publication_dataset_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import altair as alt\n",
    "import nltk\n",
    "import re\n",
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "import gensim.corpora as corpora\n",
    "from gensim.sklearn_api import TfIdfTransformer\n",
    "from gensim.matutils import corpus2dense, corpus2csc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./IEEE VIS papers 1990-2018 - Main dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing and Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove missing values\n",
    "\n",
    "df = df.dropna(subset=['Abstract'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'the use of critical point analysis to generate representations of the vector field topology of numerical flow data sets is discussed. critical points are located and characterized in a two-dimensional domain, which may be either a two-dimensional flow field or the tangential velocity field near a three-dimensional body. tangent curves are then integrated out along the principal directions of certain classes of critical points. the points and curves are linked to form a skeleton representing the two-dimensional vector field topology. when generated from the tangential velocity field near a body in a three-dimensional flow, the skeleton includes the critical points and curves which provide a basis for analyzing the three-dimensional structure of the flow separation.'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turn text into lowercase\n",
    "\n",
    "df['Abstract'] = [abstract.lower() for abstract in df['Abstract']]\n",
    "df['Abstract'] = [abstract[:-19] for abstract in df['Abstract']]\n",
    "df.iloc[0]['Abstract']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'the use of critical point analysis to generate representations of the vector field topology of numerical flow data sets is discussed critical points are located and characterized in a twodimensional domain which may be either a twodimensional flow field or the tangential velocity field near a threedimensional body tangent curves are then integrated out along the principal directions of certain classes of critical points the points and curves are linked to form a skeleton representing the twodimensional vector field topology when generated from the tangential velocity field near a body in a threedimensional flow the skeleton includes the critical points and curves which provide a basis for analyzing the threedimensional structure of the flow separation'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove punctuation\n",
    "\n",
    "def remove_punct(text):\n",
    "    text_nopunct = \"\".join([char for char in text if char not in string.punctuation])\n",
    "    return text_nopunct\n",
    "\n",
    "df['Abstract'] = df['Abstract'].apply(lambda x: remove_punct(x))\n",
    "df.iloc[0]['Abstract']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['the',\n",
       " 'use',\n",
       " 'of',\n",
       " 'critical',\n",
       " 'point',\n",
       " 'analysis',\n",
       " 'to',\n",
       " 'generate',\n",
       " 'representations',\n",
       " 'of',\n",
       " 'the',\n",
       " 'vector',\n",
       " 'field',\n",
       " 'topology',\n",
       " 'of',\n",
       " 'numerical',\n",
       " 'flow',\n",
       " 'data',\n",
       " 'sets',\n",
       " 'is',\n",
       " 'discussed',\n",
       " 'critical',\n",
       " 'points',\n",
       " 'are',\n",
       " 'located',\n",
       " 'and',\n",
       " 'characterized',\n",
       " 'in',\n",
       " 'a',\n",
       " 'twodimensional',\n",
       " 'domain',\n",
       " 'which',\n",
       " 'may',\n",
       " 'be',\n",
       " 'either',\n",
       " 'a',\n",
       " 'twodimensional',\n",
       " 'flow',\n",
       " 'field',\n",
       " 'or',\n",
       " 'the',\n",
       " 'tangential',\n",
       " 'velocity',\n",
       " 'field',\n",
       " 'near',\n",
       " 'a',\n",
       " 'threedimensional',\n",
       " 'body',\n",
       " 'tangent',\n",
       " 'curves',\n",
       " 'are',\n",
       " 'then',\n",
       " 'integrated',\n",
       " 'out',\n",
       " 'along',\n",
       " 'the',\n",
       " 'principal',\n",
       " 'directions',\n",
       " 'of',\n",
       " 'certain',\n",
       " 'classes',\n",
       " 'of',\n",
       " 'critical',\n",
       " 'points',\n",
       " 'the',\n",
       " 'points',\n",
       " 'and',\n",
       " 'curves',\n",
       " 'are',\n",
       " 'linked',\n",
       " 'to',\n",
       " 'form',\n",
       " 'a',\n",
       " 'skeleton',\n",
       " 'representing',\n",
       " 'the',\n",
       " 'twodimensional',\n",
       " 'vector',\n",
       " 'field',\n",
       " 'topology',\n",
       " 'when',\n",
       " 'generated',\n",
       " 'from',\n",
       " 'the',\n",
       " 'tangential',\n",
       " 'velocity',\n",
       " 'field',\n",
       " 'near',\n",
       " 'a',\n",
       " 'body',\n",
       " 'in',\n",
       " 'a',\n",
       " 'threedimensional',\n",
       " 'flow',\n",
       " 'the',\n",
       " 'skeleton',\n",
       " 'includes',\n",
       " 'the',\n",
       " 'critical',\n",
       " 'points',\n",
       " 'and',\n",
       " 'curves',\n",
       " 'which',\n",
       " 'provide',\n",
       " 'a',\n",
       " 'basis',\n",
       " 'for',\n",
       " 'analyzing',\n",
       " 'the',\n",
       " 'threedimensional',\n",
       " 'structure',\n",
       " 'of',\n",
       " 'the',\n",
       " 'flow',\n",
       " 'separation']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tokenize text so that the words in text are separated by commas\n",
    "\n",
    "def tokenize(text):\n",
    "    tokens = re.split(\"\\W+\", text)\n",
    "    return tokens\n",
    "\n",
    "df['Abstract'] = df['Abstract'].apply(lambda x: tokenize(x))\n",
    "df.iloc[0]['Abstract']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['use',\n",
       " 'critical',\n",
       " 'point',\n",
       " 'analysis',\n",
       " 'generate',\n",
       " 'representations',\n",
       " 'vector',\n",
       " 'field',\n",
       " 'topology',\n",
       " 'numerical',\n",
       " 'flow',\n",
       " 'data',\n",
       " 'sets',\n",
       " 'discussed',\n",
       " 'critical',\n",
       " 'points',\n",
       " 'located',\n",
       " 'characterized',\n",
       " 'twodimensional',\n",
       " 'domain',\n",
       " 'may',\n",
       " 'either',\n",
       " 'twodimensional',\n",
       " 'flow',\n",
       " 'field',\n",
       " 'tangential',\n",
       " 'velocity',\n",
       " 'field',\n",
       " 'near',\n",
       " 'threedimensional',\n",
       " 'body',\n",
       " 'tangent',\n",
       " 'curves',\n",
       " 'integrated',\n",
       " 'along',\n",
       " 'principal',\n",
       " 'directions',\n",
       " 'certain',\n",
       " 'classes',\n",
       " 'critical',\n",
       " 'points',\n",
       " 'points',\n",
       " 'curves',\n",
       " 'linked',\n",
       " 'form',\n",
       " 'skeleton',\n",
       " 'representing',\n",
       " 'twodimensional',\n",
       " 'vector',\n",
       " 'field',\n",
       " 'topology',\n",
       " 'generated',\n",
       " 'tangential',\n",
       " 'velocity',\n",
       " 'field',\n",
       " 'near',\n",
       " 'body',\n",
       " 'threedimensional',\n",
       " 'flow',\n",
       " 'skeleton',\n",
       " 'includes',\n",
       " 'critical',\n",
       " 'points',\n",
       " 'curves',\n",
       " 'provide',\n",
       " 'basis',\n",
       " 'analyzing',\n",
       " 'threedimensional',\n",
       " 'structure',\n",
       " 'flow',\n",
       " 'separation']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove stopwords (words that add little value to sentence)\n",
    "\n",
    "stopwords = nltk.corpus.stopwords.words('english')\n",
    "\n",
    "def remove_stopwords(tokenized_list):\n",
    "    text = [word for word in tokenized_list if word not in stopwords]\n",
    "    return text\n",
    "\n",
    "df['Abstract'] = df['Abstract'].apply(lambda x: remove_stopwords(x))\n",
    "df.iloc[0]['Abstract']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['use',\n",
       " 'critical',\n",
       " 'point',\n",
       " 'analysis',\n",
       " 'generate',\n",
       " 'representation',\n",
       " 'vector',\n",
       " 'field',\n",
       " 'topology',\n",
       " 'numerical',\n",
       " 'flow',\n",
       " 'data',\n",
       " 'set',\n",
       " 'discussed',\n",
       " 'critical',\n",
       " 'point',\n",
       " 'located',\n",
       " 'characterized',\n",
       " 'twodimensional',\n",
       " 'domain',\n",
       " 'may',\n",
       " 'either',\n",
       " 'twodimensional',\n",
       " 'flow',\n",
       " 'field',\n",
       " 'tangential',\n",
       " 'velocity',\n",
       " 'field',\n",
       " 'near',\n",
       " 'threedimensional',\n",
       " 'body',\n",
       " 'tangent',\n",
       " 'curve',\n",
       " 'integrated',\n",
       " 'along',\n",
       " 'principal',\n",
       " 'direction',\n",
       " 'certain',\n",
       " 'class',\n",
       " 'critical',\n",
       " 'point',\n",
       " 'point',\n",
       " 'curve',\n",
       " 'linked',\n",
       " 'form',\n",
       " 'skeleton',\n",
       " 'representing',\n",
       " 'twodimensional',\n",
       " 'vector',\n",
       " 'field',\n",
       " 'topology',\n",
       " 'generated',\n",
       " 'tangential',\n",
       " 'velocity',\n",
       " 'field',\n",
       " 'near',\n",
       " 'body',\n",
       " 'threedimensional',\n",
       " 'flow',\n",
       " 'skeleton',\n",
       " 'includes',\n",
       " 'critical',\n",
       " 'point',\n",
       " 'curve',\n",
       " 'provide',\n",
       " 'basis',\n",
       " 'analyzing',\n",
       " 'threedimensional',\n",
       " 'structure',\n",
       " 'flow',\n",
       " 'separation']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lemmatize words\n",
    "\n",
    "wn = nltk.WordNetLemmatizer()\n",
    "\n",
    "def lemmatizing(tokenized_text):\n",
    "    text = [wn.lemmatize(word) for word in tokenized_text]\n",
    "    return text\n",
    "\n",
    "df['Abstract'] = df['Abstract'].apply(lambda x: lemmatizing(x))\n",
    "df.iloc[0]['Abstract'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['use',\n",
       " 'critic',\n",
       " 'point',\n",
       " 'analysi',\n",
       " 'gener',\n",
       " 'represent',\n",
       " 'vector',\n",
       " 'field',\n",
       " 'topolog',\n",
       " 'numer',\n",
       " 'flow',\n",
       " 'data',\n",
       " 'set',\n",
       " 'discuss',\n",
       " 'critic',\n",
       " 'point',\n",
       " 'locat',\n",
       " 'character',\n",
       " 'twodimension',\n",
       " 'domain',\n",
       " 'may',\n",
       " 'either',\n",
       " 'twodimension',\n",
       " 'flow',\n",
       " 'field',\n",
       " 'tangenti',\n",
       " 'veloc',\n",
       " 'field',\n",
       " 'near',\n",
       " 'threedimension',\n",
       " 'bodi',\n",
       " 'tangent',\n",
       " 'curv',\n",
       " 'integr',\n",
       " 'along',\n",
       " 'princip',\n",
       " 'direct',\n",
       " 'certain',\n",
       " 'class',\n",
       " 'critic',\n",
       " 'point',\n",
       " 'point',\n",
       " 'curv',\n",
       " 'link',\n",
       " 'form',\n",
       " 'skeleton',\n",
       " 'repres',\n",
       " 'twodimension',\n",
       " 'vector',\n",
       " 'field',\n",
       " 'topolog',\n",
       " 'gener',\n",
       " 'tangenti',\n",
       " 'veloc',\n",
       " 'field',\n",
       " 'near',\n",
       " 'bodi',\n",
       " 'threedimension',\n",
       " 'flow',\n",
       " 'skeleton',\n",
       " 'includ',\n",
       " 'critic',\n",
       " 'point',\n",
       " 'curv',\n",
       " 'provid',\n",
       " 'basi',\n",
       " 'analyz',\n",
       " 'threedimension',\n",
       " 'structur',\n",
       " 'flow',\n",
       " 'separ']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Stem words\n",
    "\n",
    "ps = nltk.PorterStemmer()\n",
    "\n",
    "def stemming(tokenized_text):\n",
    "    text = [ps.stem(word) for word in tokenized_text]\n",
    "    return text\n",
    "\n",
    "df['Abstract'] = df['Abstract'].apply(lambda x: stemming(x))\n",
    "df.iloc[0]['Abstract']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many abstracts were presented each year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "</script>"
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      "text/plain": [
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "to_plot1 = df.groupby('Year').count().reset_index()\n",
    "alt.Chart(to_plot1).mark_line().encode(\n",
    "    x = \"Year:Q\",\n",
    "    y = \"Title:Q\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many total papers are in each track?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
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       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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       "      loadScript(\"vega\")\n",
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       "        .then(() => loadScript(\"vega-embed\"))\n",
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       "        .then(() => displayChart(vegaEmbed));\n",
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      "text/plain": [
       "alt.Chart(...)"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "to_plot2 = df.groupby('Conference').count().reset_index()\n",
    "\n",
    "alt.Chart(to_plot2).mark_bar().encode(\n",
    "    x = \"Conference:N\",\n",
    "    y = \"Abstract:Q\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many abstracts were presented by different tracks each year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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2020-01\": 56, \"PubsCited\": 56, \"Award\": 2}, {\"Conference\": \"Vis\", \"Year\": 2008, \"Title\": 50, \"DOI\": 50, \"Link\": 50, \"FirstPage\": 50, \"LastPage\": 50, \"PaperType\": 50, \"Abstract\": 50, \"AuthorNames-Deduped\": 50, \"AuthorNames\": 50, \"AuthorAffiliation\": 43, \"InternalReferences\": 47, \"AuthorKeywords\": 50, \"AminerCitationCount_02-2020\": 49, \"XploreCitationCount - 2020-01\": 50, \"PubsCited\": 50, \"Award\": 0}, {\"Conference\": \"Vis\", \"Year\": 2009, \"Title\": 54, \"DOI\": 54, \"Link\": 54, \"FirstPage\": 54, \"LastPage\": 54, \"PaperType\": 54, \"Abstract\": 54, \"AuthorNames-Deduped\": 54, \"AuthorNames\": 54, \"AuthorAffiliation\": 53, \"InternalReferences\": 54, \"AuthorKeywords\": 54, \"AminerCitationCount_02-2020\": 53, \"XploreCitationCount - 2020-01\": 54, \"PubsCited\": 54, \"Award\": 4}, {\"Conference\": \"Vis\", \"Year\": 2010, \"Title\": 48, \"DOI\": 48, \"Link\": 48, \"FirstPage\": 48, \"LastPage\": 48, \"PaperType\": 48, \"Abstract\": 48, \"AuthorNames-Deduped\": 48, \"AuthorNames\": 48, \"AuthorAffiliation\": 41, \"InternalReferences\": 47, \"AuthorKeywords\": 47, \"AminerCitationCount_02-2020\": 48, \"XploreCitationCount - 2020-01\": 48, \"PubsCited\": 48, \"Award\": 3}, {\"Conference\": \"Vis\", \"Year\": 2011, \"Title\": 49, \"DOI\": 49, \"Link\": 49, \"FirstPage\": 49, \"LastPage\": 49, \"PaperType\": 49, \"Abstract\": 49, \"AuthorNames-Deduped\": 49, \"AuthorNames\": 49, \"AuthorAffiliation\": 49, \"InternalReferences\": 48, \"AuthorKeywords\": 49, \"AminerCitationCount_02-2020\": 48, \"XploreCitationCount - 2020-01\": 49, \"PubsCited\": 49, \"Award\": 4}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_plot3 = df.groupby(['Conference', 'Year']).count().reset_index()\n",
    "alt.Chart(to_plot3,height=150).mark_line().encode(\n",
    "    x = \"Year:Q\",\n",
    "    y = \"Title:Q\",\n",
    "    color = 'Conference:N'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TF-IDF Values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We generate tf-idf values for all the words in the abstract to evaluate how relevant a word is to the document."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_tfidf(input_data):\n",
    "    # Create Dictionary\n",
    "    id2word = corpora.Dictionary(input_data)\n",
    "    model = TfIdfTransformer(dictionary=id2word)\n",
    "    \n",
    "    # Create Corpus: Term Document Frequency\n",
    "    corpus = [id2word.doc2bow(text) for text in input_data]\n",
    "    num_docs = id2word.num_docs\n",
    "    num_terms = len(id2word.keys())\n",
    "    \n",
    "    # Get tfidf matrix\n",
    "    tfidf_corpus = model.fit_transform(corpus)\n",
    "    corpus_tfidf_dense = corpus2dense(tfidf_corpus, num_terms, num_docs)\n",
    "    \n",
    "    # Map index back to word\n",
    "    words = []\n",
    "    for i in id2word.keys():\n",
    "        words.append(id2word[i])\n",
    "    mat = pd.DataFrame(data=corpus_tfidf_dense, index=words)\n",
    "    wordtfidf = pd.DataFrame(data=mat.values.T, columns=mat.index)\n",
    "    return wordtfidf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We cluster the words based on their tf-idf values to group words into four different clusters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_df = pd.DataFrame(get_tfidf(df['Abstract']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_model = KMeans(n_clusters=4)\n",
    "cluster = cluster_model.fit(tfidf_df)\n",
    "tfidf_cluster_df = pd.DataFrame(tfidf_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# top 10 words for each cluster\n",
    "\n",
    "group_key_words = []\n",
    "for label in range(4):\n",
    "    # get the sum tf-idf for each word, do sum() across rows for each column\n",
    "    group_df = tfidf_cluster_df[cluster.labels_ == label].mean(axis=0)\n",
    "    # sort the tf-idf values\n",
    "    to_sort = [{'freq': group_df[x], 'word': x} for x in group_df.index]\n",
    "    to_sort = sorted(to_sort, key=lambda d: d['freq'], reverse=True)\n",
    "    # add the words to the list\n",
    "    for i in range(10):\n",
    "        group_key_words.append([label, to_sort[i]['word'], to_sort[i]['freq']])\n",
    "keyword_df = pd.DataFrame(data=group_key_words, columns=['label', 'keyword', 'tfidf'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "\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>label</th>\n",
       "      <th>keyword</th>\n",
       "      <th>tfidf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>volum</td>\n",
       "      <td>0.054995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>render</td>\n",
       "      <td>0.053144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>surfac</td>\n",
       "      <td>0.050781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>mesh</td>\n",
       "      <td>0.038430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>algorithm</td>\n",
       "      <td>0.033684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>isosurfac</td>\n",
       "      <td>0.029684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>function</td>\n",
       "      <td>0.026991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>imag</td>\n",
       "      <td>0.025695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>method</td>\n",
       "      <td>0.024374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>textur</td>\n",
       "      <td>0.023339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>network</td>\n",
       "      <td>0.121592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>graph</td>\n",
       "      <td>0.112911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1</td>\n",
       "      <td>layout</td>\n",
       "      <td>0.069647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>node</td>\n",
       "      <td>0.065428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1</td>\n",
       "      <td>tree</td>\n",
       "      <td>0.043202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>edg</td>\n",
       "      <td>0.038365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1</td>\n",
       "      <td>social</td>\n",
       "      <td>0.028289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>diagram</td>\n",
       "      <td>0.024861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "      <td>cluster</td>\n",
       "      <td>0.024726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>1</td>\n",
       "      <td>hierarchi</td>\n",
       "      <td>0.024099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>flow</td>\n",
       "      <td>0.111234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2</td>\n",
       "      <td>field</td>\n",
       "      <td>0.094830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2</td>\n",
       "      <td>vector</td>\n",
       "      <td>0.072276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>tensor</td>\n",
       "      <td>0.059319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>vortex</td>\n",
       "      <td>0.043743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2</td>\n",
       "      <td>particl</td>\n",
       "      <td>0.040642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2</td>\n",
       "      <td>line</td>\n",
       "      <td>0.036412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>streamlin</td>\n",
       "      <td>0.032157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2</td>\n",
       "      <td>topolog</td>\n",
       "      <td>0.031022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2</td>\n",
       "      <td>method</td>\n",
       "      <td>0.030160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3</td>\n",
       "      <td>data</td>\n",
       "      <td>0.022269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3</td>\n",
       "      <td>system</td>\n",
       "      <td>0.021512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>user</td>\n",
       "      <td>0.021295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>3</td>\n",
       "      <td>design</td>\n",
       "      <td>0.019142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>3</td>\n",
       "      <td>analysi</td>\n",
       "      <td>0.018746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>3</td>\n",
       "      <td>model</td>\n",
       "      <td>0.018627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>3</td>\n",
       "      <td>inform</td>\n",
       "      <td>0.017307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>3</td>\n",
       "      <td>interact</td>\n",
       "      <td>0.017093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3</td>\n",
       "      <td>tool</td>\n",
       "      <td>0.015857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>3</td>\n",
       "      <td>explor</td>\n",
       "      <td>0.015698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    label    keyword     tfidf\n",
       "0       0      volum  0.054995\n",
       "1       0     render  0.053144\n",
       "2       0     surfac  0.050781\n",
       "3       0       mesh  0.038430\n",
       "4       0  algorithm  0.033684\n",
       "5       0  isosurfac  0.029684\n",
       "6       0   function  0.026991\n",
       "7       0       imag  0.025695\n",
       "8       0     method  0.024374\n",
       "9       0     textur  0.023339\n",
       "10      1    network  0.121592\n",
       "11      1      graph  0.112911\n",
       "12      1     layout  0.069647\n",
       "13      1       node  0.065428\n",
       "14      1       tree  0.043202\n",
       "15      1        edg  0.038365\n",
       "16      1     social  0.028289\n",
       "17      1    diagram  0.024861\n",
       "18      1    cluster  0.024726\n",
       "19      1  hierarchi  0.024099\n",
       "20      2       flow  0.111234\n",
       "21      2      field  0.094830\n",
       "22      2     vector  0.072276\n",
       "23      2     tensor  0.059319\n",
       "24      2     vortex  0.043743\n",
       "25      2    particl  0.040642\n",
       "26      2       line  0.036412\n",
       "27      2  streamlin  0.032157\n",
       "28      2    topolog  0.031022\n",
       "29      2     method  0.030160\n",
       "30      3       data  0.022269\n",
       "31      3     system  0.021512\n",
       "32      3       user  0.021295\n",
       "33      3     design  0.019142\n",
       "34      3    analysi  0.018746\n",
       "35      3      model  0.018627\n",
       "36      3     inform  0.017307\n",
       "37      3   interact  0.017093\n",
       "38      3       tool  0.015857\n",
       "39      3     explor  0.015698"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyword_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "chart = alt.hconcat()\n",
    "\n",
    "for label in range(4):\n",
    "    chart |= alt.Chart(keyword_df[keyword_df['label']==label]).mark_bar().encode(\n",
    "        x=alt.X('tfidf:Q', scale=alt.Scale(domain=[0,0.12]),title='cluster'+str(label)),\n",
    "        y=alt.Y('keyword:N', sort='-x')\n",
    "    ).properties(\n",
    "        width = 50\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"hconcat\": [{\"mark\": \"bar\", \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"tfidf\", \"scale\": {\"domain\": [0, 0.12]}, \"title\": \"cluster0\"}, \"y\": {\"type\": \"nominal\", \"field\": \"keyword\", \"sort\": \"-x\"}}, \"width\": 50}, {\"data\": {\"name\": \"data-938d456139bc0920ef0059232199750c\"}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"tfidf\", \"scale\": {\"domain\": [0, 0.12]}, \"title\": \"cluster1\"}, \"y\": {\"type\": \"nominal\", \"field\": \"keyword\", \"sort\": \"-x\"}}, \"width\": 50}, {\"data\": {\"name\": \"data-5f7171f2ab4a562412931fba59bb5ab5\"}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"tfidf\", \"scale\": {\"domain\": [0, 0.12]}, \"title\": \"cluster2\"}, \"y\": {\"type\": \"nominal\", \"field\": \"keyword\", \"sort\": \"-x\"}}, \"width\": 50}, {\"data\": {\"name\": \"data-07f1447c3a034a3d4e2792e978e73cb9\"}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"type\": \"quantitative\", \"field\": \"tfidf\", \"scale\": {\"domain\": [0, 0.12]}, \"title\": \"cluster3\"}, \"y\": {\"type\": \"nominal\", \"field\": \"keyword\", \"sort\": \"-x\"}}, \"width\": 50}], \"data\": {\"name\": \"data-e4f11219ad9173c433f0364628c4401c\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.8.1.json\", \"datasets\": {\"data-e4f11219ad9173c433f0364628c4401c\": [{\"label\": 0, \"keyword\": \"volum\", \"tfidf\": 0.05499512329697609}, {\"label\": 0, \"keyword\": \"render\", \"tfidf\": 0.05314435064792633}, {\"label\": 0, \"keyword\": \"surfac\", \"tfidf\": 0.05078074336051941}, {\"label\": 0, \"keyword\": \"mesh\", \"tfidf\": 0.03843007609248161}, {\"label\": 0, \"keyword\": \"algorithm\", \"tfidf\": 0.0336838960647583}, {\"label\": 0, \"keyword\": \"isosurfac\", \"tfidf\": 0.02968396246433258}, {\"label\": 0, \"keyword\": \"function\", \"tfidf\": 0.026990901678800583}, {\"label\": 0, \"keyword\": \"imag\", \"tfidf\": 0.02569502592086792}, {\"label\": 0, \"keyword\": \"method\", \"tfidf\": 0.02437366358935833}, {\"label\": 0, \"keyword\": \"textur\", \"tfidf\": 0.02333897538483143}], \"data-938d456139bc0920ef0059232199750c\": [{\"label\": 1, \"keyword\": \"network\", \"tfidf\": 0.12159224599599838}, {\"label\": 1, \"keyword\": \"graph\", \"tfidf\": 0.11291135102510452}, {\"label\": 1, \"keyword\": \"layout\", \"tfidf\": 0.06964654475450516}, {\"label\": 1, \"keyword\": \"node\", \"tfidf\": 0.06542763113975525}, {\"label\": 1, \"keyword\": \"tree\", \"tfidf\": 0.043201979249715805}, {\"label\": 1, \"keyword\": \"edg\", \"tfidf\": 0.03836466744542122}, {\"label\": 1, \"keyword\": \"social\", \"tfidf\": 0.02828940935432911}, {\"label\": 1, \"keyword\": \"diagram\", \"tfidf\": 0.02486104890704155}, {\"label\": 1, \"keyword\": \"cluster\", \"tfidf\": 0.02472580410540104}, {\"label\": 1, \"keyword\": \"hierarchi\", \"tfidf\": 0.02409895695745945}], \"data-5f7171f2ab4a562412931fba59bb5ab5\": [{\"label\": 2, \"keyword\": \"flow\", \"tfidf\": 0.11123397201299667}, {\"label\": 2, \"keyword\": \"field\", \"tfidf\": 0.09483018517494202}, {\"label\": 2, \"keyword\": \"vector\", \"tfidf\": 0.07227638363838196}, {\"label\": 2, \"keyword\": \"tensor\", \"tfidf\": 0.059318527579307556}, {\"label\": 2, \"keyword\": \"vortex\", \"tfidf\": 0.04374280199408531}, {\"label\": 2, \"keyword\": \"particl\", \"tfidf\": 0.04064161702990532}, {\"label\": 2, \"keyword\": \"line\", \"tfidf\": 0.03641170635819435}, {\"label\": 2, \"keyword\": \"streamlin\", \"tfidf\": 0.0321565680205822}, {\"label\": 2, \"keyword\": \"topolog\", \"tfidf\": 0.03102167323231697}, {\"label\": 2, \"keyword\": \"method\", \"tfidf\": 0.030159691348671913}], \"data-07f1447c3a034a3d4e2792e978e73cb9\": [{\"label\": 3, \"keyword\": \"data\", \"tfidf\": 0.02226928435266018}, {\"label\": 3, \"keyword\": \"system\", \"tfidf\": 0.021512452512979507}, {\"label\": 3, \"keyword\": \"user\", \"tfidf\": 0.02129482850432396}, {\"label\": 3, \"keyword\": \"design\", \"tfidf\": 0.019141951575875282}, {\"label\": 3, \"keyword\": \"analysi\", \"tfidf\": 0.018745863810181618}, {\"label\": 3, \"keyword\": \"model\", \"tfidf\": 0.018627343699336052}, {\"label\": 3, \"keyword\": \"inform\", \"tfidf\": 0.01730719767510891}, {\"label\": 3, \"keyword\": \"interact\", \"tfidf\": 0.017093222588300705}, {\"label\": 3, \"keyword\": \"tool\", \"tfidf\": 0.015856754034757614}, {\"label\": 3, \"keyword\": \"explor\", \"tfidf\": 0.015698300674557686}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Track Topics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create four documents for each track and add all the words from papers in the track to each document. After, we get tf-idf values for all the words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create four documents for each track and add all words from papers in the track to each document\n",
    "\n",
    "track_key_words = []\n",
    "tracks = ['Vis', 'InfoVis', 'VAST', 'SciVis']\n",
    "\n",
    "for track in tracks:\n",
    "    document = []\n",
    "    track_index = df[df['Conference']==track].index.values.astype(int)\n",
    "    for i in track_index:\n",
    "        document.extend(df['Abstract'][i])\n",
    "    track_key_words.append(document)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "execution_count": 22,
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   "outputs": [],
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    "track_tfidf = get_tfidf(track_key_words)"
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   "source": [
    "track_tfidf"
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   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "track_tfidf['Track'] = tracks"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After grouping the words into the different tracks and retrieving their tf-idf values, we find the top ten words in each track by the words with the highest tf-idf values in each track."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-26-65fbb6ebd2f8>:6: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  group_df = track_tfidf[track_tfidf['Track'] == track].mean(axis=0)\n"
     ]
    }
   ],
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    "# Top 10 words for each track\n",
    "\n",
    "group_key_words = []\n",
    "for track in tracks:\n",
    "    # get the sum tf-idf for each word, do sum() across rows for each column\n",
    "    group_df = track_tfidf[track_tfidf['Track'] == track].mean(axis=0)\n",
    "    # sort the tf-idf values\n",
    "    to_sort = [{'freq': group_df[x], 'word': x} for x in group_df.index]\n",
    "    to_sort = sorted(to_sort, key=lambda d: d['freq'], reverse=True)\n",
    "    # add the words to the list\n",
    "    for i in range(10):\n",
    "        group_key_words.append([track, to_sort[i]['word'], to_sort[i]['freq']])\n",
    "keyword_df = pd.DataFrame(data=group_key_words, columns=['track', 'keyword', 'tfidf'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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       "\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>track</th>\n",
       "      <th>keyword</th>\n",
       "      <th>tfidf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Vis</td>\n",
       "      <td>isosurfac</td>\n",
       "      <td>0.517898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Vis</td>\n",
       "      <td>textur</td>\n",
       "      <td>0.242682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Vis</td>\n",
       "      <td>ray</td>\n",
       "      <td>0.232033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Vis</td>\n",
       "      <td>vortex</td>\n",
       "      <td>0.180058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Vis</td>\n",
       "      <td>spl</td>\n",
       "      <td>0.170777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Vis</td>\n",
       "      <td>tetrahedr</td>\n",
       "      <td>0.126226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Vis</td>\n",
       "      <td>projector</td>\n",
       "      <td>0.120657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Vis</td>\n",
       "      <td>lod</td>\n",
       "      <td>0.107663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Vis</td>\n",
       "      <td>unsteadi</td>\n",
       "      <td>0.096526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Vis</td>\n",
       "      <td>viewdepend</td>\n",
       "      <td>0.096526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>InfoVis</td>\n",
       "      <td>nodelink</td>\n",
       "      <td>0.236616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>InfoVis</td>\n",
       "      <td>treemap</td>\n",
       "      <td>0.166762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>InfoVis</td>\n",
       "      <td>item</td>\n",
       "      <td>0.150086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>InfoVis</td>\n",
       "      <td>lineup</td>\n",
       "      <td>0.116076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>InfoVis</td>\n",
       "      <td>stori</td>\n",
       "      <td>0.116076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>InfoVis</td>\n",
       "      <td>dashboard</td>\n",
       "      <td>0.107147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>InfoVis</td>\n",
       "      <td>sketchi</td>\n",
       "      <td>0.107147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>InfoVis</td>\n",
       "      <td>storytel</td>\n",
       "      <td>0.098218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>InfoVis</td>\n",
       "      <td>social</td>\n",
       "      <td>0.098205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>InfoVis</td>\n",
       "      <td>infovi</td>\n",
       "      <td>0.092646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>VAST</td>\n",
       "      <td>sensemak</td>\n",
       "      <td>0.327953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>VAST</td>\n",
       "      <td>social</td>\n",
       "      <td>0.249540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>VAST</td>\n",
       "      <td>spinel</td>\n",
       "      <td>0.134545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>VAST</td>\n",
       "      <td>egonetwork</td>\n",
       "      <td>0.117727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>VAST</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.109318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>VAST</td>\n",
       "      <td>jigsaw</td>\n",
       "      <td>0.109318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>VAST</td>\n",
       "      <td>patent</td>\n",
       "      <td>0.109318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>VAST</td>\n",
       "      <td>financi</td>\n",
       "      <td>0.096704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>VAST</td>\n",
       "      <td>transact</td>\n",
       "      <td>0.096704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>VAST</td>\n",
       "      <td>twitter</td>\n",
       "      <td>0.092499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>SciVis</td>\n",
       "      <td>vortex</td>\n",
       "      <td>0.250046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>SciVis</td>\n",
       "      <td>aneurysm</td>\n",
       "      <td>0.187534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>SciVis</td>\n",
       "      <td>ray</td>\n",
       "      <td>0.159751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>SciVis</td>\n",
       "      <td>isosurfac</td>\n",
       "      <td>0.152806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>SciVis</td>\n",
       "      <td>mobject</td>\n",
       "      <td>0.138914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>SciVis</td>\n",
       "      <td>stent</td>\n",
       "      <td>0.138914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>SciVis</td>\n",
       "      <td>inerti</td>\n",
       "      <td>0.125023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>SciVis</td>\n",
       "      <td>corelin</td>\n",
       "      <td>0.125023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>SciVis</td>\n",
       "      <td>massless</td>\n",
       "      <td>0.125023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>SciVis</td>\n",
       "      <td>astrocyt</td>\n",
       "      <td>0.111131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      track     keyword     tfidf\n",
       "0       Vis   isosurfac  0.517898\n",
       "1       Vis      textur  0.242682\n",
       "2       Vis         ray  0.232033\n",
       "3       Vis      vortex  0.180058\n",
       "4       Vis         spl  0.170777\n",
       "5       Vis   tetrahedr  0.126226\n",
       "6       Vis   projector  0.120657\n",
       "7       Vis         lod  0.107663\n",
       "8       Vis    unsteadi  0.096526\n",
       "9       Vis  viewdepend  0.096526\n",
       "10  InfoVis    nodelink  0.236616\n",
       "11  InfoVis     treemap  0.166762\n",
       "12  InfoVis        item  0.150086\n",
       "13  InfoVis      lineup  0.116076\n",
       "14  InfoVis       stori  0.116076\n",
       "15  InfoVis   dashboard  0.107147\n",
       "16  InfoVis     sketchi  0.107147\n",
       "17  InfoVis    storytel  0.098218\n",
       "18  InfoVis      social  0.098205\n",
       "19  InfoVis      infovi  0.092646\n",
       "20     VAST    sensemak  0.327953\n",
       "21     VAST      social  0.249540\n",
       "22     VAST      spinel  0.134545\n",
       "23     VAST  egonetwork  0.117727\n",
       "24     VAST        2009  0.109318\n",
       "25     VAST      jigsaw  0.109318\n",
       "26     VAST      patent  0.109318\n",
       "27     VAST     financi  0.096704\n",
       "28     VAST    transact  0.096704\n",
       "29     VAST     twitter  0.092499\n",
       "30   SciVis      vortex  0.250046\n",
       "31   SciVis    aneurysm  0.187534\n",
       "32   SciVis         ray  0.159751\n",
       "33   SciVis   isosurfac  0.152806\n",
       "34   SciVis     mobject  0.138914\n",
       "35   SciVis       stent  0.138914\n",
       "36   SciVis      inerti  0.125023\n",
       "37   SciVis     corelin  0.125023\n",
       "38   SciVis    massless  0.125023\n",
       "39   SciVis    astrocyt  0.111131"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keyword_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "chart = alt.hconcat()\n",
    "\n",
    "for track in tracks:\n",
    "    chart |= alt.Chart(keyword_df[keyword_df['track']== track], height=150).mark_bar().encode(\n",
    "    x=alt.X('tfidf:Q', scale=alt.Scale(domain=[0,0.53]),title=track),\n",
    "    y=alt.Y('keyword:N', sort='-x')\n",
    ").properties(\n",
    "    width = 50\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      outputDiv = document.getElementById(\"altair-viz-eb919402563642baa656874ef8feae2f\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.8.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
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       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else if (typeof vegaEmbed === \"function\") {\n",
       "      displayChart(vegaEmbed);\n",
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       "        .then(() => loadScript(\"vega-lite\"))\n",
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       "</script>"
      ],
      "text/plain": [
       "alt.HConcatChart(...)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart"
   ]
  }
 ],
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