ndp-songs-lyrics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NDP Songs Lyrics Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installation\n",
    "\n",
    "Before staring work, download packages.\n",
    "\n",
    "```shell\n",
    "pip install numpy scipy matplotlib ipython scikit-learn pandas wordcloud nltk gensim bokeh\n",
    "```\n",
    "\n",
    "Then, download nltk stopwords package.\n",
    "\n",
    "```shell\n",
    "> python\n",
    "$ import nltk\n",
    "$ nltk.download(‘stopwords’)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import preprocessing\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "import os\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading and inspecting data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\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>year</th>\n",
       "      <th>title</th>\n",
       "      <th>composer</th>\n",
       "      <th>writer</th>\n",
       "      <th>singer</th>\n",
       "      <th>lyrics</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1984</td>\n",
       "      <td>Stand Up for Singapore</td>\n",
       "      <td>Hugh Harrison</td>\n",
       "      <td>Hugh Harrison</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Stand up for Singapore, do the best you can\\nR...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>Count on Me, Singapore</td>\n",
       "      <td>Hugh Harrison</td>\n",
       "      <td>Hugh Harrison</td>\n",
       "      <td>Clement Chow</td>\n",
       "      <td>We have a vision for tomorrow, just believe, j...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1987</td>\n",
       "      <td>We are Singapore</td>\n",
       "      <td>Hugh Harrison</td>\n",
       "      <td>Hugh Harrison</td>\n",
       "      <td>Jonathan Tan Teck Meng, Roslinda Baharudin, Ro...</td>\n",
       "      <td>There was a time when people said\\nThat Singap...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990</td>\n",
       "      <td>One People, One Nation, One Singapore</td>\n",
       "      <td>Jeremy Monteiro</td>\n",
       "      <td>Jim Aitchison</td>\n",
       "      <td>NaN</td>\n",
       "      <td>We've built a nation with our hands\\n The toil...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998</td>\n",
       "      <td>Home</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>Kit Chan</td>\n",
       "      <td>Whenever I am feeling low\\n I look around me a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1999</td>\n",
       "      <td>Together</td>\n",
       "      <td>Ken Lim</td>\n",
       "      <td>Ken Lim</td>\n",
       "      <td>Evelyn Tan and Dreamz FM</td>\n",
       "      <td>Let us be together aspire to achieve\\n No matt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2000</td>\n",
       "      <td>Shine on Me</td>\n",
       "      <td>Jim Lim</td>\n",
       "      <td>Jim Lim</td>\n",
       "      <td>Mavis Hee and Jai Wahab</td>\n",
       "      <td>The moment when I was born\\n Into this world, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2001</td>\n",
       "      <td>Where I Belong</td>\n",
       "      <td>Ken Lim</td>\n",
       "      <td>Ken Lim</td>\n",
       "      <td>Tanya Chua</td>\n",
       "      <td>Morning comes around and I\\n Can't wait to see...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2002</td>\n",
       "      <td>We Will Get There</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>Stefanie Sun</td>\n",
       "      <td>Remember the days, we set out together with fa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2003</td>\n",
       "      <td>One United People</td>\n",
       "      <td>Joshua Wan</td>\n",
       "      <td>Joshua Wan</td>\n",
       "      <td>Stefanie Sun</td>\n",
       "      <td>One Singapore\\n One people strong and free\\n W...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2004</td>\n",
       "      <td>Home remix</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>Kit Chan and JJ Lin</td>\n",
       "      <td>Whenever I am feeling low\\n I look around me a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2005</td>\n",
       "      <td>Reach Out for the Skies</td>\n",
       "      <td>Elaine Chan</td>\n",
       "      <td>Selena Tan</td>\n",
       "      <td>Taufik Batisah and Rui En</td>\n",
       "      <td>At a time when hope was low\\n The journey seem...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2006</td>\n",
       "      <td>My Island Home</td>\n",
       "      <td>Joshua Wan</td>\n",
       "      <td>Joshua Wan</td>\n",
       "      <td>Kaira Gong</td>\n",
       "      <td>This is my home\\n She's everything to me\\n Gra...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2007</td>\n",
       "      <td>There’s No Place I’d Rather Be</td>\n",
       "      <td>Jimmy Ye</td>\n",
       "      <td>Jimmy Ye</td>\n",
       "      <td>Kit Chan</td>\n",
       "      <td>I've walked the streets of Cairo and Bombay\\n ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2007</td>\n",
       "      <td>Will you</td>\n",
       "      <td>Jimmy Ye</td>\n",
       "      <td>Jimmy Ye</td>\n",
       "      <td>Janani Sridhar, Asha Edmund, Emma Yong, Lily A...</td>\n",
       "      <td>Will you make this island\\n Amazing in all way...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2008</td>\n",
       "      <td>Shine for Singapore</td>\n",
       "      <td>Benny Wong</td>\n",
       "      <td>Benny Wong</td>\n",
       "      <td>Joi Chua and Hady Mirza</td>\n",
       "      <td>Have you seen a star,\\n One that guides you no...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2009</td>\n",
       "      <td>What Do You See?</td>\n",
       "      <td>Electrico</td>\n",
       "      <td>Electrico</td>\n",
       "      <td>Electrico</td>\n",
       "      <td>There's a jewel on the ocean,\\n a gem upon the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2010</td>\n",
       "      <td>Song for Singapore</td>\n",
       "      <td>Corrinne May</td>\n",
       "      <td>Corrinne May</td>\n",
       "      <td>Corrinne May</td>\n",
       "      <td>It's a brand new day a brand new story\\n I rem...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2011</td>\n",
       "      <td>In a Heartbeat</td>\n",
       "      <td>Goh Kheng Long</td>\n",
       "      <td>Haresh Sharma</td>\n",
       "      <td>Sylvia Ratonel</td>\n",
       "      <td>I have a dream of starting a life \\n I have a ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012</td>\n",
       "      <td>Love at First Light</td>\n",
       "      <td>Iskandar Ismail</td>\n",
       "      <td>Paul Tan</td>\n",
       "      <td>Olivia Ong and Natanya Tan</td>\n",
       "      <td>First light\\n Rolls across my peaceful isle\\n ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013</td>\n",
       "      <td>One Singapore</td>\n",
       "      <td>Elaine Chan</td>\n",
       "      <td>Selena Tan</td>\n",
       "      <td>Sing A Nation choir</td>\n",
       "      <td>I will be here, right by your side here.\\nI wi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2015</td>\n",
       "      <td>Our Singapore</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>JJ Lin</td>\n",
       "      <td>It isn't easy building something out of nothin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016</td>\n",
       "      <td>Tomorrow's Here Today</td>\n",
       "      <td>Don Richmond</td>\n",
       "      <td>Don Richmond</td>\n",
       "      <td>53A</td>\n",
       "      <td>Raise your head to the skies\\n This is how we ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017</td>\n",
       "      <td>Because it's Singapore</td>\n",
       "      <td>Lee Wei Song</td>\n",
       "      <td>Jay Lim</td>\n",
       "      <td>Jay Lim</td>\n",
       "      <td>Step by step\\n Together we'll build our dreams...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018</td>\n",
       "      <td>We are Singapore remix</td>\n",
       "      <td>Charlie Lim</td>\n",
       "      <td>Charlie Lim</td>\n",
       "      <td>Featuring Vanessa Fernandez, Aisyah Aziz, Shak...</td>\n",
       "      <td>How many times have you heard them say;\\n The ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019</td>\n",
       "      <td>Our Singapore - remix of We Will Get There (20...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Remember the days, we set out together with fa...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year                                              title         composer  \\\n",
       "0   1984                             Stand Up for Singapore    Hugh Harrison   \n",
       "1   1986                             Count on Me, Singapore    Hugh Harrison   \n",
       "2   1987                                   We are Singapore    Hugh Harrison   \n",
       "3   1990              One People, One Nation, One Singapore  Jeremy Monteiro   \n",
       "4   1998                                               Home         Dick Lee   \n",
       "5   1999                                           Together          Ken Lim   \n",
       "6   2000                                        Shine on Me          Jim Lim   \n",
       "7   2001                                     Where I Belong          Ken Lim   \n",
       "8   2002                                  We Will Get There         Dick Lee   \n",
       "9   2003                                  One United People       Joshua Wan   \n",
       "10  2004                                         Home remix         Dick Lee   \n",
       "11  2005                            Reach Out for the Skies      Elaine Chan   \n",
       "12  2006                                     My Island Home       Joshua Wan   \n",
       "13  2007                     There’s No Place I’d Rather Be         Jimmy Ye   \n",
       "14  2007                                           Will you         Jimmy Ye   \n",
       "15  2008                                Shine for Singapore       Benny Wong   \n",
       "16  2009                                   What Do You See?        Electrico   \n",
       "17  2010                                 Song for Singapore     Corrinne May   \n",
       "18  2011                                     In a Heartbeat   Goh Kheng Long   \n",
       "19  2012                                Love at First Light  Iskandar Ismail   \n",
       "20  2013                                      One Singapore      Elaine Chan   \n",
       "21  2015                                      Our Singapore         Dick Lee   \n",
       "22  2016                              Tomorrow's Here Today     Don Richmond   \n",
       "23  2017                             Because it's Singapore     Lee Wei Song   \n",
       "24  2018                             We are Singapore remix      Charlie Lim   \n",
       "25  2019  Our Singapore - remix of We Will Get There (20...              NaN   \n",
       "\n",
       "           writer                                             singer  \\\n",
       "0   Hugh Harrison                                                NaN   \n",
       "1   Hugh Harrison                                       Clement Chow   \n",
       "2   Hugh Harrison  Jonathan Tan Teck Meng, Roslinda Baharudin, Ro...   \n",
       "3   Jim Aitchison                                                NaN   \n",
       "4        Dick Lee                                           Kit Chan   \n",
       "5         Ken Lim                           Evelyn Tan and Dreamz FM   \n",
       "6         Jim Lim                            Mavis Hee and Jai Wahab   \n",
       "7         Ken Lim                                         Tanya Chua   \n",
       "8        Dick Lee                                       Stefanie Sun   \n",
       "9      Joshua Wan                                       Stefanie Sun   \n",
       "10       Dick Lee                                Kit Chan and JJ Lin   \n",
       "11     Selena Tan                          Taufik Batisah and Rui En   \n",
       "12     Joshua Wan                                         Kaira Gong   \n",
       "13       Jimmy Ye                                           Kit Chan   \n",
       "14       Jimmy Ye  Janani Sridhar, Asha Edmund, Emma Yong, Lily A...   \n",
       "15     Benny Wong                            Joi Chua and Hady Mirza   \n",
       "16      Electrico                                          Electrico   \n",
       "17   Corrinne May                                       Corrinne May   \n",
       "18  Haresh Sharma                                     Sylvia Ratonel   \n",
       "19       Paul Tan                         Olivia Ong and Natanya Tan   \n",
       "20     Selena Tan                                Sing A Nation choir   \n",
       "21       Dick Lee                                             JJ Lin   \n",
       "22   Don Richmond                                                53A   \n",
       "23        Jay Lim                                            Jay Lim   \n",
       "24    Charlie Lim  Featuring Vanessa Fernandez, Aisyah Aziz, Shak...   \n",
       "25            NaN                                                NaN   \n",
       "\n",
       "                                               lyrics  \n",
       "0   Stand up for Singapore, do the best you can\\nR...  \n",
       "1   We have a vision for tomorrow, just believe, j...  \n",
       "2   There was a time when people said\\nThat Singap...  \n",
       "3   We've built a nation with our hands\\n The toil...  \n",
       "4   Whenever I am feeling low\\n I look around me a...  \n",
       "5   Let us be together aspire to achieve\\n No matt...  \n",
       "6   The moment when I was born\\n Into this world, ...  \n",
       "7   Morning comes around and I\\n Can't wait to see...  \n",
       "8   Remember the days, we set out together with fa...  \n",
       "9   One Singapore\\n One people strong and free\\n W...  \n",
       "10  Whenever I am feeling low\\n I look around me a...  \n",
       "11  At a time when hope was low\\n The journey seem...  \n",
       "12  This is my home\\n She's everything to me\\n Gra...  \n",
       "13  I've walked the streets of Cairo and Bombay\\n ...  \n",
       "14  Will you make this island\\n Amazing in all way...  \n",
       "15  Have you seen a star,\\n One that guides you no...  \n",
       "16  There's a jewel on the ocean,\\n a gem upon the...  \n",
       "17  It's a brand new day a brand new story\\n I rem...  \n",
       "18  I have a dream of starting a life \\n I have a ...  \n",
       "19  First light\\n Rolls across my peaceful isle\\n ...  \n",
       "20  I will be here, right by your side here.\\nI wi...  \n",
       "21  It isn't easy building something out of nothin...  \n",
       "22  Raise your head to the skies\\n This is how we ...  \n",
       "23  Step by step\\n Together we'll build our dreams...  \n",
       "24  How many times have you heard them say;\\n The ...  \n",
       "25  Remember the days, we set out together with fa...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"./songs.csv\")\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering\n",
    "#### Removing punctions & white spaces\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def formatted_text(text):\n",
    "    '''a function for removing punctuation & whitespace characters'''\n",
    "    import string\n",
    "    # replacing the punctuations with no space, \n",
    "    # which in effect deletes the punctuation marks \n",
    "    translator = str.maketrans('', '', string.punctuation)\n",
    "    # return the text stripped of punctuation marks\n",
    "    result = text.translate(translator)\n",
    "    \n",
    "    #result = result.replace(\"\\n\",\"\")\n",
    "    return result.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     stand up for singapore do the best you can\\nre...\n",
       "1     we have a vision for tomorrow just believe jus...\n",
       "2     there was a time when people said\\nthat singap...\n",
       "3     weve built a nation with our hands\\n the toil ...\n",
       "4     whenever i am feeling low\\n i look around me a...\n",
       "5     let us be together aspire to achieve\\n no matt...\n",
       "6     the moment when i was born\\n into this world i...\n",
       "7     morning comes around and i\\n cant wait to see ...\n",
       "8     remember the days we set out together with fai...\n",
       "9     one singapore\\n one people strong and free\\n w...\n",
       "10    whenever i am feeling low\\n i look around me a...\n",
       "11    at a time when hope was low\\n the journey seem...\n",
       "12    this is my home\\n shes everything to me\\n grac...\n",
       "13    ive walked the streets of cairo and bombay\\n i...\n",
       "14    will you make this island\\n amazing in all way...\n",
       "15    have you seen a star\\n one that guides you no ...\n",
       "16    theres a jewel on the ocean\\n a gem upon the s...\n",
       "17    its a brand new day a brand new story\\n i reme...\n",
       "18    i have a dream of starting a life \\n i have a ...\n",
       "19    first light\\n rolls across my peaceful isle\\n ...\n",
       "20    i will be here right by your side here\\ni will...\n",
       "21    it isnt easy building something out of nothing...\n",
       "22    raise your head to the skies\\n this is how we ...\n",
       "23    step by step\\n together well build our dreams\\...\n",
       "24    how many times have you heard them say\\n the f...\n",
       "25    remember the days we set out together with fai...\n",
       "Name: lyrics, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['lyrics'] = data['lyrics'].apply(formatted_text)\n",
    "data['lyrics']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Apply the function to each examples\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>title</th>\n",
       "      <th>composer</th>\n",
       "      <th>writer</th>\n",
       "      <th>singer</th>\n",
       "      <th>lyrics</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1984</td>\n",
       "      <td>Stand Up for Singapore</td>\n",
       "      <td>Hugh Harrison</td>\n",
       "      <td>Hugh Harrison</td>\n",
       "      <td>NaN</td>\n",
       "      <td>stand up for singapore do the best you can\\nre...</td>\n",
       "      <td>608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>Count on Me, Singapore</td>\n",
       "      <td>Hugh Harrison</td>\n",
       "      <td>Hugh Harrison</td>\n",
       "      <td>Clement Chow</td>\n",
       "      <td>we have a vision for tomorrow just believe jus...</td>\n",
       "      <td>846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1987</td>\n",
       "      <td>We are Singapore</td>\n",
       "      <td>Hugh Harrison</td>\n",
       "      <td>Hugh Harrison</td>\n",
       "      <td>Jonathan Tan Teck Meng, Roslinda Baharudin, Ro...</td>\n",
       "      <td>there was a time when people said\\nthat singap...</td>\n",
       "      <td>956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990</td>\n",
       "      <td>One People, One Nation, One Singapore</td>\n",
       "      <td>Jeremy Monteiro</td>\n",
       "      <td>Jim Aitchison</td>\n",
       "      <td>NaN</td>\n",
       "      <td>weve built a nation with our hands\\n the toil ...</td>\n",
       "      <td>548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998</td>\n",
       "      <td>Home</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>Kit Chan</td>\n",
       "      <td>whenever i am feeling low\\n i look around me a...</td>\n",
       "      <td>795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1999</td>\n",
       "      <td>Together</td>\n",
       "      <td>Ken Lim</td>\n",
       "      <td>Ken Lim</td>\n",
       "      <td>Evelyn Tan and Dreamz FM</td>\n",
       "      <td>let us be together aspire to achieve\\n no matt...</td>\n",
       "      <td>718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2000</td>\n",
       "      <td>Shine on Me</td>\n",
       "      <td>Jim Lim</td>\n",
       "      <td>Jim Lim</td>\n",
       "      <td>Mavis Hee and Jai Wahab</td>\n",
       "      <td>the moment when i was born\\n into this world i...</td>\n",
       "      <td>738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2001</td>\n",
       "      <td>Where I Belong</td>\n",
       "      <td>Ken Lim</td>\n",
       "      <td>Ken Lim</td>\n",
       "      <td>Tanya Chua</td>\n",
       "      <td>morning comes around and i\\n cant wait to see ...</td>\n",
       "      <td>936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2002</td>\n",
       "      <td>We Will Get There</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>Stefanie Sun</td>\n",
       "      <td>remember the days we set out together with fai...</td>\n",
       "      <td>1010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2003</td>\n",
       "      <td>One United People</td>\n",
       "      <td>Joshua Wan</td>\n",
       "      <td>Joshua Wan</td>\n",
       "      <td>Stefanie Sun</td>\n",
       "      <td>one singapore\\n one people strong and free\\n w...</td>\n",
       "      <td>1008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2004</td>\n",
       "      <td>Home remix</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>Kit Chan and JJ Lin</td>\n",
       "      <td>whenever i am feeling low\\n i look around me a...</td>\n",
       "      <td>1221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2005</td>\n",
       "      <td>Reach Out for the Skies</td>\n",
       "      <td>Elaine Chan</td>\n",
       "      <td>Selena Tan</td>\n",
       "      <td>Taufik Batisah and Rui En</td>\n",
       "      <td>at a time when hope was low\\n the journey seem...</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2006</td>\n",
       "      <td>My Island Home</td>\n",
       "      <td>Joshua Wan</td>\n",
       "      <td>Joshua Wan</td>\n",
       "      <td>Kaira Gong</td>\n",
       "      <td>this is my home\\n shes everything to me\\n grac...</td>\n",
       "      <td>834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2007</td>\n",
       "      <td>There’s No Place I’d Rather Be</td>\n",
       "      <td>Jimmy Ye</td>\n",
       "      <td>Jimmy Ye</td>\n",
       "      <td>Kit Chan</td>\n",
       "      <td>ive walked the streets of cairo and bombay\\n i...</td>\n",
       "      <td>653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2007</td>\n",
       "      <td>Will you</td>\n",
       "      <td>Jimmy Ye</td>\n",
       "      <td>Jimmy Ye</td>\n",
       "      <td>Janani Sridhar, Asha Edmund, Emma Yong, Lily A...</td>\n",
       "      <td>will you make this island\\n amazing in all way...</td>\n",
       "      <td>1075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2008</td>\n",
       "      <td>Shine for Singapore</td>\n",
       "      <td>Benny Wong</td>\n",
       "      <td>Benny Wong</td>\n",
       "      <td>Joi Chua and Hady Mirza</td>\n",
       "      <td>have you seen a star\\n one that guides you no ...</td>\n",
       "      <td>1170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2009</td>\n",
       "      <td>What Do You See?</td>\n",
       "      <td>Electrico</td>\n",
       "      <td>Electrico</td>\n",
       "      <td>Electrico</td>\n",
       "      <td>theres a jewel on the ocean\\n a gem upon the s...</td>\n",
       "      <td>1375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2010</td>\n",
       "      <td>Song for Singapore</td>\n",
       "      <td>Corrinne May</td>\n",
       "      <td>Corrinne May</td>\n",
       "      <td>Corrinne May</td>\n",
       "      <td>its a brand new day a brand new story\\n i reme...</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2011</td>\n",
       "      <td>In a Heartbeat</td>\n",
       "      <td>Goh Kheng Long</td>\n",
       "      <td>Haresh Sharma</td>\n",
       "      <td>Sylvia Ratonel</td>\n",
       "      <td>i have a dream of starting a life \\n i have a ...</td>\n",
       "      <td>982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012</td>\n",
       "      <td>Love at First Light</td>\n",
       "      <td>Iskandar Ismail</td>\n",
       "      <td>Paul Tan</td>\n",
       "      <td>Olivia Ong and Natanya Tan</td>\n",
       "      <td>first light\\n rolls across my peaceful isle\\n ...</td>\n",
       "      <td>902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013</td>\n",
       "      <td>One Singapore</td>\n",
       "      <td>Elaine Chan</td>\n",
       "      <td>Selena Tan</td>\n",
       "      <td>Sing A Nation choir</td>\n",
       "      <td>i will be here right by your side here\\ni will...</td>\n",
       "      <td>1706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2015</td>\n",
       "      <td>Our Singapore</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>Dick Lee</td>\n",
       "      <td>JJ Lin</td>\n",
       "      <td>it isnt easy building something out of nothing...</td>\n",
       "      <td>802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016</td>\n",
       "      <td>Tomorrow's Here Today</td>\n",
       "      <td>Don Richmond</td>\n",
       "      <td>Don Richmond</td>\n",
       "      <td>53A</td>\n",
       "      <td>raise your head to the skies\\n this is how we ...</td>\n",
       "      <td>877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017</td>\n",
       "      <td>Because it's Singapore</td>\n",
       "      <td>Lee Wei Song</td>\n",
       "      <td>Jay Lim</td>\n",
       "      <td>Jay Lim</td>\n",
       "      <td>step by step\\n together well build our dreams\\...</td>\n",
       "      <td>576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018</td>\n",
       "      <td>We are Singapore remix</td>\n",
       "      <td>Charlie Lim</td>\n",
       "      <td>Charlie Lim</td>\n",
       "      <td>Featuring Vanessa Fernandez, Aisyah Aziz, Shak...</td>\n",
       "      <td>how many times have you heard them say\\n the f...</td>\n",
       "      <td>1493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019</td>\n",
       "      <td>Our Singapore - remix of We Will Get There (20...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>remember the days we set out together with fai...</td>\n",
       "      <td>1447</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year                                              title         composer  \\\n",
       "0   1984                             Stand Up for Singapore    Hugh Harrison   \n",
       "1   1986                             Count on Me, Singapore    Hugh Harrison   \n",
       "2   1987                                   We are Singapore    Hugh Harrison   \n",
       "3   1990              One People, One Nation, One Singapore  Jeremy Monteiro   \n",
       "4   1998                                               Home         Dick Lee   \n",
       "5   1999                                           Together          Ken Lim   \n",
       "6   2000                                        Shine on Me          Jim Lim   \n",
       "7   2001                                     Where I Belong          Ken Lim   \n",
       "8   2002                                  We Will Get There         Dick Lee   \n",
       "9   2003                                  One United People       Joshua Wan   \n",
       "10  2004                                         Home remix         Dick Lee   \n",
       "11  2005                            Reach Out for the Skies      Elaine Chan   \n",
       "12  2006                                     My Island Home       Joshua Wan   \n",
       "13  2007                     There’s No Place I’d Rather Be         Jimmy Ye   \n",
       "14  2007                                           Will you         Jimmy Ye   \n",
       "15  2008                                Shine for Singapore       Benny Wong   \n",
       "16  2009                                   What Do You See?        Electrico   \n",
       "17  2010                                 Song for Singapore     Corrinne May   \n",
       "18  2011                                     In a Heartbeat   Goh Kheng Long   \n",
       "19  2012                                Love at First Light  Iskandar Ismail   \n",
       "20  2013                                      One Singapore      Elaine Chan   \n",
       "21  2015                                      Our Singapore         Dick Lee   \n",
       "22  2016                              Tomorrow's Here Today     Don Richmond   \n",
       "23  2017                             Because it's Singapore     Lee Wei Song   \n",
       "24  2018                             We are Singapore remix      Charlie Lim   \n",
       "25  2019  Our Singapore - remix of We Will Get There (20...              NaN   \n",
       "\n",
       "           writer                                             singer  \\\n",
       "0   Hugh Harrison                                                NaN   \n",
       "1   Hugh Harrison                                       Clement Chow   \n",
       "2   Hugh Harrison  Jonathan Tan Teck Meng, Roslinda Baharudin, Ro...   \n",
       "3   Jim Aitchison                                                NaN   \n",
       "4        Dick Lee                                           Kit Chan   \n",
       "5         Ken Lim                           Evelyn Tan and Dreamz FM   \n",
       "6         Jim Lim                            Mavis Hee and Jai Wahab   \n",
       "7         Ken Lim                                         Tanya Chua   \n",
       "8        Dick Lee                                       Stefanie Sun   \n",
       "9      Joshua Wan                                       Stefanie Sun   \n",
       "10       Dick Lee                                Kit Chan and JJ Lin   \n",
       "11     Selena Tan                          Taufik Batisah and Rui En   \n",
       "12     Joshua Wan                                         Kaira Gong   \n",
       "13       Jimmy Ye                                           Kit Chan   \n",
       "14       Jimmy Ye  Janani Sridhar, Asha Edmund, Emma Yong, Lily A...   \n",
       "15     Benny Wong                            Joi Chua and Hady Mirza   \n",
       "16      Electrico                                          Electrico   \n",
       "17   Corrinne May                                       Corrinne May   \n",
       "18  Haresh Sharma                                     Sylvia Ratonel   \n",
       "19       Paul Tan                         Olivia Ong and Natanya Tan   \n",
       "20     Selena Tan                                Sing A Nation choir   \n",
       "21       Dick Lee                                             JJ Lin   \n",
       "22   Don Richmond                                                53A   \n",
       "23        Jay Lim                                            Jay Lim   \n",
       "24    Charlie Lim  Featuring Vanessa Fernandez, Aisyah Aziz, Shak...   \n",
       "25            NaN                                                NaN   \n",
       "\n",
       "                                               lyrics  length  \n",
       "0   stand up for singapore do the best you can\\nre...     608  \n",
       "1   we have a vision for tomorrow just believe jus...     846  \n",
       "2   there was a time when people said\\nthat singap...     956  \n",
       "3   weve built a nation with our hands\\n the toil ...     548  \n",
       "4   whenever i am feeling low\\n i look around me a...     795  \n",
       "5   let us be together aspire to achieve\\n no matt...     718  \n",
       "6   the moment when i was born\\n into this world i...     738  \n",
       "7   morning comes around and i\\n cant wait to see ...     936  \n",
       "8   remember the days we set out together with fai...    1010  \n",
       "9   one singapore\\n one people strong and free\\n w...    1008  \n",
       "10  whenever i am feeling low\\n i look around me a...    1221  \n",
       "11  at a time when hope was low\\n the journey seem...     990  \n",
       "12  this is my home\\n shes everything to me\\n grac...     834  \n",
       "13  ive walked the streets of cairo and bombay\\n i...     653  \n",
       "14  will you make this island\\n amazing in all way...    1075  \n",
       "15  have you seen a star\\n one that guides you no ...    1170  \n",
       "16  theres a jewel on the ocean\\n a gem upon the s...    1375  \n",
       "17  its a brand new day a brand new story\\n i reme...    1200  \n",
       "18  i have a dream of starting a life \\n i have a ...     982  \n",
       "19  first light\\n rolls across my peaceful isle\\n ...     902  \n",
       "20  i will be here right by your side here\\ni will...    1706  \n",
       "21  it isnt easy building something out of nothing...     802  \n",
       "22  raise your head to the skies\\n this is how we ...     877  \n",
       "23  step by step\\n together well build our dreams\\...     576  \n",
       "24  how many times have you heard them say\\n the f...    1493  \n",
       "25  remember the days we set out together with fai...    1447  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def length(text):    \n",
    "    '''a function which returns the length of text'''\n",
    "    return len(str(text))\n",
    "data['length'] = data['lyrics'].apply(length)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histogram of text lenght of each song lyric\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 263,
       "width": 377
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)\n",
    "bins = 500\n",
    "\n",
    "plt.hist(list(data['length']), alpha = 1, label='Songs')\n",
    "\n",
    "plt.xlabel('length')\n",
    "plt.ylabel('numbers')\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "plt.legend(loc='upper right')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Removing Stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of stopwords:  179\n"
     ]
    }
   ],
   "source": [
    "# extracting the stopwords from nltk library\n",
    "sw = stopwords.words('english')\n",
    "# displaying the stopwords\n",
    "np.array(sw)\n",
    "\n",
    "print(\"Number of stopwords: \", len(sw))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stopwords(text):\n",
    "    '''a function for removing the stopword'''\n",
    "    # removing the stop words and lowercasing the selected words\n",
    "    text = [word.lower() for word in text.split() if word.lower() not in sw]\n",
    "    # joining the list of words with space separator\n",
    "    return \" \".join(text)\n",
    "\n",
    "data['lyrics'] = data['lyrics'].apply(stopwords)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stemming\n",
    "#### Collect vocabulary count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a count vectorizer object\n",
    "count_vectorizer = CountVectorizer()\n",
    "# fit the count vectorizer using the text data\n",
    "count_vectorizer.fit(data['lyrics'])\n",
    "# collect the vocabulary items used in the vectorizer\n",
    "dictionary = count_vectorizer.vocabulary_.items()  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lists to store the vocab and counts\n",
    "vocab = []\n",
    "count = []\n",
    "# iterate through each vocab and count append the value to designated lists\n",
    "for key, value in dictionary:\n",
    "    vocab.append(key)\n",
    "    count.append(value)\n",
    "# store the count in panadas dataframe with vocab as index\n",
    "vocab_bef_stem = pd.Series(count, index=vocab)\n",
    "\n",
    "# sort the dataframe\n",
    "vocab_bef_stem = vocab_bef_stem.sort_values(ascending=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "youve       595\n",
       "youre       594\n",
       "young       593\n",
       "youll       592\n",
       "you         591\n",
       "yo          590\n",
       "yet         589\n",
       "years       588\n",
       "yearning    587\n",
       "yeah        586\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_vacab = vocab_bef_stem.head(10)\n",
    "top_vacab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an object of stemming function\n",
    "stemmer = SnowballStemmer(\"english\")\n",
    "\n",
    "def stemming(text):    \n",
    "    '''a function which stems each word in the given text'''\n",
    "    text = [stemmer.stem(word) for word in text.split()]\n",
    "    return \" \".join(text) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['lyrics'] = data['lyrics'].apply(stemming)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "youve       595\n",
       "youre       594\n",
       "young       593\n",
       "youll       592\n",
       "you         591\n",
       "yo          590\n",
       "yet         589\n",
       "years       588\n",
       "yearning    587\n",
       "yeah        586\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_vacab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Counting\n",
    "### Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 130,
       "width": 385
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 790 ms, sys: 58.1 ms, total: 848 ms\n",
      "Wall time: 689 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "text = \" \".join(e for e in data['lyrics'])\n",
    "\n",
    "wordcloud = WordCloud(max_font_size=50, max_words=100, background_color=\"white\", width=700).generate(text)\n",
    "# Save image file \n",
    "wordcloud.to_file(\"./bag_of_words.png\")\n",
    "\n",
    "# Display the generated image:\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bag of Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total vacab is 596\n"
     ]
    }
   ],
   "source": [
    "# lists to store the vocab and counts\n",
    "vocab = []\n",
    "count = []\n",
    "# iterate through each vocab and count append the value to designated lists\n",
    "for key, value in dictionary:\n",
    "    vocab.append(key)\n",
    "    count.append(value)\n",
    "# store the count in panadas dataframe with vocab as index\n",
    "vocab_after_stem = pd.Series(count, index=vocab)\n",
    "\n",
    "# sort the dataframe\n",
    "vocab_after_stem = vocab_after_stem.sort_values(ascending=False)\n",
    "\n",
    "print(\"total vacab is\", len(vocab_after_stem))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature_names 504\n"
     ]
    },
    {
     "data": {
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       "<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",
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       "\n",
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       "        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>year</th>\n",
       "      <th>achiev</th>\n",
       "      <th>across</th>\n",
       "      <th>add</th>\n",
       "      <th>afar</th>\n",
       "      <th>afraid</th>\n",
       "      <th>ago</th>\n",
       "      <th>ahead</th>\n",
       "      <th>air</th>\n",
       "      <th>alight</th>\n",
       "      <th>...</th>\n",
       "      <th>yeah</th>\n",
       "      <th>year</th>\n",
       "      <th>yearn</th>\n",
       "      <th>yet</th>\n",
       "      <th>yo</th>\n",
       "      <th>you</th>\n",
       "      <th>youll</th>\n",
       "      <th>young</th>\n",
       "      <th>your</th>\n",
       "      <th>youv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1984</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1987</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1999</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2002</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2005</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2008</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2009</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2010</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 505 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  achiev  across  add  afar  afraid  ago  ahead  air  alight  ...  \\\n",
       "0   1984       0       0    0     0       0    0      0    0       0  ...   \n",
       "1   1986       8       0    0     0       0    0      0    1       0  ...   \n",
       "2   1987       1       0    0     0       0    0      0    0       0  ...   \n",
       "3   1990       0       0    0     0       0    0      0    0       0  ...   \n",
       "4   1998       0       0    0     0       0    0      0    0       0  ...   \n",
       "5   1999       1       0    0     0       1    0      0    0       0  ...   \n",
       "6   2000       0       0    0     1       0    0      0    0       0  ...   \n",
       "7   2001       0       0    0     0       0    0      0    0       0  ...   \n",
       "8   2002       0       0    0     0       0    0      0    0       0  ...   \n",
       "9   2003       0       0    0     0       0    0      0    0       0  ...   \n",
       "10  2004       0       0    0     0       0    0      0    0       0  ...   \n",
       "11  2005       6       0    0     0       0    0      0    0       0  ...   \n",
       "12  2006       0       0    0     0       0    0      0    0       0  ...   \n",
       "13  2007       0       0    0     0       0    0      0    0       0  ...   \n",
       "14  2007       0       0    0     0       0    0      0    0       0  ...   \n",
       "15  2008       2       0    0     0       0    0      0    0       0  ...   \n",
       "16  2009       1       0    0     0       1    0      0    0       0  ...   \n",
       "17  2010       0       0    0     0       0    0      0    0       0  ...   \n",
       "18  2011       0       0    0     0       0    0      0    0       1  ...   \n",
       "19  2012       0       1    1     0       0    0      0    0       0  ...   \n",
       "20  2013       0       0    0     0       0    1      0    0       0  ...   \n",
       "21  2015       0       0    0     0       0    0      2    0       0  ...   \n",
       "22  2016       0       0    0     0       0    0      0    0       0  ...   \n",
       "23  2017       0       0    0     0       0    0      0    0       0  ...   \n",
       "24  2018       1       0    0     0       0    0      0    0       0  ...   \n",
       "25  2019       0       0    0     0       0    0      1    0       0  ...   \n",
       "\n",
       "    yeah  year  yearn  yet  yo  you  youll  young  your  youv  \n",
       "0      0     0      0    0   0    0      1      0     0     2  \n",
       "1      0     0      0    0   0    0      0      0     0     0  \n",
       "2      0     0      0    0   0    0      0      0     0     0  \n",
       "3      0     0      0    0   0    0      0      0     0     0  \n",
       "4      0     0      0    0   0    0      0      0     0     0  \n",
       "5      0     1      0    0   0    0      0      0     0     0  \n",
       "6      0     0      0    0   0    0      0      1     0     0  \n",
       "7      0     0      0    0   0    0      0      0     0     0  \n",
       "8      0     0      0    0   0    0      0      0     0     0  \n",
       "9      0     0      0    0   0    0      0      0     0     0  \n",
       "10     0     0      0    0   0    0      0      0     0     0  \n",
       "11     0     0      0    0   0    0      0      0     0     0  \n",
       "12     0     0      0    0   0    0      0      0     0     0  \n",
       "13     0     0      0    1   0    0      1      0     0     0  \n",
       "14     0     0      0    0   0    0      0      0     0     0  \n",
       "15     0     0      0    0   0    0      0      0     0     0  \n",
       "16     0     0      0    0   0    0      1      0     1     0  \n",
       "17     0     0      0    0   0    0      0      0    10     0  \n",
       "18     0     0      0    0   0    0      0      0     0     0  \n",
       "19     0     0      0    0   0    0      0      0     0     0  \n",
       "20     2     0      0    0   1    0      0      0     0     0  \n",
       "21     0     1      0    1   0    0      0      0     0     0  \n",
       "22     0     0      1    0   0    1      0      0     0     0  \n",
       "23     0     0      0    0   0    0      0      0     0     0  \n",
       "24     0     0      0    0   0    0      0      0     0     0  \n",
       "25     0     1      0    1   0    0      0      1     0     0  \n",
       "\n",
       "[26 rows x 505 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = CountVectorizer()\n",
    "bag_of_words = count.fit_transform(np.array(data['lyrics']))\n",
    "# Show feature matrix\n",
    "bag_of_words.toarray()\n",
    "# Get feature names\n",
    "feature_names = count.get_feature_names()\n",
    "print(\"feature_names\", len(feature_names))\n",
    "\n",
    "bag_of_wrods_dataFrame = pd.DataFrame(bag_of_words.toarray(), columns=feature_names)\n",
    "\n",
    "pd.concat([data.year, bag_of_wrods_dataFrame], axis=1, sort=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The most used word in total lyrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>singapor</th>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>togeth</th>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dream</th>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heart</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>well</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>know</th>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>come</th>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stand</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hand</th>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>see</th>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sing</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>let</th>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>make</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alway</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>us</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reach</th>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>world</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shine</th>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count\n",
       "singapor     98\n",
       "togeth       66\n",
       "one          58\n",
       "dream        52\n",
       "home         50\n",
       "heart        34\n",
       "well         33\n",
       "know         33\n",
       "come         32\n",
       "stand        31\n",
       "hand         28\n",
       "see          26\n",
       "sing         25\n",
       "let          24\n",
       "make         23\n",
       "alway        23\n",
       "us           22\n",
       "reach        22\n",
       "world        21\n",
       "shine        21"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mostly_used_word = bag_of_wrods_dataFrame.sum(axis=0).sort_values(ascending=False)\n",
    "mostly_used_word_dataFrame = pd.DataFrame(data=mostly_used_word, columns=[\"count\"])\n",
    "mostly_used_word_dataFrame.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pick top words in bag of words\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>singapor</th>\n",
       "      <th>togeth</th>\n",
       "      <th>one</th>\n",
       "      <th>dream</th>\n",
       "      <th>home</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1984</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1987</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1999</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2002</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2003</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2004</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2005</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2006</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2008</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2009</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2010</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2015</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  singapor  togeth  one  dream  home\n",
       "0   1984         7       1    0      0     1\n",
       "1   1986         9       3    0      1     0\n",
       "2   1987        10       3    2      0     0\n",
       "3   1990         4       0    6      0     0\n",
       "4   1998         2       1    0      2     6\n",
       "5   1999         3       5    3      2     0\n",
       "6   2000         0       0    1      1     1\n",
       "7   2001         0       1    1      1     1\n",
       "8   2002         0       4    0      0     0\n",
       "9   2003        10       8   15      0     0\n",
       "10  2004         2       1    0      4    12\n",
       "11  2005         0       0    1      7     0\n",
       "12  2006         0       0    0      3    12\n",
       "13  2007         0       0    1      0     1\n",
       "14  2007         0       0    0      2     2\n",
       "15  2008         6       0    7      2     1\n",
       "16  2009         0       2    0      6     0\n",
       "17  2010        15       1    0      1     1\n",
       "18  2011         0       3    3      1     0\n",
       "19  2012         1       0    2      2     1\n",
       "20  2013         6      21   12      2     2\n",
       "21  2015         4       1    1      5     3\n",
       "22  2016         0       1    0      4     1\n",
       "23  2017         4       4    1      1     2\n",
       "24  2018        14       2    1      0     0\n",
       "25  2019         1       4    1      5     3"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Singapore, Together, One, Dream, Home\n",
    "pd.concat([data.year, bag_of_wrods_dataFrame.singapor, bag_of_wrods_dataFrame.togeth, bag_of_wrods_dataFrame.one, bag_of_wrods_dataFrame.dream, bag_of_wrods_dataFrame.home], axis=1, sort=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Song Lyrics Similiarity\n",
    "### TF-IDF Extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lyrics Similiarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>1984</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.13294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.107363</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.26588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1986</td>\n",
       "      <td>0.511749</td>\n",
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       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.103979</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1987</td>\n",
       "      <td>0.075723</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1998</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1999</td>\n",
       "      <td>0.088700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.127953</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.116440</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.12307</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.109219</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2002</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2003</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2005</td>\n",
       "      <td>0.277157</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.066635</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2006</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.096961</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2007</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.183715</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2008</td>\n",
       "      <td>0.116993</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2009</td>\n",
       "      <td>0.056188</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.081053</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.073760</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2010</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2011</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.083993</td>\n",
       "      <td>0.074540</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2012</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.115511</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.061286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.122573</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.061286</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2015</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.261340</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.118912</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2016</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.110234</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2018</td>\n",
       "      <td>0.054551</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088671</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090272</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.082150</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090272</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 448 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    year    achiev       add     afar    afraid       ago     ahead       air  \\\n",
       "0   1984  0.000000  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "1   1986  0.511749  0.000000  0.00000  0.000000  0.000000  0.000000  0.103979   \n",
       "2   1987  0.075723  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "3   1990  0.000000  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "4   1998  0.000000  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "5   1999  0.088700  0.000000  0.00000  0.127953  0.000000  0.000000  0.000000   \n",
       "6   2000  0.000000  0.000000  0.12307  0.000000  0.000000  0.000000  0.000000   \n",
       "7   2001  0.000000  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "8   2002  0.000000  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "9   2003  0.000000  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "10  2004  0.000000  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "11  2005  0.277157  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "12  2006  0.000000  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "13  2007  0.000000  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "14  2007  0.000000  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "15  2008  0.116993  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "16  2009  0.056188  0.000000  0.00000  0.081053  0.000000  0.000000  0.000000   \n",
       "17  2010  0.000000  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "18  2011  0.000000  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "19  2012  0.000000  0.115511  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "20  2013  0.000000  0.000000  0.00000  0.000000  0.061286  0.000000  0.000000   \n",
       "21  2015  0.000000  0.000000  0.00000  0.000000  0.000000  0.261340  0.000000   \n",
       "22  2016  0.000000  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "23  2017  0.000000  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "24  2018  0.054551  0.000000  0.00000  0.000000  0.000000  0.000000  0.000000   \n",
       "25  2019  0.000000  0.000000  0.00000  0.000000  0.000000  0.090272  0.000000   \n",
       "\n",
       "      alight      aliv  ...     worri  worthwhil     write      yeah  \\\n",
       "0   0.000000  0.000000  ...  0.000000    0.13294  0.000000  0.000000   \n",
       "1   0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "2   0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "3   0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "4   0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "5   0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "6   0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "7   0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "8   0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "9   0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "10  0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "11  0.000000  0.066635  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "12  0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "13  0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "14  0.000000  0.000000  ...  0.000000    0.00000  0.183715  0.000000   \n",
       "15  0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "16  0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "17  0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "18  0.083993  0.074540  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "19  0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "20  0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.122573   \n",
       "21  0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "22  0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "23  0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "24  0.000000  0.000000  ...  0.088671    0.00000  0.000000  0.000000   \n",
       "25  0.000000  0.000000  ...  0.000000    0.00000  0.000000  0.000000   \n",
       "\n",
       "        year     yearn        yo     youll     young     youv  \n",
       "0   0.000000  0.000000  0.000000  0.107363  0.000000  0.26588  \n",
       "1   0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "2   0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "3   0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "4   0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "5   0.116440  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "6   0.000000  0.000000  0.000000  0.000000  0.109219  0.00000  \n",
       "7   0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "8   0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "9   0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "10  0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "11  0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "12  0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "13  0.000000  0.000000  0.000000  0.096961  0.000000  0.00000  \n",
       "14  0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "15  0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "16  0.000000  0.000000  0.000000  0.073760  0.000000  0.00000  \n",
       "17  0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "18  0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "19  0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "20  0.000000  0.000000  0.061286  0.000000  0.000000  0.00000  \n",
       "21  0.118912  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "22  0.000000  0.110234  0.000000  0.000000  0.000000  0.00000  \n",
       "23  0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "24  0.000000  0.000000  0.000000  0.000000  0.000000  0.00000  \n",
       "25  0.082150  0.000000  0.000000  0.000000  0.090272  0.00000  \n",
       "\n",
       "[26 rows x 448 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the object of tfid vectorizer\n",
    "tfid_vectorizer = TfidfVectorizer(analyzer='word', min_df = 0, stop_words = 'english')\n",
    "# fit the vectorizer using the text data\n",
    "tfid_vectorizer.fit(data['lyrics'])\n",
    "# collect the vocabulary items used in the vectorizer\n",
    "dictionary = tfid_vectorizer.vocabulary_.items()  \n",
    "\n",
    "# extract the tfid representation matrix of the text data\n",
    "tfid_matrix = tfid_vectorizer.transform(data['lyrics'])\n",
    "# collect the tfid matrix in numpy array\n",
    "array = tfid_matrix.todense()\n",
    "\n",
    "# store the tf-idf array into pandas dataframe\n",
    "similiarity_df = pd.DataFrame(array, columns=tfid_vectorizer.get_feature_names())\n",
    "\n",
    "pd.concat([data.year, similiarity_df], axis=1, sort=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use Scikit learn Cosine Similarity function to compare the first document i.e. Document 0 with the other Documents in Corpus. \n",
    "\n",
    "Find songs with similar with Because it's Singapore\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Song:  Because it's Singapore\n"
     ]
    },
    {
     "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>cosine_similarity</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>Because it's Singapore</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.297777</td>\n",
       "      <td>We are Singapore remix</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.281339</td>\n",
       "      <td>Our Singapore - remix of We Will Get There (20...</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.278583</td>\n",
       "      <td>We are Singapore</td>\n",
       "      <td>1987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.275578</td>\n",
       "      <td>We Will Get There</td>\n",
       "      <td>2002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.274681</td>\n",
       "      <td>One United People</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.266684</td>\n",
       "      <td>Together</td>\n",
       "      <td>1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.263443</td>\n",
       "      <td>Our Singapore</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.251536</td>\n",
       "      <td>One Singapore</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.251397</td>\n",
       "      <td>One People, One Nation, One Singapore</td>\n",
       "      <td>1990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.228239</td>\n",
       "      <td>Count on Me, Singapore</td>\n",
       "      <td>1986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.193306</td>\n",
       "      <td>Home</td>\n",
       "      <td>1998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.180547</td>\n",
       "      <td>Home remix</td>\n",
       "      <td>2004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.160546</td>\n",
       "      <td>Stand Up for Singapore</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.158855</td>\n",
       "      <td>Shine on Me</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.157851</td>\n",
       "      <td>My Island Home</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.157827</td>\n",
       "      <td>Song for Singapore</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.151226</td>\n",
       "      <td>Where I Belong</td>\n",
       "      <td>2001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.148836</td>\n",
       "      <td>Love at First Light</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.118256</td>\n",
       "      <td>Shine for Singapore</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.100100</td>\n",
       "      <td>Tomorrow's Here Today</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.084479</td>\n",
       "      <td>In a Heartbeat</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.079300</td>\n",
       "      <td>Will you</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.076563</td>\n",
       "      <td>What Do You See?</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.042271</td>\n",
       "      <td>Reach Out for the Skies</td>\n",
       "      <td>2005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.041592</td>\n",
       "      <td>There’s No Place I’d Rather Be</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cosine_similarity                                              title  year\n",
       "23           1.000000                             Because it's Singapore  2017\n",
       "24           0.297777                             We are Singapore remix  2018\n",
       "25           0.281339  Our Singapore - remix of We Will Get There (20...  2019\n",
       "2            0.278583                                   We are Singapore  1987\n",
       "8            0.275578                                  We Will Get There  2002\n",
       "9            0.274681                                  One United People  2003\n",
       "5            0.266684                                           Together  1999\n",
       "21           0.263443                                      Our Singapore  2015\n",
       "20           0.251536                                      One Singapore  2013\n",
       "3            0.251397              One People, One Nation, One Singapore  1990\n",
       "1            0.228239                             Count on Me, Singapore  1986\n",
       "4            0.193306                                               Home  1998\n",
       "10           0.180547                                         Home remix  2004\n",
       "0            0.160546                             Stand Up for Singapore  1984\n",
       "6            0.158855                                        Shine on Me  2000\n",
       "12           0.157851                                     My Island Home  2006\n",
       "17           0.157827                                 Song for Singapore  2010\n",
       "7            0.151226                                     Where I Belong  2001\n",
       "19           0.148836                                Love at First Light  2012\n",
       "15           0.118256                                Shine for Singapore  2008\n",
       "22           0.100100                              Tomorrow's Here Today  2016\n",
       "18           0.084479                                     In a Heartbeat  2011\n",
       "14           0.079300                                           Will you  2007\n",
       "16           0.076563                                   What Do You See?  2009\n",
       "11           0.042271                            Reach Out for the Skies  2005\n",
       "13           0.041592                     There’s No Place I’d Rather Be  2007"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "song_number = 24\n",
    "current_song = tfid_matrix[song_number-1:song_number]\n",
    "similarity = cosine_similarity(tfid_matrix[song_number-1:song_number].toarray(), tfid_matrix.toarray())\n",
    "\n",
    "print(\"Song: \", data['title'][song_number-1])\n",
    "\n",
    "# sort the dataframe\n",
    "similarity_dataFrame = pd.DataFrame(data=similarity.flatten(), columns=[\"cosine_similarity\"])\n",
    "similarity_dataFrame['title'] = data['title']\n",
    "similarity_dataFrame['year'] = data['year']\n",
    "\n",
    "similarity_dataFrame.sort_values(by=['cosine_similarity'], ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using TSNE to Plot a Subset of Similar Words from Word2Vec\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_directory = os.getcwd()\n",
    "final_directory = os.path.join(current_directory, r'word2vec')\n",
    "if not os.path.exists(final_directory):\n",
    "   os.makedirs(final_directory)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total lines:  726\n"
     ]
    }
   ],
   "source": [
    "import gensim\n",
    "from gensim.models import Phrases\n",
    "from gensim.models.word2vec import LineSentence\n",
    "from gensim import corpora, models\n",
    "from gensim.models import LdaMulticore\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.corpora import Dictionary, MmCorpus\n",
    "\n",
    "\n",
    "data = pd.read_csv(\"./songs.csv\")\n",
    "\n",
    "all_sentences_normalized_filepath = 'word2vec/all_lyrics_text.txt'\n",
    "\n",
    "with open(all_sentences_normalized_filepath) as foo:\n",
    "    lines = len(foo.readlines())\n",
    "    print(\"total lines: \", lines)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(all_sentences_normalized_filepath, 'w', encoding='utf-8') as f:\n",
    "    c = len(data.lyrics.values)\n",
    "    for lyrics in data.lyrics.values:\n",
    "        if pd.isnull(lyrics): # if there is null, go to next\n",
    "            continue\n",
    "        f.write(formatted_text(lyrics) + '\\n')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 12.9 ms, sys: 2.04 ms, total: 14.9 ms\n",
      "Wall time: 13.5 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "USE_PREMADE_BIGRAM_MODEL = False\n",
    "all_bigram_model_filepath = 'word2vec/all_bigram_model'\n",
    "\n",
    "all_unigram_sentences = LineSentence(all_sentences_normalized_filepath)\n",
    "if not USE_PREMADE_BIGRAM_MODEL:    \n",
    "    \n",
    "    all_bigram_model = Phrases(all_unigram_sentences) #check whether phrase or not\n",
    "    all_bigram_model.save(all_bigram_model_filepath)\n",
    "    \n",
    "else:\n",
    "    all_bigram_model = Phrases.load(all_bigram_model_filepath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "word2vec/all_lyrics_text.txt\n"
     ]
    }
   ],
   "source": [
    "print(all_sentences_normalized_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 213 ms, sys: 14.7 ms, total: 228 ms\n",
      "Wall time: 1.18 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "USE_PREMADE_BIGRAM_SENTENCES = False\n",
    "\n",
    "all_bigram_sentences_filepath = 'word2vec/all_sentences_for_word2vec.txt'\n",
    "\n",
    "if not USE_PREMADE_BIGRAM_SENTENCES:\n",
    "    \n",
    "    with open(all_bigram_sentences_filepath, 'w', encoding='utf-8') as f:\n",
    "        for unigram_sentence in all_unigram_sentences:\n",
    "            all_bigram_sentence = all_bigram_model[unigram_sentence]\n",
    "            f.write(' '.join(all_bigram_sentence) + '\\n')\n",
    "else:\n",
    "    assert path.exists(all_bigram_sentences_filepath)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 25.6 s, sys: 1.24 s, total: 26.8 s\n",
      "Wall time: 28.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "USE_PREMADE_WORD2VEC = False\n",
    "\n",
    "all2vec_filepath = 'word2vec/all_word2vec_model'\n",
    "\n",
    "if not USE_PREMADE_WORD2VEC:\n",
    "    lyrics_for_word2vec = LineSentence(all_bigram_sentences_filepath)\n",
    "    all2vec = Word2Vec(lyrics_for_word2vec, size=100, window=5, min_count=1, sg=1)\n",
    "    for _ in range(25):\n",
    "        all2vec.train(lyrics_for_word2vec, total_examples=675, epochs=30)\n",
    "    all2vec.save(all2vec_filepath)\n",
    "else:\n",
    "    all2vec = Word2Vec.load(all2vec_filepath)\n",
    "all2vec.init_sims()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "all2vec_filepath = 'word2vec/all_word2vec_model'\n",
    "all2vec = Word2Vec.load(all2vec_filepath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>and</td>\n",
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       "      <th>3</th>\n",
       "      <td>a</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>to</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "     0\n",
       "0  the\n",
       "1   we\n",
       "2  and\n",
       "3    a\n",
       "4   to"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv_dataFrame = pd.DataFrame(all2vec.wv.index2word)\n",
    "wv_dataFrame.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find the most smiliar words with 'singapore'  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('can_achieve', 0.47907426953315735),\n",
       " ('singaporeans', 0.4682055413722992),\n",
       " ('taken', 0.4670264720916748),\n",
       " ('forevermore', 0.46100330352783203),\n",
       " ('granted', 0.4527587592601776),\n",
       " ('lies', 0.4477716088294983),\n",
       " ('sometimes', 0.4453321695327759),\n",
       " ('goal', 0.4383291006088257),\n",
       " ('smile', 0.4373680651187897),\n",
       " ('stand_up', 0.43636220693588257),\n",
       " ('ohohohohohohoh', 0.4358106553554535),\n",
       " ('thankful', 0.43003904819488525),\n",
       " ('hands', 0.4268384575843811),\n",
       " ('winding', 0.4247167706489563),\n",
       " ('best', 0.41068035364151),\n",
       " ('gone', 0.3935614228248596),\n",
       " ('things', 0.39219731092453003),\n",
       " ('we’ve', 0.3921252489089966),\n",
       " ('heartbeat', 0.38764864206314087),\n",
       " ('more', 0.38321107625961304)]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all2vec.wv.most_similar(positive=['singapore'], topn=20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>6</th>\n",
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       "      <th>97</th>\n",
       "      <th>98</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>stand_up</th>\n",
       "      <td>0.239891</td>\n",
       "      <td>0.565018</td>\n",
       "      <td>0.063534</td>\n",
       "      <td>0.164660</td>\n",
       "      <td>-0.234081</td>\n",
       "      <td>-0.309161</td>\n",
       "      <td>0.489351</td>\n",
       "      <td>-0.780812</td>\n",
       "      <td>0.899208</td>\n",
       "      <td>-0.157450</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.233194</td>\n",
       "      <td>0.821353</td>\n",
       "      <td>-0.549739</td>\n",
       "      <td>-0.468398</td>\n",
       "      <td>-0.085528</td>\n",
       "      <td>-0.649743</td>\n",
       "      <td>0.647005</td>\n",
       "      <td>-0.571035</td>\n",
       "      <td>-0.741131</td>\n",
       "      <td>-0.115513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for</th>\n",
       "      <td>0.889538</td>\n",
       "      <td>0.673244</td>\n",
       "      <td>-0.824383</td>\n",
       "      <td>0.274197</td>\n",
       "      <td>-0.185458</td>\n",
       "      <td>0.151348</td>\n",
       "      <td>-0.231167</td>\n",
       "      <td>0.314655</td>\n",
       "      <td>-0.139669</td>\n",
       "      <td>0.386256</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.160107</td>\n",
       "      <td>0.335228</td>\n",
       "      <td>0.113395</td>\n",
       "      <td>0.371620</td>\n",
       "      <td>0.026625</td>\n",
       "      <td>-0.455606</td>\n",
       "      <td>0.377863</td>\n",
       "      <td>-0.433757</td>\n",
       "      <td>0.080183</td>\n",
       "      <td>-0.602206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>singapore</th>\n",
       "      <td>-0.135616</td>\n",
       "      <td>0.655438</td>\n",
       "      <td>-0.386355</td>\n",
       "      <td>0.826533</td>\n",
       "      <td>0.144880</td>\n",
       "      <td>0.215064</td>\n",
       "      <td>0.498785</td>\n",
       "      <td>-0.288478</td>\n",
       "      <td>-0.153735</td>\n",
       "      <td>-0.387304</td>\n",
       "      <td>...</td>\n",
       "      <td>0.154733</td>\n",
       "      <td>0.027422</td>\n",
       "      <td>-0.176869</td>\n",
       "      <td>0.273895</td>\n",
       "      <td>0.096506</td>\n",
       "      <td>0.034425</td>\n",
       "      <td>0.418511</td>\n",
       "      <td>-0.304195</td>\n",
       "      <td>0.494054</td>\n",
       "      <td>0.178736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>do</th>\n",
       "      <td>0.572304</td>\n",
       "      <td>-0.298063</td>\n",
       "      <td>0.420800</td>\n",
       "      <td>0.371776</td>\n",
       "      <td>-0.271642</td>\n",
       "      <td>0.017550</td>\n",
       "      <td>0.627456</td>\n",
       "      <td>-0.002975</td>\n",
       "      <td>0.359950</td>\n",
       "      <td>0.332036</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.008266</td>\n",
       "      <td>0.998744</td>\n",
       "      <td>0.156871</td>\n",
       "      <td>0.102102</td>\n",
       "      <td>-0.456976</td>\n",
       "      <td>0.104097</td>\n",
       "      <td>0.457750</td>\n",
       "      <td>-0.652603</td>\n",
       "      <td>-0.207516</td>\n",
       "      <td>-0.074222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>the</th>\n",
       "      <td>0.037027</td>\n",
       "      <td>-0.373364</td>\n",
       "      <td>-0.159412</td>\n",
       "      <td>-0.032979</td>\n",
       "      <td>-0.158839</td>\n",
       "      <td>0.945273</td>\n",
       "      <td>0.424767</td>\n",
       "      <td>-0.093098</td>\n",
       "      <td>0.476826</td>\n",
       "      <td>-0.156614</td>\n",
       "      <td>...</td>\n",
       "      <td>0.097924</td>\n",
       "      <td>-0.131293</td>\n",
       "      <td>-0.380869</td>\n",
       "      <td>0.158264</td>\n",
       "      <td>0.206562</td>\n",
       "      <td>-0.264686</td>\n",
       "      <td>-0.369306</td>\n",
       "      <td>-0.567412</td>\n",
       "      <td>0.246076</td>\n",
       "      <td>-0.071524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 0         1         2         3         4         5   \\\n",
       "stand_up   0.239891  0.565018  0.063534  0.164660 -0.234081 -0.309161   \n",
       "for        0.889538  0.673244 -0.824383  0.274197 -0.185458  0.151348   \n",
       "singapore -0.135616  0.655438 -0.386355  0.826533  0.144880  0.215064   \n",
       "do         0.572304 -0.298063  0.420800  0.371776 -0.271642  0.017550   \n",
       "the        0.037027 -0.373364 -0.159412 -0.032979 -0.158839  0.945273   \n",
       "\n",
       "                 6         7         8         9   ...        90        91  \\\n",
       "stand_up   0.489351 -0.780812  0.899208 -0.157450  ... -0.233194  0.821353   \n",
       "for       -0.231167  0.314655 -0.139669  0.386256  ... -0.160107  0.335228   \n",
       "singapore  0.498785 -0.288478 -0.153735 -0.387304  ...  0.154733  0.027422   \n",
       "do         0.627456 -0.002975  0.359950  0.332036  ... -0.008266  0.998744   \n",
       "the        0.424767 -0.093098  0.476826 -0.156614  ...  0.097924 -0.131293   \n",
       "\n",
       "                 92        93        94        95        96        97  \\\n",
       "stand_up  -0.549739 -0.468398 -0.085528 -0.649743  0.647005 -0.571035   \n",
       "for        0.113395  0.371620  0.026625 -0.455606  0.377863 -0.433757   \n",
       "singapore -0.176869  0.273895  0.096506  0.034425  0.418511 -0.304195   \n",
       "do         0.156871  0.102102 -0.456976  0.104097  0.457750 -0.652603   \n",
       "the       -0.380869  0.158264  0.206562 -0.264686 -0.369306 -0.567412   \n",
       "\n",
       "                 98        99  \n",
       "stand_up  -0.741131 -0.115513  \n",
       "for        0.080183 -0.602206  \n",
       "singapore  0.494054  0.178736  \n",
       "do        -0.207516 -0.074222  \n",
       "the        0.246076 -0.071524  \n",
       "\n",
       "[5 rows x 100 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all2vec_dic = dict({})\n",
    "for idx, key in enumerate(all2vec.wv.vocab):\n",
    "    all2vec_dic[key] = all2vec.wv[key].tolist()\n",
    "    \n",
    "all2vec_dataFrame = pd.DataFrame.from_dict(all2vec_dic).T\n",
    "all2vec_dataFrame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.63 s, sys: 31.8 ms, total: 4.66 s\n",
      "Wall time: 4.69 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from sklearn.manifold import TSNE\n",
    "import pickle\n",
    "\n",
    "USE_PREMADE_TSNE = False\n",
    "\n",
    "tsne_filepath = 'example/tsne.pkl'\n",
    "\n",
    "if not USE_PREMADE_TSNE:\n",
    "    \n",
    "    tsne = TSNE(random_state=0)\n",
    "    tsne_points = tsne.fit_transform(all2vec_dataFrame)\n",
    "    with open(tsne_filepath, 'wb') as f:\n",
    "        pickle.dump(tsne_points, f)\n",
    "else:\n",
    "    with open(tsne_filepath, 'rb') as f:\n",
    "        tsne_points = pickle.load(f)\n",
    "\n",
    "tsne_df = pd.DataFrame(tsne_points, index=all2vec_dataFrame.index, columns=['x_coord', 'y_coord'])\n",
    "tsne_df['word'] = tsne_df.index\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "      <th>word</th>\n",
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       "  <tbody>\n",
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       "      <th>stand_up</th>\n",
       "      <td>9.463366</td>\n",
       "      <td>25.297653</td>\n",
       "      <td>stand_up</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>for</th>\n",
       "      <td>9.690619</td>\n",
       "      <td>-7.415847</td>\n",
       "      <td>for</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>singapore</th>\n",
       "      <td>9.550549</td>\n",
       "      <td>-9.126614</td>\n",
       "      <td>singapore</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>do</th>\n",
       "      <td>9.451916</td>\n",
       "      <td>23.508945</td>\n",
       "      <td>do</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>the</th>\n",
       "      <td>7.538430</td>\n",
       "      <td>-8.201977</td>\n",
       "      <td>the</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            x_coord    y_coord       word\n",
       "stand_up   9.463366  25.297653   stand_up\n",
       "for        9.690619  -7.415847        for\n",
       "singapore  9.550549  -9.126614  singapore\n",
       "do         9.451916  23.508945         do\n",
       "the        7.538430  -8.201977        the"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tsne_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
       "        <span id=\"1046\">Loading BokehJS ...</span>\n",
       "    </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "(function(root) {\n",
       "  function now() {\n",
       "    return new Date();\n",
       "  }\n",
       "\n",
       "  var force = true;\n",
       "\n",
       "  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "    root._bokeh_onload_callbacks = [];\n",
       "    root._bokeh_is_loading = undefined;\n",
       "  }\n",
       "\n",
       "  var JS_MIME_TYPE = 'application/javascript';\n",
       "  var HTML_MIME_TYPE = 'text/html';\n",
       "  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
       "  var CLASS_NAME = 'output_bokeh rendered_html';\n",
       "\n",
       "  /**\n",
       "   * Render data to the DOM node\n",
       "   */\n",
       "  function render(props, node) {\n",
       "    var script = document.createElement(\"script\");\n",
       "    node.appendChild(script);\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when an output is cleared or removed\n",
       "   */\n",
       "  function handleClearOutput(event, handle) {\n",
       "    var cell = handle.cell;\n",
       "\n",
       "    var id = cell.output_area._bokeh_element_id;\n",
       "    var server_id = cell.output_area._bokeh_server_id;\n",
       "    // Clean up Bokeh references\n",
       "    if (id != null && id in Bokeh.index) {\n",
       "      Bokeh.index[id].model.document.clear();\n",
       "      delete Bokeh.index[id];\n",
       "    }\n",
       "\n",
       "    if (server_id !== undefined) {\n",
       "      // Clean up Bokeh references\n",
       "      var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
       "      cell.notebook.kernel.execute(cmd, {\n",
       "        iopub: {\n",
       "          output: function(msg) {\n",
       "            var id = msg.content.text.trim();\n",
       "            if (id in Bokeh.index) {\n",
       "              Bokeh.index[id].model.document.clear();\n",
       "              delete Bokeh.index[id];\n",
       "            }\n",
       "          }\n",
       "        }\n",
       "      });\n",
       "      // Destroy server and session\n",
       "      var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
       "      cell.notebook.kernel.execute(cmd);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  /**\n",
       "   * Handle when a new output is added\n",
       "   */\n",
       "  function handleAddOutput(event, handle) {\n",
       "    var output_area = handle.output_area;\n",
       "    var output = handle.output;\n",
       "\n",
       "    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
       "    if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
       "      return\n",
       "    }\n",
       "\n",
       "    var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
       "\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
       "      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
       "      // store reference to embed id on output_area\n",
       "      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
       "    }\n",
       "    if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
       "      var bk_div = document.createElement(\"div\");\n",
       "      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "      var script_attrs = bk_div.children[0].attributes;\n",
       "      for (var i = 0; i < script_attrs.length; i++) {\n",
       "        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
       "      }\n",
       "      // store reference to server id on output_area\n",
       "      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
       "    }\n",
       "  }\n",
       "\n",
       "  function register_renderer(events, OutputArea) {\n",
       "\n",
       "    function append_mime(data, metadata, element) {\n",
       "      // create a DOM node to render to\n",
       "      var toinsert = this.create_output_subarea(\n",
       "        metadata,\n",
       "        CLASS_NAME,\n",
       "        EXEC_MIME_TYPE\n",
       "      );\n",
       "      this.keyboard_manager.register_events(toinsert);\n",
       "      // Render to node\n",
       "      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "      render(props, toinsert[toinsert.length - 1]);\n",
       "      element.append(toinsert);\n",
       "      return toinsert\n",
       "    }\n",
       "\n",
       "    /* Handle when an output is cleared or removed */\n",
       "    events.on('clear_output.CodeCell', handleClearOutput);\n",
       "    events.on('delete.Cell', handleClearOutput);\n",
       "\n",
       "    /* Handle when a new output is added */\n",
       "    events.on('output_added.OutputArea', handleAddOutput);\n",
       "\n",
       "    /**\n",
       "     * Register the mime type and append_mime function with output_area\n",
       "     */\n",
       "    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "      /* Is output safe? */\n",
       "      safe: true,\n",
       "      /* Index of renderer in `output_area.display_order` */\n",
       "      index: 0\n",
       "    });\n",
       "  }\n",
       "\n",
       "  // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
       "  if (root.Jupyter !== undefined) {\n",
       "    var events = require('base/js/events');\n",
       "    var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
       "\n",
       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
       "      register_renderer(events, OutputArea);\n",
       "    }\n",
       "  }\n",
       "\n",
       "  \n",
       "  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "    root._bokeh_timeout = Date.now() + 5000;\n",
       "    root._bokeh_failed_load = false;\n",
       "  }\n",
       "\n",
       "  var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "     \"<div style='background-color: #fdd'>\\n\"+\n",
       "     \"<p>\\n\"+\n",
       "     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "     \"</p>\\n\"+\n",
       "     \"<ul>\\n\"+\n",
       "     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "     \"</ul>\\n\"+\n",
       "     \"<code>\\n\"+\n",
       "     \"from bokeh.resources import INLINE\\n\"+\n",
       "     \"output_notebook(resources=INLINE)\\n\"+\n",
       "     \"</code>\\n\"+\n",
       "     \"</div>\"}};\n",
       "\n",
       "  function display_loaded() {\n",
       "    var el = document.getElementById(\"1046\");\n",
       "    if (el != null) {\n",
       "      el.textContent = \"BokehJS is loading...\";\n",
       "    }\n",
       "    if (root.Bokeh !== undefined) {\n",
       "      if (el != null) {\n",
       "        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
       "      }\n",
       "    } else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(display_loaded, 100)\n",
       "    }\n",
       "  }\n",
       "\n",
       "\n",
       "  function run_callbacks() {\n",
       "    try {\n",
       "      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "    }\n",
       "    finally {\n",
       "      delete root._bokeh_onload_callbacks\n",
       "    }\n",
       "    console.info(\"Bokeh: all callbacks have finished\");\n",
       "  }\n",
       "\n",
       "  function load_libs(js_urls, callback) {\n",
       "    root._bokeh_onload_callbacks.push(callback);\n",
       "    if (root._bokeh_is_loading > 0) {\n",
       "      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "      return null;\n",
       "    }\n",
       "    if (js_urls == null || js_urls.length === 0) {\n",
       "      run_callbacks();\n",
       "      return null;\n",
       "    }\n",
       "    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "    root._bokeh_is_loading = js_urls.length;\n",
       "    for (var i = 0; i < js_urls.length; i++) {\n",
       "      var url = js_urls[i];\n",
       "      var s = document.createElement('script');\n",
       "      s.src = url;\n",
       "      s.async = false;\n",
       "      s.onreadystatechange = s.onload = function() {\n",
       "        root._bokeh_is_loading--;\n",
       "        if (root._bokeh_is_loading === 0) {\n",
       "          console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "          run_callbacks()\n",
       "        }\n",
       "      };\n",
       "      s.onerror = function() {\n",
       "        console.warn(\"failed to load library \" + url);\n",
       "      };\n",
       "      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "      document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "    }\n",
       "  };var element = document.getElementById(\"1046\");\n",
       "  if (element == null) {\n",
       "    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1046' but no matching script tag was found. \")\n",
       "    return false;\n",
       "  }\n",
       "\n",
       "  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n",
       "\n",
       "  var inline_js = [\n",
       "    function(Bokeh) {\n",
       "      Bokeh.set_log_level(\"info\");\n",
       "    },\n",
       "    \n",
       "    function(Bokeh) {\n",
       "      \n",
       "    },\n",
       "    function(Bokeh) {\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n",
       "      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n",
       "      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n",
       "    }\n",
       "  ];\n",
       "\n",
       "  function run_inline_js() {\n",
       "    \n",
       "    if ((root.Bokeh !== undefined) || (force === true)) {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i].call(root, root.Bokeh);\n",
       "      }if (force === true) {\n",
       "        display_loaded();\n",
       "      }} else if (Date.now() < root._bokeh_timeout) {\n",
       "      setTimeout(run_inline_js, 100);\n",
       "    } else if (!root._bokeh_failed_load) {\n",
       "      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "      root._bokeh_failed_load = true;\n",
       "    } else if (force !== true) {\n",
       "      var cell = $(document.getElementById(\"1046\")).parents('.cell').data().cell;\n",
       "      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "    }\n",
       "\n",
       "  }\n",
       "\n",
       "  if (root._bokeh_is_loading === 0) {\n",
       "    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "    run_inline_js();\n",
       "  } else {\n",
       "    load_libs(js_urls, function() {\n",
       "      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "      run_inline_js();\n",
       "    });\n",
       "  }\n",
       "}(window));"
      ],
      "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \"<div style='background-color: #fdd'>\\n\"+\n     \"<p>\\n\"+\n     \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded() {\n    var el = document.getElementById(\"1046\");\n    if (el != null) {\n      el.textContent = \"BokehJS is loading...\";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n    }\n    finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.info(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(js_urls, callback) {\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    root._bokeh_is_loading = js_urls.length;\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var s = document.createElement('script');\n      s.src = url;\n      s.async = false;\n      s.onreadystatechange = s.onload = function() {\n        root._bokeh_is_loading--;\n        if (root._bokeh_is_loading === 0) {\n          console.log(\"Bokeh: all BokehJS libraries loaded\");\n          run_callbacks()\n        }\n      };\n      s.onerror = function() {\n        console.warn(\"failed to load library \" + url);\n      };\n      console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.getElementsByTagName(\"head\")[0].appendChild(s);\n    }\n  };var element = document.getElementById(\"1046\");\n  if (element == null) {\n    console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1046' but no matching script tag was found. \")\n    return false;\n  }\n\n  var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n      console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n      Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n    }\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\"1046\")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(js_urls, function() {\n      console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "  <div class=\"bk-root\" id=\"a1e51071-b3c6-4632-bb77-5243778a42f8\" data-root-id=\"1003\"></div>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "(function(root) {\n",
       "  function embed_document(root) {\n",
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:\"float32\",\"shape\":[701]}},\"selected\":{\"id\":\"1052\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"1053\",\"type\":\"UnionRenderers\"}},\"id\":\"1001\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"1011\",\"type\":\"LinearScale\"},{\"attributes\":{\"formatter\":{\"id\":\"1048\",\"type\":\"BasicTickFormatter\"},\"plot\":{\"id\":\"1003\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"1014\",\"type\":\"BasicTicker\"},\"visible\":false},\"id\":\"1013\",\"type\":\"LinearAxis\"}],\"root_ids\":[\"1003\"]},\"title\":\"Bokeh Application\",\"version\":\"1.0.4\"}};\n",
       "  var render_items = [{\"docid\":\"828f6687-3639-46cb-b8ab-e2ea21d32f71\",\"roots\":{\"1003\":\"a1e51071-b3c6-4632-bb77-5243778a42f8\"}}];\n",
       "  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "\n",
       "  }\n",
       "  if (root.Bokeh !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined) {\n",
       "        embed_document(root);\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "      attempts++;\n",
       "      if (attempts > 100) {\n",
       "        console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        clearInterval(timer);\n",
       "      }\n",
       "    }, 10, root)\n",
       "  }\n",
       "})(window);"
      ],
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "1003"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, show, output_notebook\n",
    "\n",
    "from bokeh.models import HoverTool, ColumnDataSource, LabelSet, value\n",
    "\n",
    "\n",
    "# prepare the data in a form suitable for bokeh.\n",
    "plot_data = ColumnDataSource(data=tsne_df)\n",
    "\n",
    "# create the plot and configure it\n",
    "tsne_plot = figure(title='t-SNE Word Embeddings',\n",
    "                   plot_width = 800,\n",
    "                   plot_height = 800,\n",
    "                   active_scroll='wheel_zoom'\n",
    "                  )\n",
    "\n",
    "tsne_plot.circle('x_coord', 'y_coord', source=plot_data,\n",
    "                 color='red', line_alpha=0.2, fill_alpha=0.1,\n",
    "                 size=10, hover_line_color='orange')\n",
    "\n",
    "labels = LabelSet(x='x_coord', y='y_coord', text='word', level='glyph',\n",
    "              x_offset=5, y_offset=5, source=plot_data, render_mode='canvas')\n",
    "\n",
    "\n",
    "# adjust visual elements of the plot\n",
    "tsne_plot.title.text_font_size = value('16pt')\n",
    "tsne_plot.xaxis.visible = False\n",
    "tsne_plot.yaxis.visible = False\n",
    "tsne_plot.grid.grid_line_color = None\n",
    "tsne_plot.outline_line_color = None\n",
    "\n",
    "tsne_plot.add_layout(labels)\n",
    "\n",
    "# show time\n",
    "output_notebook()\n",
    "show(tsne_plot);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}