etc.\r\n",
        "# FAC\t        Buildings, airports, highways, bridges, etc.\r\n",
        "# PRODUCT\t    Objects, vehicles, foods, etc. (Not services.)\r\n",
        "\r\n",
        "# EVENT     \tNamed hurricanes, battles, wars, sports events, etc.    [Rare]\r\n",
        "\r\n",
        "# WORK_OF_ART\tTitles of books, songs, etc.    [Rare]\r\n",
        "\r\n",
        "# LANGUAGE\t    Any named language.  [Rare]\r\n",
        "\r\n",
        "# DATE\t        Absolute or relative dates or periods.\r\n",
        "# TIME\t        Times smaller than a day.\r\n",
        "\r\n",
        "# ORDINAL\t    “first”, “second”, etc.\r\n",
        "# PERCENT\t    Percentage, including ”%“.\r\n",
        "# CARDINAL  \tNumerals that do not fall under another type.\r\n",
        "# QUANTITY\t    Measurements, as of weight or distance.\r\n",
        "\r\n",
        "# MONEY\t        Monetary values, including unit.\r\n"
      ],
      "execution_count": 158,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "596mnPDrN3HQ"
      },
      "source": [
        "# Features from 'video_type_name' and 'talks__tags' columns"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OmMQZmMXN3cq"
      },
      "source": [
        "# df['video_type_name'].value_counts()#.index\r\n",
        "\r\n",
        "\r\n",
        "df['is_video__Stage']      =  (df['video_type_name']=='TED Stage Talk'    )*1\r\n",
        "df['is_video__TEDx']       =  (df['video_type_name']=='TEDx Talk'         )*1\r\n",
        "df['is_video__TED_Ed']     =  (df['video_type_name']=='TED-Ed Original'   )*1\r\n",
        "df['is_video__Institute']  =  (df['video_type_name']=='TED Institute Talk')*1\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "# TODO:  Improve these buckets - will take some time to do manually.  Or, do market basket analysis to do it partly automatically.\r\n",
        "# l  =  sum([ast.literal_eval(df['talks__tags'].iloc[i]) for i in range(len(df))], [])\r\n",
        "# x = Counter(l)\r\n",
        "# # [i[0] for i in x.most_common(87)]\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "stem_tags  =   ['science', 'technology', 'innovation',  'biology', 'medicine',\r\n",
        "                'brain', 'health', 'health care', 'public health',\r\n",
        "                'disease', 'space', 'oceans', 'neuroscience',\r\n",
        "                'engineering', 'invention', 'computers', 'Internet',\r\n",
        "                'data', 'math', 'physics', 'medical research', 'human body', \r\n",
        "                'mental health', 'illness', 'universe']\r\n",
        "\r\n",
        "culture_tags  =  ['culture', 'world cultures', 'society', 'social change',\r\n",
        "                  'community', 'race']\r\n",
        "\r\n",
        "global_issue_tags  =  ['global issues', 'sustainability', 'nature', 'climate change',\r\n",
        "                       'environment', 'future']\r\n",
        "\r\n",
        "\r\n",
        "df['is_talk_tags__stem']           =  [any([True  if tag in stem_tags          else False    for tag in ast.literal_eval(df['talks__tags'].iloc[idx])])*1        for idx in  range(len(df))]\r\n",
        "df['is_talk_tags__culture']        =  [any([True  if tag in culture_tags       else False    for tag in ast.literal_eval(df['talks__tags'].iloc[idx])])*1        for idx in  range(len(df))]\r\n",
        "df['is_talk_tags__global_issues']  =  [any([True  if tag in global_issue_tags  else False    for tag in ast.literal_eval(df['talks__tags'].iloc[idx])])*1        for idx in  range(len(df))]\r\n",
        "\r\n"
      ],
      "execution_count": 159,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-5RMsklZAuTE"
      },
      "source": [
        "# Date Time Features:  recording_date, published_timestamp"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m7kz33Ig793_",
        "outputId": "7c58e682-be7b-45d1-d127-024a508fc2d1"
      },
      "source": [
        "df['recording_date']       =  pd.to_datetime(df['recording_date'])\r\n",
        "df['published_timestamp']  =  pd.to_datetime(df['published_timestamp'])\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "df['recording_date__year']   =  df['recording_date'].dt.year\r\n",
        "df['recording_date__month']  =  df['recording_date'].dt.month\r\n",
        "df['recording_date__day']    =  df['recording_date'].dt.day\r\n",
        "\r\n",
        "df['recording_date__weekofyear']  =  df['recording_date'].dt.weekofyear\r\n",
        "df['recording_date__dayofweek']   =  df['recording_date'].dt.dayofweek    # Monday=0, Sunday=6\r\n",
        "df['recording_date__is_weekend']  =  np.where(((df['recording_date']).dt.dayofweek) < 5,0,1)\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "df['published_date__year']   =  df['published_timestamp'].dt.year\r\n",
        "df['published_date__month']  =  df['published_timestamp'].dt.month\r\n",
        "df['published_date__day']    =  df['published_timestamp'].dt.day\r\n",
        "\r\n",
        "df['published_date__weekofyear']  =  df['published_timestamp'].dt.weekofyear\r\n",
        "df['published_date__dayofweek']   =  df['published_timestamp'].dt.dayofweek\r\n",
        "df['published_date__is_weekend']  =  np.where(((df['published_timestamp']).dt.dayofweek) < 5,0,1)\r\n",
        "\r\n",
        "df['published_date__hour']  =  df['published_timestamp'].dt.hour\r\n",
        "\r\n",
        "\r\n",
        "\r\n",
        "df['published_minus_recording']  =  (df['published_timestamp'] - df['recording_date']).dt.days\r\n",
        "df['published_minus_recording'].min(), (df['published_minus_recording'] < 0).sum()    # 17 values are clearly wrong."
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-578.0, 17)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPUQ23G7ahNF"
      },
      "source": [
        "# Rearraning columns.\r\n",
        "df = df[['talk__id',\r\n",
        " 'talk__name',\r\n",
        " 'talk__name__2',\r\n",
        " 'talk__name__3',\r\n",
        " 'talk__name__4',\r\n",
        " 'talk__name__sentiment',\r\n",
        " 'talk__name__subjectivity',\r\n",
        " 'talk__name__char_count',\r\n",
        " 'talk__name__word_count',\r\n",
        " 'talk__name__word_density',\r\n",
        " 'talk__name__punctuation_count',\r\n",
        " 'talk__name__title_word_count',\r\n",
        " 'talk__name__upper_case_word_count',\r\n",
        " 'talk__name__stopword_count',\r\n",
        " 'talk__name__noun_count',\r\n",
        " 'talk__name__verb_count',\r\n",
        " 'talk__name__adj_count',\r\n",
        " 'talk__name__adv_count',\r\n",
        " 'talk__name__pron_count',\r\n",
        " 'talk__name__punctuation_count__norm',\r\n",
        " 'talk__name__title_word_count__norm',\r\n",
        " 'talk__name__upper_case_word_count__norm',\r\n",
        " 'talk__name__stopword_count__norm',\r\n",
        " 'talk__name__noun_count__norm',\r\n",
        " 'talk__name__verb_count__norm',\r\n",
        " 'talk__name__adj_count__norm',\r\n",
        " 'talk__name__adv_count__norm',\r\n",
        " 'talk__name__pron_count__norm',\r\n",
        " 'talk__description',\r\n",
        " 'talk__description__2',\r\n",
        " 'talk__description__3',\r\n",
        " 'talk__description__4',\r\n",
        " 'talk__description__sentiment',\r\n",
        " 'talk__description__subjectivity',\r\n",
        " 'talk__description__char_count',\r\n",
        " 'talk__description__word_count',\r\n",
        " 'talk__description__word_density',\r\n",
        " 'talk__description__punctuation_count',\r\n",
        " 'talk__description__title_word_count',\r\n",
        " 'talk__description__upper_case_word_count',\r\n",
        " 'talk__description__stopword_count',\r\n",
        " 'talk__description__noun_count',\r\n",
        " 'talk__description__verb_count',\r\n",
        " 'talk__description__adj_count',\r\n",
        " 'talk__description__adv_count',\r\n",
        " 'talk__description__pron_count',\r\n",
        " 'talk__description__punctuation_count__norm',\r\n",
        " 'talk__description__title_word_count__norm',\r\n",
        " 'talk__description__upper_case_word_count__norm',\r\n",
        " 'talk__description__stopword_count__norm',\r\n",
        " 'talk__description__noun_count__norm',\r\n",
        " 'talk__description__verb_count__norm',\r\n",
        " 'talk__description__adj_count__norm',\r\n",
        " 'talk__description__adv_count__norm',\r\n",
        " 'talk__description__pron_count__norm',\r\n",
        " 'view_count',\r\n",
        " 'comment_count',\r\n",
        " 'duration',\r\n",
        " 'transcript',\r\n",
        " 'transcript__2',\r\n",
        " 'transcript__3',\r\n",
        " 'transcript__4',\r\n",
        " 'transcript__5',\r\n",
        " 'transcript__sentiment',\r\n",
        " 'transcript__subjectivity',\r\n",
        " 'transcript__char_count',\r\n",
        " 'transcript__word_count',\r\n",
        " 'transcript__sentence_count',\r\n",
        " 'transcript__word_density',\r\n",
        " 'transcript__sentence_density_wrt_char',\r\n",
        " 'transcript__sentence_density_wrt_word',\r\n",
        " 'transcript__char_per_second',\r\n",
        " 'transcript__word_per_second',\r\n",
        " 'transcript__sentence_per_second',\r\n",
        " 'transcript__Laughter_count',\r\n",
        " 'transcript__Applause_count',\r\n",
        " 'transcript__exclamation_mark_count',\r\n",
        " 'transcript__question_mark_count',\r\n",
        " 'transcript__punctuation_count',\r\n",
        " 'transcript__title_word_count',\r\n",
        " 'transcript__upper_case_word_count',\r\n",
        " 'transcript__stopword_count',\r\n",
        " 'transcript__top_1000_count',\r\n",
        " 'transcript__top_3000_count',\r\n",
        " 'transcript__rare_word_count',\r\n",
        " 'transcript__noun_count',\r\n",
        " 'transcript__verb_count',\r\n",
        " 'transcript__adj_count',\r\n",
        " 'transcript__adv_count',\r\n",
        " 'transcript__pron_count',\r\n",
        " 'transcript__ner_person__count',\r\n",
        " 'transcript__ner_gpe_loc__count',\r\n",
        " 'transcript__ner_org_product__count',\r\n",
        " 'transcript__ner_event__count',\r\n",
        " 'transcript__ner_work_of_art__count',\r\n",
        " 'transcript__ner_language__count',\r\n",
        " 'transcript__ner_date_time__count',\r\n",
        " 'transcript__ner_quantity__count',\r\n",
        " 'transcript__ner_money__count',\r\n",
        " 'transcript__Laughter_count__norm',\r\n",
        " 'transcript__Applause_count__norm',\r\n",
        " 'transcript__exclamation_mark_count__norm',\r\n",
        " 'transcript__question_mark_count__norm',\r\n",
        " 'transcript__punctuation_count__norm',\r\n",
        " 'transcript__title_word_count__norm',\r\n",
        " 'transcript__upper_case_word_count__norm',\r\n",
        " 'transcript__stopword_count__norm',\r\n",
        " 'transcript__top_1000_count__norm',\r\n",
        " 'transcript__top_3000_count__norm',\r\n",
        " 'transcript__rare_word_count__norm',\r\n",
        " 'transcript__noun_count__norm',\r\n",
        " 'transcript__verb_count__norm',\r\n",
        " 'transcript__adj_count__norm',\r\n",
        " 'transcript__adv_count__norm',\r\n",
        " 'transcript__pron_count__norm',\r\n",
        " 'transcript__ner_person__count__norm',\r\n",
        " 'transcript__ner_gpe_loc__count__norm',\r\n",
        " 'transcript__ner_org_product__count__norm',\r\n",
        " 'transcript__ner_event__count__norm',\r\n",
        " 'transcript__ner_work_of_art__count__norm',\r\n",
        " 'transcript__ner_language__count__norm',\r\n",
        " 'transcript__ner_date_time__count__norm',\r\n",
        " 'transcript__ner_quantity__count__norm',\r\n",
        " 'transcript__ner_money__count__norm',\r\n",
        " 'video_type_name',\r\n",
        " 'is_video__Stage',\r\n",
        " 'is_video__TEDx',\r\n",
        " 'is_video__TED_Ed',\r\n",
        " 'is_video__Institute',\r\n",
        " 'event',\r\n",
        " 'number_of__speakers',\r\n",
        " 'speaker__id',\r\n",
        " 'speaker__name',\r\n",
        " 'speaker__description',\r\n",
        " 'speaker__who_he_is',\r\n",
        " 'speaker__who_he_is__2',\r\n",
        " 'speaker__who_he_is__3',\r\n",
        " 'speaker__who_he_is__4',\r\n",
        " 'speaker__who_he_is__sentiment',\r\n",
        " 'speaker__who_he_is__subjectivity',\r\n",
        " 'speaker__who_he_is__char_count',\r\n",
        " 'speaker__who_he_is__word_count',\r\n",
        " 'speaker__who_he_is__word_density',\r\n",
        " 'speaker__who_he_is__punctuation_count',\r\n",
        " 'speaker__who_he_is__title_word_count',\r\n",
        " 'speaker__who_he_is__upper_case_word_count',\r\n",
        " 'speaker__who_he_is__stopword_count',\r\n",
        " 'speaker__who_he_is__noun_count',\r\n",
        " 'speaker__who_he_is__verb_count',\r\n",
        " 'speaker__who_he_is__adj_count',\r\n",
        " 'speaker__who_he_is__adv_count',\r\n",
        " 'speaker__who_he_is__pron_count',\r\n",
        " 'speaker__who_he_is__punctuation_count__norm',\r\n",
        " 'speaker__who_he_is__title_word_count__norm',\r\n",
        " 'speaker__who_he_is__upper_case_word_count__norm',\r\n",
        " 'speaker__who_he_is__stopword_count__norm',\r\n",
        " 'speaker__who_he_is__noun_count__norm',\r\n",
        " 'speaker__who_he_is__verb_count__norm',\r\n",
        " 'speaker__who_he_is__adj_count__norm',\r\n",
        " 'speaker__who_he_is__adv_count__norm',\r\n",
        " 'speaker__who_he_is__pron_count__norm',\r\n",
        " 'speaker__why_listen',\r\n",
        " 'speaker__why_listen__2',\r\n",
        " 'speaker__why_listen__3',\r\n",
        " 'speaker__why_listen__4',\r\n",
        " 'speaker__why_listen__sentiment',\r\n",
        " 'speaker__why_listen__subjectivity',\r\n",
        " 'speaker__why_listen__char_count',\r\n",
        " 'speaker__why_listen__word_count',\r\n",
        " 'speaker__why_listen__word_density',\r\n",
        " 'speaker__why_listen__punctuation_count',\r\n",
        " 'speaker__why_listen__title_word_count',\r\n",
        " 'speaker__why_listen__upper_case_word_count',\r\n",
        " 'speaker__why_listen__stopword_count',\r\n",
        " 'speaker__why_listen__noun_count',\r\n",
        " 'speaker__why_listen__verb_count',\r\n",
        " 'speaker__why_listen__adj_count',\r\n",
        " 'speaker__why_listen__adv_count',\r\n",
        " 'speaker__why_listen__pron_count',\r\n",
        " 'speaker__why_listen__punctuation_count__norm',\r\n",
        " 'speaker__why_listen__title_word_count__norm',\r\n",
        " 'speaker__why_listen__upper_case_word_count__norm',\r\n",
        " 'speaker__why_listen__stopword_count__norm',\r\n",
        " 'speaker__why_listen__noun_count__norm',\r\n",
        " 'speaker__why_listen__verb_count__norm',\r\n",
        " 'speaker__why_listen__adj_count__norm',\r\n",
        " 'speaker__why_listen__adv_count__norm',\r\n",
        " 'speaker__why_listen__pron_count__norm',\r\n",
        " 'speaker__what_others_say',\r\n",
        " 'speaker__what_others_say__2',\r\n",
        " 'speaker__what_others_say__3',\r\n",
        " 'speaker__what_others_say__4',\r\n",
        " 'speaker__what_others_say__sentiment',\r\n",
        " 'speaker__what_others_say__subjectivity',\r\n",
        " 'speaker__what_others_say__char_count',\r\n",
        " 'speaker__what_others_say__word_count',\r\n",
        " 'speaker__what_others_say__word_density',\r\n",
        " 'speaker__what_others_say__punctuation_count',\r\n",
        " 'speaker__what_others_say__title_word_count',\r\n",
        " 'speaker__what_others_say__upper_case_word_count',\r\n",
        " 'speaker__what_others_say__stopword_count',\r\n",
        " 'speaker__what_others_say__noun_count',\r\n",
        " 'speaker__what_others_say__verb_count',\r\n",
        " 'speaker__what_others_say__adj_count',\r\n",
        " 'speaker__what_others_say__adv_count',\r\n",
        " 'speaker__what_others_say__pron_count',\r\n",
        " 'speaker__what_others_say__punctuation_count__norm',\r\n",
        " 'speaker__what_others_say__title_word_count__norm',\r\n",
        " 'speaker__what_others_say__upper_case_word_count__norm',\r\n",
        " 'speaker__what_others_say__stopword_count__norm',\r\n",
        " 'speaker__what_others_say__noun_count__norm',\r\n",
        " 'speaker__what_others_say__verb_count__norm',\r\n",
        " 'speaker__what_others_say__adj_count__norm',\r\n",
        " 'speaker__what_others_say__adv_count__norm',\r\n",
        " 'speaker__what_others_say__pron_count__norm',\r\n",
        " 'speaker__is_published',\r\n",
        " 'all_speakers_details',\r\n",
        " 'is_talk_featured',\r\n",
        " 'has_talk_citation',\r\n",
        " 'recording_date',\r\n",
        " 'published_timestamp',\r\n",
        " 'recording_date__day',\r\n",
        " 'recording_date__month', \r\n",
        " 'recording_date__year',\r\n",
        " 'recording_date__dayofweek',\r\n",
        " 'recording_date__weekofyear', \r\n",
        " 'recording_date__is_weekend', \r\n",
        " 'published_date__hour',\r\n",
        " 'published_date__day',\r\n",
        " 'published_date__month', \r\n",
        " 'published_date__year',\r\n",
        " 'published_date__dayofweek',\r\n",
        " 'published_date__weekofyear', \r\n",
        " 'published_date__is_weekend', \r\n",
        " 'talks__tags',\r\n",
        " 'is_talk_tags__stem',\r\n",
        " 'is_talk_tags__culture',\r\n",
        " 'is_talk_tags__global_issues',\r\n",
        " 'number_of__tags',\r\n",
        " 'language',\r\n",
        " 'native_language',\r\n",
        " 'language_swap',\r\n",
        " 'is_subtitle_required',\r\n",
        " 'url__webpage',\r\n",
        " 'url__audio',\r\n",
        " 'url__video',\r\n",
        " 'url__photo__talk',\r\n",
        " 'url__photo__speaker',\r\n",
        " 'url__subtitled_videos',\r\n",
        " 'number_of__subtitled_videos',\r\n",
        " 'talk__download_languages',\r\n",
        " 'number_of__talk__download_languages',\r\n",
        " 'talk__more_resources',\r\n",
        " 'number_of__talk__more_resources',\r\n",
        " 'talk__recommendations__blurb',\r\n",
        " 'talk__recommendations',\r\n",
        " 'number_of__talk__recommendations',\r\n",
        " 'related_talks',\r\n",
        " 'number_of__related_talks',\r\n",
        " 'intro_duration',\r\n",
        " 'ad_duration',\r\n",
        " 'post_ad_duration',\r\n",
        " 'external__duration',\r\n",
        " 'external__start_time',\r\n",
        " 'talks__player_talks__resources__h264__00__bitrate',\r\n",
        " 'talks__take_action',\r\n",
        " 'number_of__talks__take_actions']]"
      ],
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PvhMUmQMwW6_"
      },
      "source": [
        "df.to_pickle('df.pkl')"
      ],
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yv6e4k3k790Z",
        "outputId": "a862bbb7-d033-44c8-f767-c18d358aeeb6"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4312, 267)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "_pSOz5Og797b",
        "outputId": "572a47bf-249a-40aa-dfde-36ff95da1c1c"
      },
      "source": [
        "df.sample(1)"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>talk__id</th>\n",
              "      <th>talk__name</th>\n",
              "      <th>talk__name__2</th>\n",
              "      <th>talk__name__3</th>\n",
              "      <th>talk__name__4</th>\n",
              "      <th>talk__name__sentiment</th>\n",
              "      <th>talk__name__subjectivity</th>\n",
              "      <th>talk__name__char_count</th>\n",
              "      <th>talk__name__word_count</th>\n",
              "      <th>talk__name__word_density</th>\n",
              "      <th>talk__name__punctuation_count</th>\n",
              "      <th>talk__name__title_word_count</th>\n",
              "      <th>talk__name__upper_case_word_count</th>\n",
              "      <th>talk__name__stopword_count</th>\n",
              "      <th>talk__name__noun_count</th>\n",
              "      <th>talk__name__verb_count</th>\n",
              "      <th>talk__name__adj_count</th>\n",
              "      <th>talk__name__adv_count</th>\n",
              "      <th>talk__name__pron_count</th>\n",
              "      <th>talk__name__punctuation_count__norm</th>\n",
              "      <th>talk__name__title_word_count__norm</th>\n",
              "      <th>talk__name__upper_case_word_count__norm</th>\n",
              "      <th>talk__name__stopword_count__norm</th>\n",
              "      <th>talk__name__noun_count__norm</th>\n",
              "      <th>talk__name__verb_count__norm</th>\n",
              "      <th>talk__name__adj_count__norm</th>\n",
              "      <th>talk__name__adv_count__norm</th>\n",
              "      <th>talk__name__pron_count__norm</th>\n",
              "      <th>talk__description</th>\n",
              "      <th>talk__description__2</th>\n",
              "      <th>talk__description__3</th>\n",
              "      <th>talk__description__4</th>\n",
              "      <th>talk__description__sentiment</th>\n",
              "      <th>talk__description__subjectivity</th>\n",
              "      <th>talk__description__char_count</th>\n",
              "      <th>talk__description__word_count</th>\n",
              "      <th>talk__description__word_density</th>\n",
              "      <th>talk__description__punctuation_count</th>\n",
              "      <th>talk__description__title_word_count</th>\n",
              "      <th>talk__description__upper_case_word_count</th>\n",
              "      <th>talk__description__stopword_count</th>\n",
              "      <th>talk__description__noun_count</th>\n",
              "      <th>talk__description__verb_count</th>\n",
              "      <th>talk__description__adj_count</th>\n",
              "      <th>talk__description__adv_count</th>\n",
              "      <th>talk__description__pron_count</th>\n",
              "      <th>talk__description__punctuation_count__norm</th>\n",
              "      <th>talk__description__title_word_count__norm</th>\n",
              "      <th>talk__description__upper_case_word_count__norm</th>\n",
              "      <th>talk__description__stopword_count__norm</th>\n",
              "      <th>talk__description__noun_count__norm</th>\n",
              "      <th>talk__description__verb_count__norm</th>\n",
              "      <th>talk__description__adj_count__norm</th>\n",
              "      <th>talk__description__adv_count__norm</th>\n",
              "      <th>talk__description__pron_count__norm</th>\n",
              "      <th>view_count</th>\n",
              "      <th>comment_count</th>\n",
              "      <th>duration</th>\n",
              "      <th>transcript</th>\n",
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              "      <th>transcript__3</th>\n",
              "      <th>transcript__4</th>\n",
              "      <th>transcript__5</th>\n",
              "      <th>transcript__sentiment</th>\n",
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              "      <th>transcript__char_count</th>\n",
              "      <th>transcript__word_count</th>\n",
              "      <th>transcript__sentence_count</th>\n",
              "      <th>transcript__word_density</th>\n",
              "      <th>transcript__sentence_density_wrt_char</th>\n",
              "      <th>transcript__sentence_density_wrt_word</th>\n",
              "      <th>transcript__char_per_second</th>\n",
              "      <th>transcript__word_per_second</th>\n",
              "      <th>transcript__sentence_per_second</th>\n",
              "      <th>transcript__Laughter_count</th>\n",
              "      <th>transcript__Applause_count</th>\n",
              "      <th>transcript__exclamation_mark_count</th>\n",
              "      <th>transcript__question_mark_count</th>\n",
              "      <th>transcript__punctuation_count</th>\n",
              "      <th>transcript__title_word_count</th>\n",
              "      <th>transcript__upper_case_word_count</th>\n",
              "      <th>transcript__stopword_count</th>\n",
              "      <th>transcript__top_1000_count</th>\n",
              "      <th>transcript__top_3000_count</th>\n",
              "      <th>transcript__rare_word_count</th>\n",
              "      <th>transcript__noun_count</th>\n",
              "      <th>transcript__verb_count</th>\n",
              "      <th>transcript__adj_count</th>\n",
              "      <th>transcript__adv_count</th>\n",
              "      <th>transcript__pron_count</th>\n",
              "      <th>transcript__ner_person__count</th>\n",
              "      <th>transcript__ner_gpe_loc__count</th>\n",
              "      <th>transcript__ner_org_product__count</th>\n",
              "      <th>transcript__ner_event__count</th>\n",
              "      <th>transcript__ner_work_of_art__count</th>\n",
              "      <th>transcript__ner_language__count</th>\n",
              "      <th>transcript__ner_date_time__count</th>\n",
              "      <th>transcript__ner_quantity__count</th>\n",
              "      <th>transcript__ner_money__count</th>\n",
              "      <th>transcript__Laughter_count__norm</th>\n",
              "      <th>transcript__Applause_count__norm</th>\n",
              "      <th>transcript__exclamation_mark_count__norm</th>\n",
              "      <th>transcript__question_mark_count__norm</th>\n",
              "      <th>transcript__punctuation_count__norm</th>\n",
              "      <th>transcript__title_word_count__norm</th>\n",
              "      <th>transcript__upper_case_word_count__norm</th>\n",
              "      <th>transcript__stopword_count__norm</th>\n",
              "      <th>transcript__top_1000_count__norm</th>\n",
              "      <th>transcript__top_3000_count__norm</th>\n",
              "      <th>transcript__rare_word_count__norm</th>\n",
              "      <th>transcript__noun_count__norm</th>\n",
              "      <th>transcript__verb_count__norm</th>\n",
              "      <th>transcript__adj_count__norm</th>\n",
              "      <th>transcript__adv_count__norm</th>\n",
              "      <th>transcript__pron_count__norm</th>\n",
              "      <th>transcript__ner_person__count__norm</th>\n",
              "      <th>transcript__ner_gpe_loc__count__norm</th>\n",
              "      <th>transcript__ner_org_product__count__norm</th>\n",
              "      <th>transcript__ner_event__count__norm</th>\n",
              "      <th>transcript__ner_work_of_art__count__norm</th>\n",
              "      <th>transcript__ner_language__count__norm</th>\n",
              "      <th>transcript__ner_date_time__count__norm</th>\n",
              "      <th>transcript__ner_quantity__count__norm</th>\n",
              "      <th>transcript__ner_money__count__norm</th>\n",
              "      <th>video_type_name</th>\n",
              "      <th>is_video__Stage</th>\n",
              "      <th>is_video__TEDx</th>\n",
              "      <th>is_video__TED_Ed</th>\n",
              "      <th>is_video__Institute</th>\n",
              "      <th>event</th>\n",
              "      <th>number_of__speakers</th>\n",
              "      <th>speaker__id</th>\n",
              "      <th>speaker__name</th>\n",
              "      <th>speaker__description</th>\n",
              "      <th>speaker__who_he_is</th>\n",
              "      <th>speaker__who_he_is__2</th>\n",
              "      <th>speaker__who_he_is__3</th>\n",
              "      <th>speaker__who_he_is__4</th>\n",
              "      <th>speaker__who_he_is__sentiment</th>\n",
              "      <th>speaker__who_he_is__subjectivity</th>\n",
              "      <th>speaker__who_he_is__char_count</th>\n",
              "      <th>speaker__who_he_is__word_count</th>\n",
              "      <th>speaker__who_he_is__word_density</th>\n",
              "      <th>speaker__who_he_is__punctuation_count</th>\n",
              "      <th>speaker__who_he_is__title_word_count</th>\n",
              "      <th>speaker__who_he_is__upper_case_word_count</th>\n",
              "      <th>speaker__who_he_is__stopword_count</th>\n",
              "      <th>speaker__who_he_is__noun_count</th>\n",
              "      <th>speaker__who_he_is__verb_count</th>\n",
              "      <th>speaker__who_he_is__adj_count</th>\n",
              "      <th>speaker__who_he_is__adv_count</th>\n",
              "      <th>speaker__who_he_is__pron_count</th>\n",
              "      <th>speaker__who_he_is__punctuation_count__norm</th>\n",
              "      <th>speaker__who_he_is__title_word_count__norm</th>\n",
              "      <th>speaker__who_he_is__upper_case_word_count__norm</th>\n",
              "      <th>speaker__who_he_is__stopword_count__norm</th>\n",
              "      <th>speaker__who_he_is__noun_count__norm</th>\n",
              "      <th>speaker__who_he_is__verb_count__norm</th>\n",
              "      <th>speaker__who_he_is__adj_count__norm</th>\n",
              "      <th>speaker__who_he_is__adv_count__norm</th>\n",
              "      <th>speaker__who_he_is__pron_count__norm</th>\n",
              "      <th>speaker__why_listen</th>\n",
              "      <th>speaker__why_listen__2</th>\n",
              "      <th>speaker__why_listen__3</th>\n",
              "      <th>speaker__why_listen__4</th>\n",
              "      <th>speaker__why_listen__sentiment</th>\n",
              "      <th>speaker__why_listen__subjectivity</th>\n",
              "      <th>speaker__why_listen__char_count</th>\n",
              "      <th>speaker__why_listen__word_count</th>\n",
              "      <th>speaker__why_listen__word_density</th>\n",
              "      <th>speaker__why_listen__punctuation_count</th>\n",
              "      <th>speaker__why_listen__title_word_count</th>\n",
              "      <th>speaker__why_listen__upper_case_word_count</th>\n",
              "      <th>speaker__why_listen__stopword_count</th>\n",
              "      <th>speaker__why_listen__noun_count</th>\n",
              "      <th>speaker__why_listen__verb_count</th>\n",
              "      <th>speaker__why_listen__adj_count</th>\n",
              "      <th>speaker__why_listen__adv_count</th>\n",
              "      <th>speaker__why_listen__pron_count</th>\n",
              "      <th>speaker__why_listen__punctuation_count__norm</th>\n",
              "      <th>speaker__why_listen__title_word_count__norm</th>\n",
              "      <th>speaker__why_listen__upper_case_word_count__norm</th>\n",
              "      <th>speaker__why_listen__stopword_count__norm</th>\n",
              "      <th>speaker__why_listen__noun_count__norm</th>\n",
              "      <th>speaker__why_listen__verb_count__norm</th>\n",
              "      <th>speaker__why_listen__adj_count__norm</th>\n",
              "      <th>speaker__why_listen__adv_count__norm</th>\n",
              "      <th>speaker__why_listen__pron_count__norm</th>\n",
              "      <th>speaker__what_others_say</th>\n",
              "      <th>speaker__what_others_say__2</th>\n",
              "      <th>speaker__what_others_say__3</th>\n",
              "      <th>speaker__what_others_say__4</th>\n",
              "      <th>speaker__what_others_say__sentiment</th>\n",
              "      <th>speaker__what_others_say__subjectivity</th>\n",
              "      <th>speaker__what_others_say__char_count</th>\n",
              "      <th>speaker__what_others_say__word_count</th>\n",
              "      <th>speaker__what_others_say__word_density</th>\n",
              "      <th>speaker__what_others_say__punctuation_count</th>\n",
              "      <th>speaker__what_others_say__title_word_count</th>\n",
              "      <th>speaker__what_others_say__upper_case_word_count</th>\n",
              "      <th>speaker__what_others_say__stopword_count</th>\n",
              "      <th>speaker__what_others_say__noun_count</th>\n",
              "      <th>speaker__what_others_say__verb_count</th>\n",
              "      <th>speaker__what_others_say__adj_count</th>\n",
              "      <th>speaker__what_others_say__adv_count</th>\n",
              "      <th>speaker__what_others_say__pron_count</th>\n",
              "      <th>speaker__what_others_say__punctuation_count__norm</th>\n",
              "      <th>speaker__what_others_say__title_word_count__norm</th>\n",
              "      <th>speaker__what_others_say__upper_case_word_count__norm</th>\n",
              "      <th>speaker__what_others_say__stopword_count__norm</th>\n",
              "      <th>speaker__what_others_say__noun_count__norm</th>\n",
              "      <th>speaker__what_others_say__verb_count__norm</th>\n",
              "      <th>speaker__what_others_say__adj_count__norm</th>\n",
              "      <th>speaker__what_others_say__adv_count__norm</th>\n",
              "      <th>speaker__what_others_say__pron_count__norm</th>\n",
              "      <th>speaker__is_published</th>\n",
              "      <th>all_speakers_details</th>\n",
              "      <th>is_talk_featured</th>\n",
              "      <th>has_talk_citation</th>\n",
              "      <th>recording_date</th>\n",
              "      <th>published_timestamp</th>\n",
              "      <th>recording_date__day</th>\n",
              "      <th>recording_date__month</th>\n",
              "      <th>recording_date__year</th>\n",
              "      <th>recording_date__dayofweek</th>\n",
              "      <th>recording_date__weekofyear</th>\n",
              "      <th>recording_date__is_weekend</th>\n",
              "      <th>published_date__hour</th>\n",
              "      <th>published_date__day</th>\n",
              "      <th>published_date__month</th>\n",
              "      <th>published_date__year</th>\n",
              "      <th>published_date__dayofweek</th>\n",
              "      <th>published_date__weekofyear</th>\n",
              "      <th>published_date__is_weekend</th>\n",
              "      <th>talks__tags</th>\n",
              "      <th>is_talk_tags__stem</th>\n",
              "      <th>is_talk_tags__culture</th>\n",
              "      <th>is_talk_tags__global_issues</th>\n",
              "      <th>number_of__tags</th>\n",
              "      <th>language</th>\n",
              "      <th>native_language</th>\n",
              "      <th>language_swap</th>\n",
              "      <th>is_subtitle_required</th>\n",
              "      <th>url__webpage</th>\n",
              "      <th>url__audio</th>\n",
              "      <th>url__video</th>\n",
              "      <th>url__photo__talk</th>\n",
              "      <th>url__photo__speaker</th>\n",
              "      <th>url__subtitled_videos</th>\n",
              "      <th>number_of__subtitled_videos</th>\n",
              "      <th>talk__download_languages</th>\n",
              "      <th>number_of__talk__download_languages</th>\n",
              "      <th>talk__more_resources</th>\n",
              "      <th>number_of__talk__more_resources</th>\n",
              "      <th>talk__recommendations__blurb</th>\n",
              "      <th>talk__recommendations</th>\n",
              "      <th>number_of__talk__recommendations</th>\n",
              "      <th>related_talks</th>\n",
              "      <th>number_of__related_talks</th>\n",
              "      <th>intro_duration</th>\n",
              "      <th>ad_duration</th>\n",
              "      <th>post_ad_duration</th>\n",
              "      <th>external__duration</th>\n",
              "      <th>external__start_time</th>\n",
              "      <th>talks__player_talks__resources__h264__00__bitrate</th>\n",
              "      <th>talks__take_action</th>\n",
              "      <th>number_of__talks__take_actions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1264</th>\n",
              "      <td>18514</td>\n",
              "      <td>Did the Amazons really exist?</td>\n",
              "      <td>Did the Amazons really exist?</td>\n",
              "      <td>do the Amazons really exist ?</td>\n",
              "      <td>amazons really exist</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>29</td>\n",
              "      <td>5</td>\n",
              "      <td>4.833333</td>\n",
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              "      <td>It was long assumed that Amazons, the fierce and fearsome women warriors of Greece, were imaginary. But curiously enough, stories from ancient Egypt, Persia, the Middle East, Central Asia, India and China also featured Amazon-like warrior women. And Amazons were described in ancient historical a...</td>\n",
              "      <td>It was long assumed that Amazons, the fierce and fearsome women warriors of Greece, were imaginary. But curiously enough, stories from ancient Egypt, Persia, the Middle East, Central Asia, India and China also featured Amazon-like warrior women. And Amazons were described in ancient historical a...</td>\n",
              "      <td>It be long assume that Amazons , the fierce and fearsome woman warrior of Greece , be imaginary . but curiously enough , story from ancient Egypt , Persia , the Middle East , Central Asia , India and China also feature Amazon - like warrior woman . and Amazons be describe in ancient historical a...</td>\n",
              "      <td>long assume amazons fierce fearsome woman warrior greece imaginary curiously enough story ancient egypt persia middle east central asia india china also feature amazon like warrior woman amazons describe ancient historical account myth real woman warrior know amazons adrienne mayor investigate t...</td>\n",
              "      <td>0.025</td>\n",
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              "      <td>0.090909</td>\n",
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              "      <td>2040214</td>\n",
              "      <td>NaN</td>\n",
              "      <td>302</td>\n",
              "      <td>Since the time of Homer, ancient stories told of fierce warriors  dwelling beyond the Mediterranean world, striking fear into the mightiest  empires of antiquity. Their exploits were recounted by many epic poets. They fought in the legendary Trojan War  and their grand army invaded Athens. Jason...</td>\n",
              "      <td>Since the time of Homer, ancient stories told of fierce warriors dwelling beyond the Mediterranean world, striking fear into the mightiest empires of antiquity. Their exploits were recounted by many epic poets. They fought in the legendary Trojan War and their grand army invaded Athens. Jason an...</td>\n",
              "      <td>since the time of Homer , ancient story tell of fierce warrior dwell beyond the Mediterranean world , strike fear into the mighty empire of antiquity . Their exploit be recount by many epic poet . They fight in the legendary Trojan War and their grand army invade Athens . Jason and the Argonauts...</td>\n",
              "      <td>since time homer ancient story tell fierce warrior dwell beyond mediterranean world strike fear mighty empire antiquity exploit recount many epic poet fight legendary trojan war grand army invade athens jason argonauts pass shore barely avoid deadly arrow formidable fighter face great champion m...</td>\n",
              "      <td>homer ancient fierce warrior dwell mediterranean strike mighty empire antiquity exploit recount epic poet legendary grand army invade shore barely deadly arrow formidable fighter champion myth warrior equal courage familiar ancient battle decorate acropolis statue adorn temple doll favorite vase...</td>\n",
              "      <td>0.111179</td>\n",
              "      <td>0.449701</td>\n",
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              "      <td>619</td>\n",
              "      <td>40</td>\n",
              "      <td>6.353226</td>\n",
              "      <td>96.073171</td>\n",
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              "      <td>12.914754</td>\n",
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              "      <td>93</td>\n",
              "      <td>2</td>\n",
              "      <td>233</td>\n",
              "      <td>147</td>\n",
              "      <td>222</td>\n",
              "      <td>101</td>\n",
              "      <td>204</td>\n",
              "      <td>98</td>\n",
              "      <td>72</td>\n",
              "      <td>33</td>\n",
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              "      <td>17</td>\n",
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              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.004839</td>\n",
              "      <td>0.164516</td>\n",
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              "      <td>TED-Ed Original</td>\n",
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              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>TED-Ed</td>\n",
              "      <td>1</td>\n",
              "      <td>4101.0</td>\n",
              "      <td>Adrienne Mayor</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
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              "      <td>[{'languageName': 'English', 'endonym': 'English', 'languageCode': 'en', 'ianaCode': 'en', 'isRtl': False}, {'languageName': 'Spanish', 'endonym': 'Español', 'languageCode': 'es', 'ianaCode': 'es', 'isRtl': False}, {'languageName': 'Turkish', 'endonym': 'Türkçe', 'languageCode': 'tr', 'ianaCode'...</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
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              "1264     18514  Did the Amazons really exist?  Did the Amazons really exist?   do the Amazons really exist ?  amazons really exist                    0.2                       0.2                      29                       5                  4.833333                              1                             2                                  0                           2                       2                       1                      0                      1                       0                             0.166667                            0.333333                                      0.0                          0.333333                      0.333333                      0.166667                          0.0                     0.166667                           0.0  It was long assumed that Amazons, the fierce and fearsome women warriors of Greece, were imaginary. But curiously enough, stories from ancient Egypt, Persia, the Middle East, Central Asia, India and China also featured Amazon-like warrior women. And Amazons were described in ancient historical a...  It was long assumed that Amazons, the fierce and fearsome women warriors of Greece, were imaginary. But curiously enough, stories from ancient Egypt, Persia, the Middle East, Central Asia, India and China also featured Amazon-like warrior women. And Amazons were described in ancient historical a...  It be long assume that Amazons , the fierce and fearsome woman warrior of Greece , be imaginary . but curiously enough , story from ancient Egypt , Persia , the Middle East , Central Asia , India and China also feature Amazon - like warrior woman . and Amazons be describe in ancient historical a...  long assume amazons fierce fearsome woman warrior greece imaginary curiously enough story ancient egypt persia middle east central asia india china also feature amazon like warrior woman amazons describe ancient historical account myth real woman warrior know amazons adrienne mayor investigate t...                         0.025                         0.241667                            437                             65                         6.621212                                    17                                   21                                         0                                 21                             30                              8                             8                             6                              2                                    0.257576                                   0.318182                                             0.0                                 0.318182                             0.454545                             0.121212                            0.121212                            0.090909                             0.030303     2040214            NaN       302  Since the time of Homer, ancient stories told of fierce warriors  dwelling beyond the Mediterranean world, striking fear into the mightiest  empires of antiquity. Their exploits were recounted by many epic poets. They fought in the legendary Trojan War  and their grand army invaded Athens. Jason...  Since the time of Homer, ancient stories told of fierce warriors dwelling beyond the Mediterranean world, striking fear into the mightiest empires of antiquity. Their exploits were recounted by many epic poets. They fought in the legendary Trojan War and their grand army invaded Athens. Jason an...  since the time of Homer , ancient story tell of fierce warrior dwell beyond the Mediterranean world , strike fear into the mighty empire of antiquity . Their exploit be recount by many epic poet . They fight in the legendary Trojan War and their grand army invade Athens . Jason and the Argonauts...  since time homer ancient story tell fierce warrior dwell beyond mediterranean world strike fear mighty empire antiquity exploit recount many epic poet fight legendary trojan war grand army invade athens jason argonauts pass shore barely avoid deadly arrow formidable fighter face great champion m...  homer ancient fierce warrior dwell mediterranean strike mighty empire antiquity exploit recount epic poet legendary grand army invade shore barely deadly arrow formidable fighter champion myth warrior equal courage familiar ancient battle decorate acropolis statue adorn temple doll favorite vase...               0.111179                  0.449701                    3939                     619                          40                  6.353226                              96.073171                              15.097561                    12.914754                     2.029508                         0.131148                           0                           0                                   0                                3                            102                            93                                  2                         233                         147                         222                          101                     204                      98                     72                     33                      24                              6                              15                                  17                             0                                   0                                0                                 2                                4                             0                               0.0                               0.0                                       0.0                               0.004839                             0.164516                                0.15                                 0.003226                          0.375806                          0.237097                          0.358065                           0.162903                      0.329032                      0.158065                     0.116129                     0.053226                       0.03871                             0.009677                              0.024194                                  0.027419                                 0.0                                       0.0                                    0.0                                0.003226                               0.006452                                 0.0  TED-Ed Original                0               0                 1                    0  TED-Ed                    1       4101.0  Adrienne Mayor                  NaN                NaN                                                                                              0.0                               0.0                               0                               0                               0.0                                      0                                     0                                          0                                   0                               0                               0                              0                              0                               0                                          0.0                                         0.0                                              0.0                                       0.0                                   0.0                                   0.0                                  0.0                                  0.0                                   0.0                 NaN                                                                                                  0.0                                0.0                                0                                0                                0.0                                       0                 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       False               NaN     2018-06-18 2018-06-18 19:13:48                 18.0                    6.0                2018.0                        0.0                        25.0                           0                    19                   18                      6                  2018                          0                          25                           0  ['TED-Ed', 'history', 'ancient world']                   0                      0                            0                3       en              en          False                 False  https://www.ted.com/talks/adrienne_mayor_did_the_amazons_really_exist        NaN        NaN  https://s3.amazonaws.com/talkstar-photos/uploads/b75b8947-67dd-4fdf-ae7f-0f8020621fa3/amazon_texless.jpg                 NaN                   NaN                            0  [{'languageName': 'English', 'endonym': 'English', 'languageCode': 'en', 'ianaCode': 'en', 'isRtl': False}, {'languageName': 'Spanish', 'endonym': 'Español', 'languageCode': 'es', 'ianaCode': 'es', 'isRtl': False}, {'languageName': 'Turkish', 'endonym': 'Türkçe', 'languageCode': 'tr', 'ianaCode'...                                   23                  NaN                                0                          NaN                   NaN                                 0  [{'id': '17848', 'hero': 'https://s3.amazonaws.com/talkstar-photos/uploads/78acbec6-ef10-4149-93c3-184f81148edd/giants_textless.jpg', 'speaker': 'Iseult Gillespie', 'title': \"The Irish myth of the Giant's Causeway\", 'duration': 223, 'slug': 'iseult_gillespie_the_irish_myth_of_the_giant_s_causewa...                         6             0.0          0.0               0.0               306.0                   0.0                                              180.0                NaN                               0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xj7pCb6nG8iA"
      },
      "source": [
        ""
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yGDza3cL8eTF"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bAVwBb9l8eWo"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2giiBHthCZC-"
      },
      "source": [
        "# Understanding relation between different features\r\n",
        "## Analysing correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mbCryJiXG8lh",
        "outputId": "5ac6a479-55d4-4de3-8234-5c8a42e3ea98"
      },
      "source": [
        "df_corr_columns  =  df.corr().columns.tolist()    # the list of numerical columns.\r\n",
        "# THRESHOLD = 0.5, 1    # -1, -0.3\r\n",
        "THRESHOLD = 0.8\r\n",
        "\r\n",
        "edges = []\r\n",
        "for  c1,c2  in itertools.combinations(df_corr_columns,2):\r\n",
        "    corr = df[c1].corr(df[c2])\r\n",
        "    # if (corr > THRESHOLD[0]) and (corr < THRESHOLD[1])      and (c1.split('_')[0] != c2.split('_')[0]):\r\n",
        "    if (abs(corr) > THRESHOLD)   and (c1.split('_')[0] != c2.split('_')[0]):\r\n",
        "        edges.append([c1, c2])\r\n",
        "        print(c1 + ';  ' + c2 + ';  ' + str(round(corr, 4)))\r\n",
        "        "
      ],
      "execution_count": 230,
      "outputs": [
        {
          "output_type": "stream",
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            "is_video__TED_Ed;  speaker__why_listen__stopword_count__norm;  -0.8428\n",
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            "speaker__why_listen__word_density;  is_talk_featured;  0.9282\n",
            "speaker__why_listen__word_density;  intro_duration;  0.9033\n",
            "speaker__why_listen__word_density;  ad_duration;  0.9033\n",
            "speaker__why_listen__word_density;  post_ad_duration;  0.9033\n",
            "speaker__why_listen__stopword_count__norm;  is_talk_featured;  0.9143\n",
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            "speaker__why_listen__verb_count__norm;  is_talk_featured;  0.8144\n",
            "is_talk_featured;  intro_duration;  0.9469\n",
            "is_talk_featured;  ad_duration;  0.9469\n",
            "is_talk_featured;  post_ad_duration;  0.9469\n",
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            "intro_duration;  ad_duration;  1.0\n",
            "intro_duration;  post_ad_duration;  1.0\n",
            "ad_duration;  post_ad_duration;  1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1yFS3WBMwXAy"
      },
      "source": [
        "# Findings:\r\n",
        "# Mostly, Featured Talks are from Published Speakers and vice versa. Exception: There are 90 Non-featured Talks from Published Speakers."
      ],
      "execution_count": 226,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i5fcSq9RYyYr"
      },
      "source": [
        "#### Visualize Correlation Pairs as Interactive Graphs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUa6dLJRaK7o"
      },
      "source": [
        "!pip install dimcli networkx pyvis -U --quiet\r\n",
        "from dimcli.core.extras import NetworkViz\r\n",
        "import networkx as nx"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tr65DXWl9SVJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "outputId": "0f921ed9-bfc7-4f47-a572-c630ff6c7df3"
      },
      "source": [
        "nodes = list(set(sum(edges, [])))\r\n",
        "edges = [tuple(i) for i in edges]\r\n",
        "\r\n",
        "g = NetworkViz(notebook=True)    # height=100, width=30, directed=True\r\n",
        "g.add_nodes(nodes)\r\n",
        "g.add_edges(edges)\r\n",
        "g.show(\"example.html\")"
      ],
      "execution_count": 231,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<html>\n",
              "<head>\n",
              "<link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/vis/4.16.1/vis.css\" type=\"text/css\" />\n",
              "<script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/vis/4.16.1/vis-network.min.js\"> </script>\n",
              "<center>\n",
              "<h1>None</h1>\n",
              "</center>\n",
              "\n",
              "<!-- <link rel=\"stylesheet\" href=\"../node_modules/vis/dist/vis.min.css\" type=\"text/css\" />\n",
              "<script type=\"text/javascript\" src=\"../node_modules/vis/dist/vis.js\"> </script>-->\n",
              "\n",
              "<style type=\"text/css\">\n",
              "\n",
              "        #mynetwork {\n",
              "            width: 500px;\n",
              "            height: 500px;\n",
              "            background-color: #ffffff;\n",
              "            border: 1px solid lightgray;\n",
              "            position: relative;\n",
              "            float: left;\n",
              "        }\n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "</style>\n",
              "\n",
              "</head>\n",
              "\n",
              "<body>\n",
              "<div id = \"mynetwork\"></div>\n",
              "\n",
              "\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "    // initialize global variables.\n",
              "    var edges;\n",
              "    var nodes;\n",
              "    var network; \n",
              "    var container;\n",
              "    var options, data;\n",
              "\n",
              "    \n",
              "    // This method is responsible for drawing the graph, returns the drawn network\n",
              "    function drawGraph() {\n",
              "        var container = document.getElementById('mynetwork');\n",
              "        \n",
              "        \n",
              "\n",
              "        // parsing and collecting nodes and edges from the python\n",
              "        nodes = new vis.DataSet([{\"id\": \"transcript__stopword_count\", \"label\": \"transcript__stopword_count\", \"shape\": \"dot\"}, {\"id\": \"speaker__why_listen__stopword_count__norm\", \"label\": \"speaker__why_listen__stopword_count__norm\", \"shape\": \"dot\"}, {\"id\": \"transcript__pron_count\", \"label\": \"transcript__pron_count\", \"shape\": \"dot\"}, {\"id\": \"speaker__why_listen__noun_count__norm\", \"label\": \"speaker__why_listen__noun_count__norm\", \"shape\": \"dot\"}, {\"id\": \"intro_duration\", \"label\": \"intro_duration\", \"shape\": \"dot\"}, {\"id\": \"transcript__verb_count\", \"label\": \"transcript__verb_count\", \"shape\": \"dot\"}, {\"id\": \"transcript__noun_count\", \"label\": \"transcript__noun_count\", \"shape\": \"dot\"}, {\"id\": \"is_video__TED_Ed\", \"label\": \"is_video__TED_Ed\", \"shape\": \"dot\"}, {\"id\": \"speaker__who_he_is__word_density\", \"label\": \"speaker__who_he_is__word_density\", \"shape\": \"dot\"}, {\"id\": \"published_date__year\", \"label\": \"published_date__year\", \"shape\": \"dot\"}, {\"id\": \"transcript__punctuation_count\", \"label\": \"transcript__punctuation_count\", \"shape\": \"dot\"}, {\"id\": \"transcript__word_count\", \"label\": \"transcript__word_count\", \"shape\": \"dot\"}, {\"id\": \"transcript__top_3000_count\", \"label\": \"transcript__top_3000_count\", \"shape\": \"dot\"}, {\"id\": \"duration\", \"label\": \"duration\", \"shape\": \"dot\"}, {\"id\": \"transcript__char_count\", \"label\": \"transcript__char_count\", \"shape\": \"dot\"}, {\"id\": \"ad_duration\", \"label\": \"ad_duration\", \"shape\": \"dot\"}, {\"id\": \"speaker__why_listen__word_density\", \"label\": \"speaker__why_listen__word_density\", \"shape\": \"dot\"}, {\"id\": \"transcript__adj_count\", \"label\": \"transcript__adj_count\", \"shape\": \"dot\"}, {\"id\": \"transcript__top_1000_count\", \"label\": \"transcript__top_1000_count\", \"shape\": \"dot\"}, {\"id\": \"is_talk_featured\", \"label\": \"is_talk_featured\", \"shape\": \"dot\"}, {\"id\": \"transcript__sentence_count\", \"label\": \"transcript__sentence_count\", \"shape\": \"dot\"}, {\"id\": \"recording_date__year\", \"label\": \"recording_date__year\", \"shape\": \"dot\"}, {\"id\": \"transcript__title_word_count\", \"label\": \"transcript__title_word_count\", \"shape\": \"dot\"}, {\"id\": \"post_ad_duration\", \"label\": \"post_ad_duration\", \"shape\": \"dot\"}, {\"id\": \"transcript__adv_count\", \"label\": \"transcript__adv_count\", \"shape\": \"dot\"}, {\"id\": \"speaker__why_listen__verb_count__norm\", \"label\": \"speaker__why_listen__verb_count__norm\", \"shape\": \"dot\"}]);\n",
              "        edges = new vis.DataSet([{\"from\": \"duration\", \"to\": \"transcript__char_count\"}, {\"from\": \"duration\", \"to\": \"transcript__word_count\"}, {\"from\": \"duration\", \"to\": \"transcript__sentence_count\"}, {\"from\": \"duration\", \"to\": \"transcript__punctuation_count\"}, {\"from\": \"duration\", \"to\": \"transcript__title_word_count\"}, {\"from\": \"duration\", \"to\": \"transcript__stopword_count\"}, {\"from\": \"duration\", \"to\": \"transcript__top_1000_count\"}, {\"from\": \"duration\", \"to\": \"transcript__top_3000_count\"}, {\"from\": \"duration\", \"to\": \"transcript__noun_count\"}, {\"from\": \"duration\", \"to\": \"transcript__verb_count\"}, {\"from\": \"duration\", \"to\": \"transcript__adj_count\"}, {\"from\": \"duration\", \"to\": \"transcript__adv_count\"}, {\"from\": \"duration\", \"to\": \"transcript__pron_count\"}, {\"from\": \"is_video__TED_Ed\", \"to\": \"speaker__who_he_is__word_density\"}, {\"from\": \"is_video__TED_Ed\", \"to\": \"speaker__why_listen__word_density\"}, {\"from\": \"is_video__TED_Ed\", \"to\": \"speaker__why_listen__stopword_count__norm\"}, {\"from\": \"is_video__TED_Ed\", \"to\": \"speaker__why_listen__noun_count__norm\"}, {\"from\": \"is_video__TED_Ed\", \"to\": \"intro_duration\"}, {\"from\": \"is_video__TED_Ed\", \"to\": \"ad_duration\"}, {\"from\": \"is_video__TED_Ed\", \"to\": \"post_ad_duration\"}, {\"from\": \"speaker__why_listen__word_density\", \"to\": \"is_talk_featured\"}, {\"from\": \"speaker__why_listen__word_density\", \"to\": \"intro_duration\"}, {\"from\": \"speaker__why_listen__word_density\", \"to\": \"ad_duration\"}, {\"from\": \"speaker__why_listen__word_density\", \"to\": \"post_ad_duration\"}, {\"from\": \"speaker__why_listen__stopword_count__norm\", \"to\": \"is_talk_featured\"}, {\"from\": \"speaker__why_listen__stopword_count__norm\", \"to\": \"intro_duration\"}, {\"from\": \"speaker__why_listen__stopword_count__norm\", \"to\": \"ad_duration\"}, {\"from\": \"speaker__why_listen__stopword_count__norm\", \"to\": \"post_ad_duration\"}, {\"from\": \"speaker__why_listen__noun_count__norm\", \"to\": \"is_talk_featured\"}, {\"from\": \"speaker__why_listen__noun_count__norm\", \"to\": \"intro_duration\"}, {\"from\": \"speaker__why_listen__noun_count__norm\", \"to\": \"ad_duration\"}, {\"from\": \"speaker__why_listen__noun_count__norm\", \"to\": \"post_ad_duration\"}, {\"from\": \"speaker__why_listen__verb_count__norm\", \"to\": \"is_talk_featured\"}, {\"from\": \"is_talk_featured\", \"to\": \"intro_duration\"}, {\"from\": \"is_talk_featured\", \"to\": \"ad_duration\"}, {\"from\": \"is_talk_featured\", \"to\": \"post_ad_duration\"}, {\"from\": \"recording_date__year\", \"to\": \"published_date__year\"}, {\"from\": \"intro_duration\", \"to\": \"ad_duration\"}, {\"from\": \"intro_duration\", \"to\": \"post_ad_duration\"}, {\"from\": \"ad_duration\", \"to\": \"post_ad_duration\"}]);\n",
              "\n",
              "        // adding nodes and edges to the graph\n",
              "        data = {nodes: nodes, edges: edges};\n",
              "\n",
              "        var options = {\n",
              "    \"configure\": {\n",
              "        \"enabled\": false\n",
              "    },\n",
              "    \"edges\": {\n",
              "        \"color\": {\n",
              "            \"inherit\": true\n",
              "        },\n",
              "        \"smooth\": {\n",
              "            \"enabled\": false,\n",
              "            \"type\": \"continuous\"\n",
              "        }\n",
              "    },\n",
              "    \"interaction\": {\n",
              "        \"dragNodes\": true,\n",
              "        \"hideEdgesOnDrag\": false,\n",
              "        \"hideNodesOnDrag\": false\n",
              "    },\n",
              "    \"physics\": {\n",
              "        \"enabled\": true,\n",
              "        \"stabilization\": {\n",
              "            \"enabled\": true,\n",
              "            \"fit\": true,\n",
              "            \"iterations\": 1000,\n",
              "            \"onlyDynamicEdges\": false,\n",
              "            \"updateInterval\": 50\n",
              "        }\n",
              "    }\n",
              "};\n",
              "        \n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        network = new vis.Network(container, data, options);\n",
              "\n",
              "        \n",
              "\n",
              "\n",
              "        \n",
              "\n",
              "        return network;\n",
              "\n",
              "    }\n",
              "\n",
              "    drawGraph();\n",
              "\n",
              "</script>\n",
              "</body>\n",
              "</html>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 231
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3Q_l1Dceqn9"
      },
      "source": [
        "# TODO: Work on making weighted Graphs for representing the Correlation value.\r\n",
        "#       Use Networkx to form Feature clusters."
      ],
      "execution_count": 209,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q0MaWQg4ejmd"
      },
      "source": [
        ""
      ],
      "execution_count": 227,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XuPjlUi7lj1d"
      },
      "source": [
        "## Feature Clustering based on Correlation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "88th4KQdejpG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9bc0c9bc-63dc-431c-fc4c-8ab83e61665e"
      },
      "source": [
        "df_corr  =  df.corr()\r\n",
        "print(df_corr.shape)\r\n",
        "df_corr = df_corr.dropna(how='all', axis=0).dropna(how='all', axis=1)\r\n",
        "print(df_corr.shape)"
      ],
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(218, 218)\n",
            "(215, 215)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "94Xzmcp4mUZf",
        "outputId": "36957978-7659-49ce-bac7-c7b71f8eb4b6"
      },
      "source": [
        "import scipy.cluster.hierarchy as sch\r\n",
        "\r\n",
        "d = sch.distance.pdist(df_corr.values)\r\n",
        "L = sch.linkage(d, method='complete')\r\n",
        "ind = sch.fcluster(L, 0.35*d.max(), 'distance')\r\n",
        "columns  =  [df_corr.columns.tolist()[i]  for i in list(np.argsort(ind))]\r\n",
        "\r\n",
        "dendrogram  =  sch.dendrogram(L,\r\n",
        "                              color_threshold=0.35*d.max(),\r\n",
        "                              labels=df_corr.columns.tolist(), \r\n",
        "                              orientation='left',\r\n",
        "                              leaf_font_size=12)\r\n",
        "\r\n",
        "plt.rcParams[\"figure.figsize\"] = (20,80)\r\n",
        "plt.title('Dendrogram')\r\n",
        "plt.xlabel('Euclidean Distances')\r\n",
        "plt.show()"
      ],
      "execution_count": 275,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x5760 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jprPvHJ0ejsK"
      },
      "source": [
        ""
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3hI4MXJnejut"
      },
      "source": [
        ""
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JLcdj3ZSlRZN"
      },
      "source": [
        ""
      ],
      "execution_count": 275,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WtvwxIcqq1tk"
      },
      "source": [
        "# df.to_pickle('df.pkl')\r\n",
        "df = pd.read_pickle('df.pkl')"
      ],
      "execution_count": 276,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2AOJnsWoq13M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2bd17ba3-65d1-4d33-cee7-15c58edb9b57"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 279,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4312, 267)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 279
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mMX9bRAuoCei"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XsQ-Sv6x9SdK"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b-28y10b21fO"
      },
      "source": [
        "# Most common words/phrases"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rabd6wpsCgN3"
      },
      "source": [
        "### Tokens/Words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WCOJ3YJqCnoR"
      },
      "source": [
        "# Source:  https://github.com/susanli2016/NLP-with-Python/blob/master/EDA%20and%20visualization%20for%20Text%20Data.ipynb\r\n",
        "from sklearn.feature_extraction.text import CountVectorizer\r\n",
        "\r\n",
        "def get_top_n(corpus, nr, n=20):\r\n",
        "    vec = CountVectorizer(ngram_range=(nr,nr), stop_words = 'english').fit(corpus)\r\n",
        "    bag_of_words = vec.transform(corpus)\r\n",
        "    sum_words = bag_of_words.sum(axis=0) \r\n",
        "    words_freq = [(word, sum_words[0, idx]) for word, idx in vec.vocabulary_.items()]\r\n",
        "    words_freq = sorted(words_freq, key = lambda x: x[1], reverse=True)\r\n",
        "    return words_freq[:n]\r\n"
      ],
      "execution_count": 280,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QWcEnQQprnpZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "86651c1c-4033-47ef-fa53-4034d9ac3cbd"
      },
      "source": [
        "# On entire 'transcript__2' column.\r\n",
        "common_words = get_top_n(df['transcript__2'], 1)\r\n",
        "for word, freq in common_words:    print(word, freq)"
      ],
      "execution_count": 286,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "people 28806\n",
            "like 28566\n",
            "just 23572\n",
            "know 18608\n",
            "going 17371\n",
            "think 17103\n",
            "time 15915\n",
            "really 15312\n",
            "laughter 14424\n",
            "world 14112\n",
            "way 13254\n",
            "did 12975\n",
            "years 12623\n",
            "actually 12035\n",
            "make 11915\n",
            "things 11849\n",
            "want 11589\n",
            "right 11190\n",
            "new 9888\n",
            "work 9539\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "olggiojtC0NO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e18ec976-51f0-4b1b-b5aa-b9aa6f568f4e"
      },
      "source": [
        "# On first 'transcript2'.\r\n",
        "common_words = get_top_n(df[['transcript__2']].iloc[0], 1)\r\n",
        "for word, freq in common_words:    print(word, freq)"
      ],
      "execution_count": 284,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "laughter 40\n",
            "think 26\n",
            "education 22\n",
            "said 22\n",
            "did 17\n",
            "people 16\n",
            "say 10\n",
            "things 10\n",
            "way 10\n",
            "want 9\n",
            "know 9\n",
            "like 9\n",
            "school 9\n",
            "just 8\n",
            "children 8\n",
            "really 8\n",
            "human 7\n",
            "actually 7\n",
            "world 7\n",
            "kids 7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dQaFZp13tEza",
        "outputId": "7454b1a1-416f-419a-9764-14d4ba107972"
      },
      "source": [
        "# On first 'transcript__5'.\r\n",
        "common_words = get_top_n(df[['transcript__5']].iloc[0], 1)\r\n",
        "for word, freq in common_words:    print(word, freq)"
      ],
      "execution_count": 287,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "laughter 40\n",
            "educate 6\n",
            "capacity 6\n",
            "earth 6\n",
            "dance 6\n",
            "intelligence 6\n",
            "creativity 5\n",
            "wonderful 5\n",
            "ballet 5\n",
            "extraordinary 4\n",
            "applause 4\n",
            "bear 4\n",
            "creative 4\n",
            "university 4\n",
            "academic 4\n",
            "royal 4\n",
            "frankly 3\n",
            "god 3\n",
            "talent 3\n",
            "gift 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mbx1tdPRBO6n"
      },
      "source": [
        "### Bigrams"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L83OrqnPt3Iy",
        "outputId": "3ff03463-b553-4c20-d560-ca5753440bba"
      },
      "source": [
        "# On entire 'transcript__2' column.\r\n",
        "common_words = get_top_n(df['transcript__2'], 2)\r\n",
        "for word, freq in common_words:    print(word, freq)"
      ],
      "execution_count": 289,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "thank applause 3133\n",
            "years ago 2678\n",
            "little bit 2099\n",
            "united states 1743\n",
            "new york 1253\n",
            "things like 1088\n",
            "look like 1060\n",
            "just like 1034\n",
            "looks like 973\n",
            "did know 941\n",
            "years old 918\n",
            "year old 915\n",
            "laughter applause 847\n",
            "climate change 847\n",
            "10 years 695\n",
            "make sure 681\n",
            "people like 674\n",
            "long time 661\n",
            "20 years 612\n",
            "health care 605\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KbxZAvjQuBwv",
        "outputId": "71376974-06dc-4fa3-9967-715eaa58450e"
      },
      "source": [
        "# On entire 'transcript__5' column.\r\n",
        "common_words = get_top_n(df['transcript__5'], 2)\r\n",
        "for word, freq in common_words:    print(word, freq)"
      ],
      "execution_count": 290,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "laughter applause 1033\n",
            "laughter laughter 805\n",
            "applause applause 610\n",
            "percent percent 503\n",
            "brain brain 414\n",
            "carbon dioxide 263\n",
            "global warming 244\n",
            "fossil fuel 234\n",
            "datum datum 225\n",
            "artificial intelligence 211\n",
            "dollar dollar 197\n",
            "york times 187\n",
            "lady gentleman 175\n",
            "trillion dollar 174\n",
            "applause cheer 166\n",
            "robot robot 161\n",
            "planet earth 159\n",
            "male female 158\n",
            "coral reef 155\n",
            "silicon valley 149\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uqh-5y3Gt3Ml",
        "outputId": "14a402c2-9136-464f-c009-6ca6422c022a"
      },
      "source": [
        "# On first 'transcript2'.\r\n",
        "common_words = get_top_n(df[['transcript__2']].iloc[0], 2)\r\n",
        "for word, freq in common_words:    print(word, freq)"
      ],
      "execution_count": 291,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "did want 4\n",
            "royal ballet 4\n",
            "want talk 3\n",
            "work education 3\n",
            "frankly laughter 3\n",
            "boy said 3\n",
            "los angeles 3\n",
            "public education 3\n",
            "education actually 2\n",
            "say work 2\n",
            "education think 2\n",
            "thank applause 2\n",
            "drawing lesson 2\n",
            "teacher said 2\n",
            "said girl 2\n",
            "said drawing 2\n",
            "girl said 2\n",
            "said bring 2\n",
            "frightened wrong 2\n",
            "prepared wrong 2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4dvwOEWbCAqM"
      },
      "source": [
        "### Trigrams"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wilaX33nrn35",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e5a86a0-90b9-4c50-b7a1-973afec33b00"
      },
      "source": [
        "# On entire 'transcript__2' column.\r\n",
        "common_words = get_top_n(df['transcript__2'], 3)\r\n",
        "for word, freq in common_words:    print(word, freq)"
      ],
      "execution_count": 293,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "new york city 322\n",
            "applause thank applause 233\n",
            "thank applause thank 201\n",
            "new york times 187\n",
            "000 years ago 172\n",
            "world war ii 156\n",
            "10 years ago 145\n",
            "little bit like 130\n",
            "million years ago 127\n",
            "just little bit 119\n",
            "couple years ago 117\n",
            "20 years ago 109\n",
            "thank thank applause 106\n",
            "sub saharan africa 100\n",
            "applause chris anderson 97\n",
            "talk little bit 96\n",
            "spend lot time 88\n",
            "tell little bit 83\n",
            "does look like 82\n",
            "10 000 years 80\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYFICc-zud4q"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NvXs-gt8vBTj"
      },
      "source": [
        "# Key Phrase Extraction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "09BRA6AdxwSv"
      },
      "source": [
        "text = df.transcript__2.iloc[0]\r\n",
        "count = 20"
      ],
      "execution_count": 315,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7px4YUwZvERq",
        "outputId": "2333f99b-f7ba-4a22-ec7c-55b7ddd527b1"
      },
      "source": [
        "!python -m spacy download en_core_web_sm\r\n",
        "!pip install pytextrank\r\n",
        "!pip install -r pytextrank/requirements.txt\r\n",
        "\r\n",
        "!pip install rake-nltk"
      ],
      "execution_count": 316,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: en_core_web_sm==2.2.5 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.5/en_core_web_sm-2.2.5.tar.gz#egg=en_core_web_sm==2.2.5 in /usr/local/lib/python3.6/dist-packages (2.2.5)\n",
            "Requirement already satisfied: spacy>=2.2.2 in /usr/local/lib/python3.6/dist-packages (from en_core_web_sm==2.2.5) (2.2.4)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.8.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (50.3.2)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.23.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (4.41.1)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.5)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.5)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.1.3)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (0.4.1)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (2.0.5)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (7.4.0)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.0.0)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.2.2->en_core_web_sm==2.2.5) (1.19.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.2.2->en_core_web_sm==2.2.5) (2.10)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.3.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.2.2->en_core_web_sm==2.2.5) (3.4.0)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_sm')\n",
            "Requirement already satisfied: pytextrank in /usr/local/lib/python3.6/dist-packages (2.0.3)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.6/dist-packages (from pytextrank) (0.15)\n",
            "Requirement already satisfied: spacy in /usr/local/lib/python3.6/dist-packages (from pytextrank) (2.2.4)\n",
            "Requirement already satisfied: coverage in /usr/local/lib/python3.6/dist-packages (from pytextrank) (5.3.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.6/dist-packages (from pytextrank) (2.5)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy->pytextrank) (3.0.5)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy->pytextrank) (7.4.0)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy->pytextrank) (1.19.4)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy->pytextrank) (1.0.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy->pytextrank) (50.3.2)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy->pytextrank) (2.23.0)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy->pytextrank) (1.0.5)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy->pytextrank) (0.4.1)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy->pytextrank) (2.0.5)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy->pytextrank) (1.0.5)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy->pytextrank) (0.8.0)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy->pytextrank) (1.1.3)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy->pytextrank) (4.41.1)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx->pytextrank) (4.4.2)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy->pytextrank) (3.3.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy->pytextrank) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy->pytextrank) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy->pytextrank) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy->pytextrank) (2020.12.5)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy->pytextrank) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy->pytextrank) (3.4.0)\n",
            "Requirement already satisfied: coverage>=5.0.1 in /usr/local/lib/python3.6/dist-packages (from -r pytextrank/requirements.txt (line 1)) (5.3.1)\n",
            "Requirement already satisfied: graphviz>=0.13 in /usr/local/lib/python3.6/dist-packages (from -r pytextrank/requirements.txt (line 2)) (0.15)\n",
            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.6/dist-packages (from -r pytextrank/requirements.txt (line 3)) (2.5)\n",
            "Requirement already satisfied: spacy>=2.0 in /usr/local/lib/python3.6/dist-packages (from -r pytextrank/requirements.txt (line 4)) (2.2.4)\n",
            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=2.0->-r pytextrank/requirements.txt (line 3)) (4.4.2)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (4.41.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (50.3.2)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (3.0.5)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (1.0.0)\n",
            "Requirement already satisfied: blis<0.5.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (0.4.1)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (2.0.5)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (1.0.5)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (1.0.5)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (1.1.3)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (2.23.0)\n",
            "Requirement already satisfied: thinc==7.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (7.4.0)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (0.8.0)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (1.19.4)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (3.3.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (3.0.4)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (3.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.0->-r pytextrank/requirements.txt (line 4)) (3.7.4.3)\n",
            "Requirement already satisfied: rake-nltk in /usr/local/lib/python3.6/dist-packages (1.0.4)\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.6/dist-packages (from rake-nltk) (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from nltk->rake-nltk) (1.15.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4x4XYUdu_k9",
        "outputId": "6a943c0f-7b4b-4049-ecb7-3fed1a3c90ff"
      },
      "source": [
        "# From Pytextrank\r\n",
        "import spacy\r\n",
        "import pytextrank\r\n",
        "\r\n",
        "nlp = spacy.load(\"en_core_web_sm\")\r\n",
        "\r\n",
        "# add PyTextRank to the spaCy pipeline\r\n",
        "tr = pytextrank.TextRank()\r\n",
        "nlp.add_pipe(tr.PipelineComponent, name=\"textrank\", last=True)\r\n",
        "\r\n",
        "doc = nlp(text)\r\n",
        "for p in doc._.phrases[:count]:\r\n",
        "    print(\"{:.4f} {:5d}\".format(p.rank, p.count), '\\t', p.text, '\\t', p.chunks)"
      ],
      "execution_count": 317,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.0618     1 \t national education systems \t [national education systems]\n",
            "0.0584     3 \t public education \t [public education, public education, public education]\n",
            "0.0578    14 \t education \t [education, education, education, education, education, education, education, education, education, education, education, education, education, their education]\n",
            "0.0502     1 \t other things \t [other things]\n",
            "0.0489     1 \t most people \t [most people]\n",
            "0.0482    11 \t people \t [people, people, people, people, people, people, people, people, people, the people, the people]\n",
            "0.0473    10 \t things \t [things, things, things, those things, these things, the thing, three things, some things, all the things, all these things]\n",
            "0.0459     1 \t human capacity \t [human capacity]\n",
            "0.0457     1 \t more people \t [More people]\n",
            "0.0439     1 \t human creativity \t [human creativity]\n",
            "0.0422     1 \t human ecology \t [human ecology]\n",
            "0.0413     1 \t gillian lynne \t [Gillian Lynne]\n",
            "0.0405     1 \t different disciplinary ways \t [different disciplinary ways]\n",
            "0.0392     1 \t literal way \t [literal way]\n",
            "0.0384     4 \t every education system \t [every education system, an education system, Our education system, Our education system]\n",
            "0.0378     1 \t most kids \t [most kids]\n",
            "0.0373    10 \t gillian \t [Gillian, Gillian, Gillian, Gillian, Gillian, Gillian, Gillian, Gillian, Gillian, Gillian]\n",
            "0.0366     9 \t children \t [children, children, children, Children, children, all children, a child, our children, our children]\n",
            "0.0363     1 \t little kid \t [Little kid]\n",
            "0.0344     6 \t kids \t [kids, all kids, two kids, a kid, the kids, the kids]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fcczI9Zsu_nw",
        "outputId": "4e4dcae8-a5d6-4dac-e811-3697a1b1c9f7"
      },
      "source": [
        "# From Rake algorithm\r\n",
        "from rake_nltk import Rake\r\n",
        "r = Rake() \r\n",
        "\r\n",
        "r.extract_keywords_from_text(text)\r\n",
        "keywords_  =  r.get_ranked_phrases_with_scores() \r\n",
        "for  i  in keywords_[:count]:    print((i[1], i[0]))\r\n"
      ],
      "execution_count": 318,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('never find another girl like sarah .\"', 25.320261437908496)\n",
            "('girl hardly ever paid attention', 24.0)\n",
            "('successful musical theater productions', 16.0)\n",
            "('met andrew lloyd webber', 16.0)\n",
            "('must try harder .\"', 14.764705882352942)\n",
            "('probably steered benignly away', 13.5)\n",
            "('teaches dance every day', 13.35)\n",
            "('running national education systems', 13.055555555555555)\n",
            "('god looks like .\"', 11.986928104575163)\n",
            "('life would flourish .\"', 11.473039215686274)\n",
            "('pretty much every system', 11.15)\n",
            "('gillian lynne dance company', 11.133333333333333)\n",
            "('great story recently —', 10.166666666666668)\n",
            "('three themes running', 9.5)\n",
            "('somebodys english class', 9.0)\n",
            "('playing video games', 9.0)\n",
            "('many highly talented', 9.0)\n",
            "('five years ago', 9.0)\n",
            "('al gore spoke', 9.0)\n",
            "('stop speaking like', 8.88888888888889)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZVAaq7TVu_q_",
        "outputId": "021f803f-ee95-4fb3-be4c-e6d9ea8f30e6"
      },
      "source": [
        "# From Gensim\r\n",
        "from gensim.summarization import keywords\r\n",
        "keywords_  =  keywords(text, words=count, scores = True, lemmatize = True)\r\n",
        "for i in keywords_:    print(i)"
      ],
      "execution_count": 319,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('laughter', 0.5433658815516097)\n",
            "('liked', 0.15846528994305886)\n",
            "('people', 0.13360885327216984)\n",
            "('humanities', 0.13010272297310732)\n",
            "('said', 0.10975368128520507)\n",
            "('called', 0.10744302672319532)\n",
            "('universities', 0.10402322998542893)\n",
            "('educate', 0.1028536408924354)\n",
            "('story', 0.09781397833010785)\n",
            "('boys', 0.09608866702804954)\n",
            "('think', 0.09287791662948397)\n",
            "('actually', 0.09268466153303254)\n",
            "('years', 0.08878267411793946)\n",
            "('creative', 0.08779255248444212)\n",
            "('dance', 0.08724389349007834)\n",
            "('new', 0.08413318894477183)\n",
            "('academic', 0.0841331889447715)\n",
            "('things', 0.08235792674377976)\n",
            "('speaks', 0.0820615754286847)\n",
            "('gift', 0.07838479795840765)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XXdMbw07u_xj"
      },
      "source": [
        "# Clearly, Keyphrases from Pytextrank looks the best."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WvGHuTZXu_0a"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1SHDszD6Gl5l"
      },
      "source": [
        "# A Simple Recommendation Engine with TFIDF and Cosine Similarity"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-mnE5iWhroT0"
      },
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\r\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\r\n",
        "\r\n",
        "tf = TfidfVectorizer(analyzer='word', \r\n",
        "                     ngram_range=(1, 3), \r\n",
        "                     min_df=0, \r\n",
        "                     stop_words='english',\r\n",
        "                     max_features=30000)\r\n",
        "\r\n",
        "tfidf_matrix = tf.fit_transform(df['transcript__2'].fillna('') + \\\r\n",
        "                                df['talk__name'].fillna('') + \\\r\n",
        "                                df['speaker__name'].fillna('') + \\\r\n",
        "                                df['talk__description'].fillna('') + \\\r\n",
        "                                df['event'].fillna('') + \\\r\n",
        "                                df['speaker__why_listen'].fillna('') \r\n",
        "                                )\r\n",
        "\r\n",
        "cosine_similarities = cosine_similarity(tfidf_matrix)"
      ],
      "execution_count": 346,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0HEfirypKZmd",
        "outputId": "b57703a4-cf3e-4221-d14d-089a70d7516c"
      },
      "source": [
        "%%time\r\n",
        "top_k = 15    # Count of predictions we are making.\r\n",
        "\r\n",
        "indices = []    # Of most related talks.\r\n",
        "for  j  in range(len(df)):\r\n",
        "    c = cosine_similarities[j]\r\n",
        "    c2 = sorted(zip(c, range(len(c))), key = lambda x:-x[0])[1:top_k+1]\r\n",
        "    idx = [i[1]  for i in c2]\r\n",
        "    indices.append(idx)\r\n",
        "\r\n",
        "\r\n",
        "l = []\r\n",
        "for  i  in range(len(df)):\r\n",
        "    l.append([df.iloc[i]['talk__name'], \r\n",
        "              [df.iloc[j]['talk__name']  for  j  in indices[i]],\r\n",
        "              [j['title']  for j in ast.literal_eval(df.iloc[i]['related_talks'])]])\r\n",
        "    \r\n",
        "final  =  pd.DataFrame(l, columns = ['talk__name', 'recommened_by_RE', 'related_talks'])    # RE for Recommendation Engine\r\n",
        "\r\n",
        "\r\n",
        "# Creating  'match_ratio'  column.\r\n",
        "def jaccard(l1, l2):\r\n",
        "    if l2:\r\n",
        "        s1, s2  =  set(l1), set(l2)\r\n",
        "        return len(s1.intersection(s2))/len(s2)\r\n",
        "    else:    return -1\r\n",
        "final['match_ratio']  =  final.apply(lambda x:jaccard(x['recommened_by_RE'], x['related_talks']), axis=1)"
      ],
      "execution_count": 347,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 51 s, sys: 845 ms, total: 51.8 s\n",
            "Wall time: 50.6 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "18TKmdiKl_pk",
        "outputId": "45e4b124-5129-43fb-b1cf-1d3f193f6c80"
      },
      "source": [
        "final.head()"
      ],
      "execution_count": 348,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n      window.createDataTable({\n        data: [[{\n            'v': 0,\n            'f': \"0\",\n        },\n\"Do schools kill creativity?\",\n[\"Bring on the learning revolution!\", \"How to escape education's death valley\", \"Sir Ken Robinson (still) wants an education revolution\\t\", \"Nerdcore comedy\", \"A one-man world summit\", \"How to run a company with (almost) no rules\", \"Every kid needs a champion\", \"Design and discovery\", \"Our failing schools. Enough is enough!\", \"Build a School in the Cloud\", \"How I stopped the Taliban from shutting down my school\", \"My 5 lives as an artist\", \"Rebuilding a neighborhood with beauty, dignity, hope\", \"Letting go of God\", \"Kids need structure\"],\n[\"Sir Ken Robinson (still) wants an education revolution\\t\", \"Bring on the learning revolution!\", \"How to escape education's death valley\", \"How to run a company with (almost) no rules\", \"The search for \\\"aha!\\\" moments\", \"Why some of us don't have one true calling\"],\n{\n            'v': 0.6666666666666666,\n            'f': \"0.6666666666666666\",\n        }],\n [{\n            'v': 1,\n            'f': \"1\",\n        },\n\"This is what happens when you reply to spam email\",\n[\"The agony of trying to unsubscribe\", \"Nerdcore comedy\", \"A one-man world summit\", \"Letting go of God\", \"All things are Moleeds\", \"Love, no matter what\", \"Every kid needs a champion\", \"Depression, the secret we share\", \"Bring on the learning revolution!\", \"A comic sendup of TED2006\", \"Strange answers to the psychopath test\", \"How the worst moments in our lives make us who we are\", \"Do schools kill creativity?\", \"Simplicity sells\", \"10 things you didn't know about orgasm\"],\n[\"Why we laugh\", \"If I controlled the Internet\", \"The agony of trying to unsubscribe\", \"How to stop screwing yourself over\"],\n{\n            'v': 0.25,\n            'f': \"0.25\",\n        }],\n [{\n            'v': 2,\n            'f': \"2\",\n        },\n\"Your body language may shape who you are\",\n[\"How to understand power\", \"How to stop screwing yourself over\", \"How to speak up for yourself\", \"It's OK to feel overwhelmed. Here's what to do next\", \"Play! Experiment! Discover!\", \"What does the future hold? 11 characters offer quirky answers\", \"How to run a company with (almost) no rules\", \"How you can use impostor syndrome to your benefit\", \"The genesis of Google\", \"A one-woman global village\", \"How to find work you love\", \"Global power shifts\", \"Why ordinary people need to understand power\", \"One woman, five characters, and a sex lesson from the future\", \"You are not alone in your loneliness\"],\n[\"The power of vulnerability\", \"How to let go of being a \\\"good\\\" person -- and become a better person\", \"The gift and power of emotional courage\", \"How to find work you love\", \"It's OK to feel overwhelmed. Here's what to do next\", \"How to speak up for yourself\"],\n{\n            'v': 0.5,\n            'f': \"0.5\",\n        }],\n [{\n            'v': 3,\n            'f': \"3\",\n        },\n\"How great leaders inspire action\",\n[\"Why good leaders make you feel safe\", \"How to get your ideas to spread\", \"How to find work you love\", \"Why we make bad decisions\", \"Fifty shades of gay\", \"The path to ending systemic racism in the US\", \"How brain science will change computing\", \"How to build a company where the best ideas win\", \"The tribes we lead\", \"How to buy happiness\", \"Reflections from a lifetime fighting to end child poverty\", \"How to run a company with (almost) no rules\", \"The web is more than \\\"better TV\\\"\", \"What are you willing to give up to change the way we work?\", \"The lie that invented racism\"],\n[\"Why good leaders make you feel safe\", \"3 questions to ask yourself about everything you do\", \"Why the secret to success is setting the right goals\", \"How to pitch to a VC\", \"Confessions of a recovering micromanager\", \"How we need to remake the internet\"],\n{\n            'v': 0.16666666666666666,\n            'f': \"0.16666666666666666\",\n        }],\n [{\n            'v': 4,\n            'f': \"4\",\n        },\n\"The power of vulnerability\",\n[\"Listening to shame\", \"It's OK to feel overwhelmed. Here's what to do next\", \"How changing your story can change your life\", \"Why children stay silent following sexual violence\", \"Play! Experiment! Discover!\", \"How to run a company with (almost) no rules\", \"A one-woman global village\", \"Connected, but alone?\", \"Design and discovery\", \"Why I'm done trying to be \\\"man enough\\\"\", \"Our failing schools. Enough is enough!\", \"What does the future hold? 11 characters offer quirky answers\", \"Nerdcore comedy\", \"One woman, five characters, and a sex lesson from the future\", \"How to find work you love\"],\n[\"Listening to shame\", \"Gaming to re-engage boys in learning\", \"Our loss of wisdom\", \"It's OK to feel overwhelmed. Here's what to do next\", \"The lies our culture tells us about what matters -- and a better way to live\", \"How to turn climate anxiety into action\"],\n{\n            'v': 0.3333333333333333,\n            'f': \"0.3333333333333333\",\n        }]],\n        columns: [[\"number\", \"index\"], [\"string\", \"talk__name\"], [\"string\", \"recommened_by_RE\"], [\"string\", \"related_talks\"], [\"number\", \"match_ratio\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
            "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>talk__name</th>\n",
              "      <th>recommened_by_RE</th>\n",
              "      <th>related_talks</th>\n",
              "      <th>match_ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Do schools kill creativity?</td>\n",
              "      <td>[Bring on the learning revolution!, How to escape education's death valley, Sir Ken Robinson (still) wants an education revolution\\t, Nerdcore comedy, A one-man world summit, How to run a company with (almost) no rules, Every kid needs a champion, Design and discovery, Our failing schools. Enoug...</td>\n",
              "      <td>[Sir Ken Robinson (still) wants an education revolution\\t, Bring on the learning revolution!, How to escape education's death valley, How to run a company with (almost) no rules, The search for \"aha!\" moments, Why some of us don't have one true calling]</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>This is what happens when you reply to spam email</td>\n",
              "      <td>[The agony of trying to unsubscribe, Nerdcore comedy, A one-man world summit, Letting go of God, All things are Moleeds, Love, no matter what, Every kid needs a champion, Depression, the secret we share, Bring on the learning revolution!, A comic sendup of TED2006, Strange answers to the psychop...</td>\n",
              "      <td>[Why we laugh, If I controlled the Internet, The agony of trying to unsubscribe, How to stop screwing yourself over]</td>\n",
              "      <td>0.250000</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Your body language may shape who you are</td>\n",
              "      <td>[How to understand power, How to stop screwing yourself over, How to speak up for yourself, It's OK to feel overwhelmed. Here's what to do next, Play! Experiment! Discover!, What does the future hold? 11 characters offer quirky answers, How to run a company with (almost) no rules, How you can us...</td>\n",
              "      <td>[The power of vulnerability, How to let go of being a \"good\" person -- and become a better person, The gift and power of emotional courage, How to find work you love, It's OK to feel overwhelmed. Here's what to do next, How to speak up for yourself]</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>How great leaders inspire action</td>\n",
              "      <td>[Why good leaders make you feel safe, How to get your ideas to spread, How to find work you love, Why we make bad decisions, Fifty shades of gay, The path to ending systemic racism in the US, How brain science will change computing, How to build a company where the best ideas win, The tribes we ...</td>\n",
              "      <td>[Why good leaders make you feel safe, 3 questions to ask yourself about everything you do, Why the secret to success is setting the right goals, How to pitch to a VC, Confessions of a recovering micromanager, How we need to remake the internet]</td>\n",
              "      <td>0.166667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The power of vulnerability</td>\n",
              "      <td>[Listening to shame, It's OK to feel overwhelmed. Here's what to do next, How changing your story can change your life, Why children stay silent following sexual violence, Play! Experiment! Discover!, How to run a company with (almost) no rules, A one-woman global village, Connected, but alone?,...</td>\n",
              "      <td>[Listening to shame, Gaming to re-engage boys in learning, Our loss of wisdom, It's OK to feel overwhelmed. Here's what to do next, The lies our culture tells us about what matters -- and a better way to live, How to turn climate anxiety into action]</td>\n",
              "      <td>0.333333</td>\n",
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            ],
            "text/plain": [
              "                                          talk__name                                                                                                                                                                                                                                                                                             recommened_by_RE                                                                                                                                                                                                                                                  related_talks  match_ratio\n",
              "0                        Do schools kill creativity?  [Bring on the learning revolution!, How to escape education's death valley, Sir Ken Robinson (still) wants an education revolution\\t, Nerdcore comedy, A one-man world summit, How to run a company with (almost) no rules, Every kid needs a champion, Design and discovery, Our failing schools. Enoug...  [Sir Ken Robinson (still) wants an education revolution\\t, Bring on the learning revolution!, How to escape education's death valley, How to run a company with (almost) no rules, The search for \"aha!\" moments, Why some of us don't have one true calling]     0.666667\n",
              "1  This is what happens when you reply to spam email  [The agony of trying to unsubscribe, Nerdcore comedy, A one-man world summit, Letting go of God, All things are Moleeds, Love, no matter what, Every kid needs a champion, Depression, the secret we share, Bring on the learning revolution!, A comic sendup of TED2006, Strange answers to the psychop...                                                                                                                                           [Why we laugh, If I controlled the Internet, The agony of trying to unsubscribe, How to stop screwing yourself over]     0.250000\n",
              "2           Your body language may shape who you are  [How to understand power, How to stop screwing yourself over, How to speak up for yourself, It's OK to feel overwhelmed. Here's what to do next, Play! Experiment! Discover!, What does the future hold? 11 characters offer quirky answers, How to run a company with (almost) no rules, How you can us...      [The power of vulnerability, How to let go of being a \"good\" person -- and become a better person, The gift and power of emotional courage, How to find work you love, It's OK to feel overwhelmed. Here's what to do next, How to speak up for yourself]     0.500000\n",
              "3                   How great leaders inspire action  [Why good leaders make you feel safe, How to get your ideas to spread, How to find work you love, Why we make bad decisions, Fifty shades of gay, The path to ending systemic racism in the US, How brain science will change computing, How to build a company where the best ideas win, The tribes we ...           [Why good leaders make you feel safe, 3 questions to ask yourself about everything you do, Why the secret to success is setting the right goals, How to pitch to a VC, Confessions of a recovering micromanager, How we need to remake the internet]     0.166667\n",
              "4                         The power of vulnerability  [Listening to shame, It's OK to feel overwhelmed. Here's what to do next, How changing your story can change your life, Why children stay silent following sexual violence, Play! Experiment! Discover!, How to run a company with (almost) no rules, A one-woman global village, Connected, but alone?,...     [Listening to shame, Gaming to re-engage boys in learning, Our loss of wisdom, It's OK to feel overwhelmed. Here's what to do next, The lies our culture tells us about what matters -- and a better way to live, How to turn climate anxiety into action]     0.333333"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 348
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5hXSdz-Yrn9g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a5cffa6-7001-4d54-e96e-a84597404e1c"
      },
      "source": [
        "(final.match_ratio==-1).sum(), final.match_ratio.mean()"
      ],
      "execution_count": 349,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 0.48644094001236887)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 349
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "604l5G6XzVoa"
      },
      "source": [
        "# Conclusion:  Only with this simple code, we could correctly recommend almost half of the 'related_talks' on average."
      ],
      "execution_count": 350,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7AVLWEeuzVrF"
      },
      "source": [
        ""
      ],
      "execution_count": 322,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G84Phb9TzVuO"
      },
      "source": [
        ""
      ],
      "execution_count": 351,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k7iJC3bsDcdZ"
      },
      "source": [
        "# Topic Modelling of 'transcript__5'"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGXIjwb_rn_6"
      },
      "source": [
        "# Source:  https://github.com/susanli2016/NLP-with-Python/blob/master/Topic%20Modeling%20for%20Data%20Preprocessing.ipynb\r\n",
        "vectorizer = CountVectorizer(analyzer='word',       \r\n",
        "                             min_df=3,                         # minimum reqd occurences of a word \r\n",
        "                             stop_words='english',             # remove stop words\r\n",
        "                             lowercase=True,                   # convert all words to lowercase\r\n",
        "                             token_pattern='[a-zA-Z0-9]{3,}',  # num chars > 3\r\n",
        "                             max_features=30000,                # max number of uniq words\r\n",
        "                            )\r\n",
        "\r\n",
        "data_vectorized = vectorizer.fit_transform(df['transcript__5'])"
      ],
      "execution_count": 374,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NP3P0G7froCv"
      },
      "source": [
        "from sklearn.decomposition import LatentDirichletAllocation\r\n",
        "\r\n",
        "lda_model = LatentDirichletAllocation(n_components=8,           # Number of topics\r\n",
        "                                      learning_method='online',\r\n",
        "                                      random_state=0,       \r\n",
        "                                      n_jobs = -1                \r\n",
        "                                     )\r\n",
        "\r\n",
        "lda_output = lda_model.fit_transform(data_vectorized)"
      ],
      "execution_count": 377,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9Gz5OfXhroGL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 257
        },
        "outputId": "7e9cbd19-3b36-4a92-8303-fc3613bc1e0f"
      },
      "source": [
        "# Show top k keywords for each topic\r\n",
        "def show_topics(vectorizer=vectorizer, lda_model=lda_model, n_words=20):\r\n",
        "    keywords = np.array(vectorizer.get_feature_names())\r\n",
        "    topic_keywords = []\r\n",
        "    for topic_weights in lda_model.components_:\r\n",
        "        top_keyword_locs = (-topic_weights).argsort()[:n_words]\r\n",
        "        topic_keywords.append(keywords.take(top_keyword_locs))\r\n",
        "    return topic_keywords\r\n",
        "\r\n",
        "topic_keywords = show_topics(vectorizer=vectorizer, lda_model=lda_model, n_words=20)        \r\n",
        "\r\n",
        "# Topic - Keywords Dataframe\r\n",
        "df_topic_keywords = pd.DataFrame(topic_keywords)\r\n",
        "df_topic_keywords.columns = ['Word '+str(i) for i in range(df_topic_keywords.shape[1])]\r\n",
        "df_topic_keywords.index = ['Topic '+str(i) for i in range(df_topic_keywords.shape[0])]\r\n",
        "df_topic_keywords"
      ],
      "execution_count": 378,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.module+javascript": "\n      import \"https://ssl.gstatic.com/colaboratory/data_table/a6224c040fa35dcf/data_table.js\";\n\n      window.createDataTable({\n        data: [[\"Topic 0\",\n\"brain\",\n\"sleep\",\n\"laughter\",\n\"percent\",\n\"mental\",\n\"stress\",\n\"emotion\",\n\"gene\",\n\"normal\",\n\"medicine\",\n\"self\",\n\"healthy\",\n\"disorder\",\n\"surgery\",\n\"depression\",\n\"emotional\",\n\"tumor\",\n\"symptom\",\n\"illness\",\n\"therapy\"],\n [\"Topic 1\",\n\"percent\",\n\"dollar\",\n\"datum\",\n\"global\",\n\"income\",\n\"access\",\n\"united\",\n\"china\",\n\"innovation\",\n\"applause\",\n\"poverty\",\n\"solution\",\n\"invest\",\n\"solve\",\n\"crisis\",\n\"trust\",\n\"average\",\n\"sector\",\n\"tool\",\n\"wealth\"],\n [\"Topic 2\",\n\"climate\",\n\"percent\",\n\"carbon\",\n\"global\",\n\"virus\",\n\"planet\",\n\"vaccine\",\n\"fuel\",\n\"forest\",\n\"earth\",\n\"waste\",\n\"farmer\",\n\"clean\",\n\"solution\",\n\"nuclear\",\n\"solar\",\n\"plastic\",\n\"emission\",\n\"scale\",\n\"farm\"],\n [\"Topic 3\",\n\"earth\",\n\"datum\",\n\"planet\",\n\"universe\",\n\"object\",\n\"sun\",\n\"map\",\n\"galaxy\",\n\"particle\",\n\"wave\",\n\"speed\",\n\"moon\",\n\"physics\",\n\"hole\",\n\"intelligence\",\n\"quantum\",\n\"laughter\",\n\"algorithm\",\n\"sky\",\n\"scale\"],\n [\"Topic 4\",\n\"applause\",\n\"god\",\n\"conversation\",\n\"laughter\",\n\"united\",\n\"justice\",\n\"bear\",\n\"gender\",\n\"prison\",\n\"identity\",\n\"medium\",\n\"religion\",\n\"freedom\",\n\"conflict\",\n\"compassion\",\n\"refugee\",\n\"self\",\n\"struggle\",\n\"democracy\",\n\"hate\"],\n [\"Topic 5\",\n\"ocean\",\n\"specie\",\n\"male\",\n\"bird\",\n\"female\",\n\"shark\",\n\"coral\",\n\"creature\",\n\"whale\",\n\"ant\",\n\"dinosaur\",\n\"egg\",\n\"reef\",\n\"elephant\",\n\"marine\",\n\"boat\",\n\"swim\",\n\"ship\",\n\"ancestor\",\n\"wild\"],\n [\"Topic 6\",\n\"laughter\",\n\"applause\",\n\"video\",\n\"amazing\",\n\"york\",\n\"ted\",\n\"connect\",\n\"basically\",\n\"fun\",\n\"completely\",\n\"wonderful\",\n\"creative\",\n\"dance\",\n\"photo\",\n\"paint\",\n\"favorite\",\n\"perfect\",\n\"architecture\",\n\"train\",\n\"interested\"],\n [\"Topic 7\",\n\"robot\",\n\"brain\",\n\"device\",\n\"molecule\",\n\"protein\",\n\"lab\",\n\"neuron\",\n\"tissue\",\n\"bacteria\",\n\"applause\",\n\"shape\",\n\"signal\",\n\"chemical\",\n\"smell\",\n\"muscle\",\n\"laughter\",\n\"bee\",\n\"sugar\",\n\"biology\",\n\"organ\"]],\n        columns: [[\"string\", \"index\"], [\"string\", \"Word 0\"], [\"string\", \"Word 1\"], [\"string\", \"Word 2\"], [\"string\", \"Word 3\"], [\"string\", \"Word 4\"], [\"string\", \"Word 5\"], [\"string\", \"Word 6\"], [\"string\", \"Word 7\"], [\"string\", \"Word 8\"], [\"string\", \"Word 9\"], [\"string\", \"Word 10\"], [\"string\", \"Word 11\"], [\"string\", \"Word 12\"], [\"string\", \"Word 13\"], [\"string\", \"Word 14\"], [\"string\", \"Word 15\"], [\"string\", \"Word 16\"], [\"string\", \"Word 17\"], [\"string\", \"Word 18\"], [\"string\", \"Word 19\"]],\n        columnOptions: [{\"width\": \"1px\", \"className\": \"index_column\"}],\n        rowsPerPage: 25,\n        helpUrl: \"https://colab.research.google.com/notebooks/data_table.ipynb\",\n        suppressOutputScrolling: true,\n        minimumWidth: undefined,\n      });\n    ",
            "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>Word 0</th>\n",
              "      <th>Word 1</th>\n",
              "      <th>Word 2</th>\n",
              "      <th>Word 3</th>\n",
              "      <th>Word 4</th>\n",
              "      <th>Word 5</th>\n",
              "      <th>Word 6</th>\n",
              "      <th>Word 7</th>\n",
              "      <th>Word 8</th>\n",
              "      <th>Word 9</th>\n",
              "      <th>Word 10</th>\n",
              "      <th>Word 11</th>\n",
              "      <th>Word 12</th>\n",
              "      <th>Word 13</th>\n",
              "      <th>Word 14</th>\n",
              "      <th>Word 15</th>\n",
              "      <th>Word 16</th>\n",
              "      <th>Word 17</th>\n",
              "      <th>Word 18</th>\n",
              "      <th>Word 19</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Topic 0</th>\n",
              "      <td>brain</td>\n",
              "      <td>sleep</td>\n",
              "      <td>laughter</td>\n",
              "      <td>percent</td>\n",
              "      <td>mental</td>\n",
              "      <td>stress</td>\n",
              "      <td>emotion</td>\n",
              "      <td>gene</td>\n",
              "      <td>normal</td>\n",
              "      <td>medicine</td>\n",
              "      <td>self</td>\n",
              "      <td>healthy</td>\n",
              "      <td>disorder</td>\n",
              "      <td>surgery</td>\n",
              "      <td>depression</td>\n",
              "      <td>emotional</td>\n",
              "      <td>tumor</td>\n",
              "      <td>symptom</td>\n",
              "      <td>illness</td>\n",
              "      <td>therapy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 1</th>\n",
              "      <td>percent</td>\n",
              "      <td>dollar</td>\n",
              "      <td>datum</td>\n",
              "      <td>global</td>\n",
              "      <td>income</td>\n",
              "      <td>access</td>\n",
              "      <td>united</td>\n",
              "      <td>china</td>\n",
              "      <td>innovation</td>\n",
              "      <td>applause</td>\n",
              "      <td>poverty</td>\n",
              "      <td>solution</td>\n",
              "      <td>invest</td>\n",
              "      <td>solve</td>\n",
              "      <td>crisis</td>\n",
              "      <td>trust</td>\n",
              "      <td>average</td>\n",
              "      <td>sector</td>\n",
              "      <td>tool</td>\n",
              "      <td>wealth</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 2</th>\n",
              "      <td>climate</td>\n",
              "      <td>percent</td>\n",
              "      <td>carbon</td>\n",
              "      <td>global</td>\n",
              "      <td>virus</td>\n",
              "      <td>planet</td>\n",
              "      <td>vaccine</td>\n",
              "      <td>fuel</td>\n",
              "      <td>forest</td>\n",
              "      <td>earth</td>\n",
              "      <td>waste</td>\n",
              "      <td>farmer</td>\n",
              "      <td>clean</td>\n",
              "      <td>solution</td>\n",
              "      <td>nuclear</td>\n",
              "      <td>solar</td>\n",
              "      <td>plastic</td>\n",
              "      <td>emission</td>\n",
              "      <td>scale</td>\n",
              "      <td>farm</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 3</th>\n",
              "      <td>earth</td>\n",
              "      <td>datum</td>\n",
              "      <td>planet</td>\n",
              "      <td>universe</td>\n",
              "      <td>object</td>\n",
              "      <td>sun</td>\n",
              "      <td>map</td>\n",
              "      <td>galaxy</td>\n",
              "      <td>particle</td>\n",
              "      <td>wave</td>\n",
              "      <td>speed</td>\n",
              "      <td>moon</td>\n",
              "      <td>physics</td>\n",
              "      <td>hole</td>\n",
              "      <td>intelligence</td>\n",
              "      <td>quantum</td>\n",
              "      <td>laughter</td>\n",
              "      <td>algorithm</td>\n",
              "      <td>sky</td>\n",
              "      <td>scale</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 4</th>\n",
              "      <td>applause</td>\n",
              "      <td>god</td>\n",
              "      <td>conversation</td>\n",
              "      <td>laughter</td>\n",
              "      <td>united</td>\n",
              "      <td>justice</td>\n",
              "      <td>bear</td>\n",
              "      <td>gender</td>\n",
              "      <td>prison</td>\n",
              "      <td>identity</td>\n",
              "      <td>medium</td>\n",
              "      <td>religion</td>\n",
              "      <td>freedom</td>\n",
              "      <td>conflict</td>\n",
              "      <td>compassion</td>\n",
              "      <td>refugee</td>\n",
              "      <td>self</td>\n",
              "      <td>struggle</td>\n",
              "      <td>democracy</td>\n",
              "      <td>hate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 5</th>\n",
              "      <td>ocean</td>\n",
              "      <td>specie</td>\n",
              "      <td>male</td>\n",
              "      <td>bird</td>\n",
              "      <td>female</td>\n",
              "      <td>shark</td>\n",
              "      <td>coral</td>\n",
              "      <td>creature</td>\n",
              "      <td>whale</td>\n",
              "      <td>ant</td>\n",
              "      <td>dinosaur</td>\n",
              "      <td>egg</td>\n",
              "      <td>reef</td>\n",
              "      <td>elephant</td>\n",
              "      <td>marine</td>\n",
              "      <td>boat</td>\n",
              "      <td>swim</td>\n",
              "      <td>ship</td>\n",
              "      <td>ancestor</td>\n",
              "      <td>wild</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 6</th>\n",
              "      <td>laughter</td>\n",
              "      <td>applause</td>\n",
              "      <td>video</td>\n",
              "      <td>amazing</td>\n",
              "      <td>york</td>\n",
              "      <td>ted</td>\n",
              "      <td>connect</td>\n",
              "      <td>basically</td>\n",
              "      <td>fun</td>\n",
              "      <td>completely</td>\n",
              "      <td>wonderful</td>\n",
              "      <td>creative</td>\n",
              "      <td>dance</td>\n",
              "      <td>photo</td>\n",
              "      <td>paint</td>\n",
              "      <td>favorite</td>\n",
              "      <td>perfect</td>\n",
              "      <td>architecture</td>\n",
              "      <td>train</td>\n",
              "      <td>interested</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Topic 7</th>\n",
              "      <td>robot</td>\n",
              "      <td>brain</td>\n",
              "      <td>device</td>\n",
              "      <td>molecule</td>\n",
              "      <td>protein</td>\n",
              "      <td>lab</td>\n",
              "      <td>neuron</td>\n",
              "      <td>tissue</td>\n",
              "      <td>bacteria</td>\n",
              "      <td>applause</td>\n",
              "      <td>shape</td>\n",
              "      <td>signal</td>\n",
              "      <td>chemical</td>\n",
              "      <td>smell</td>\n",
              "      <td>muscle</td>\n",
              "      <td>laughter</td>\n",
              "      <td>bee</td>\n",
              "      <td>sugar</td>\n",
              "      <td>biology</td>\n",
              "      <td>organ</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Word 0    Word 1        Word 2    Word 3   Word 4   Word 5   Word 6     Word 7      Word 8      Word 9    Word 10   Word 11   Word 12   Word 13       Word 14    Word 15   Word 16       Word 17    Word 18     Word 19\n",
              "Topic 0     brain     sleep      laughter   percent   mental   stress  emotion       gene      normal    medicine       self   healthy  disorder   surgery    depression  emotional     tumor       symptom    illness     therapy\n",
              "Topic 1   percent    dollar         datum    global   income   access   united      china  innovation    applause    poverty  solution    invest     solve        crisis      trust   average        sector       tool      wealth\n",
              "Topic 2   climate   percent        carbon    global    virus   planet  vaccine       fuel      forest       earth      waste    farmer     clean  solution       nuclear      solar   plastic      emission      scale        farm\n",
              "Topic 3     earth     datum        planet  universe   object      sun      map     galaxy    particle        wave      speed      moon   physics      hole  intelligence    quantum  laughter     algorithm        sky       scale\n",
              "Topic 4  applause       god  conversation  laughter   united  justice     bear     gender      prison    identity     medium  religion   freedom  conflict    compassion    refugee      self      struggle  democracy        hate\n",
              "Topic 5     ocean    specie          male      bird   female    shark    coral   creature       whale         ant   dinosaur       egg      reef  elephant        marine       boat      swim          ship   ancestor        wild\n",
              "Topic 6  laughter  applause         video   amazing     york      ted  connect  basically         fun  completely  wonderful  creative     dance     photo         paint   favorite   perfect  architecture      train  interested\n",
              "Topic 7     robot     brain        device  molecule  protein      lab   neuron     tissue    bacteria    applause      shape    signal  chemical     smell        muscle   laughter       bee         sugar    biology       organ"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 378
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMcG5OvttuNZ"
      },
      "source": [
        "# TODO:  Currently, the topics are not clear, and terms/topics are overlapping. Do more preprocessing, and find the optimal number of topics. Then, use Topic Model features for Clustering."
      ],
      "execution_count": 379,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvuWVhfnpeNB"
      },
      "source": [
        ""
      ],
      "execution_count": 370,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDwb9B0qu_l8"
      },
      "source": [
        ""
      ],
      "execution_count": 350,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jGlO-udarocQ"
      },
      "source": [
        ""
      ],
      "execution_count": 350,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tPuPRLzUronT"
      },
      "source": [
        ""
      ],
      "execution_count": 350,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_TGYbMy69SrS"
      },
      "source": [
        ""
      ],
      "execution_count": 350,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmkvlzLTnJJD"
      },
      "source": [
        ""
      ],
      "execution_count": 350,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuV_S7oQokbj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DXwPEeLRor1o"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ry7MUKHutPck"
      },
      "source": [
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