3_text_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](images/mk.png)\n",
    "\n",
    "<h1><center>ONLINE SUPPLEMENT</center></h1>\n",
    "\n",
    "John McLevey & Reid-McIlroy-Young. **Introducing *metaknowledge*: Software for Computational Research in Information Science, Science of Science, and Network Analysis.** *Journal of Informetrics*. XX(XX):XX-XX.\n",
    "\n",
    "<h1><center>Part 3: Text Analysis</center></h1>\n",
    "\n",
    "This supplementary notebook was prepared by [Steve McColl](http://networkslab.org/) (NetLab, University of Waterloo), Dr. [John McLevey](http://www.johnmclevey.com/) (University of Waterloo), and [Reid McIlroy-Young](http://reidmcy.com/) (University of Chicago). The code in this notebook is current as of *metaknowledge* version 3.1.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Program Files\\Anaconda3\\lib\\site-packages\\gensim\\utils.py:840: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n",
      "c:\\Program Files\\Anaconda3\\lib\\site-packages\\gensim\\utils.py:1015: UserWarning: Pattern library is not installed, lemmatization won't be available.\n",
      "  warnings.warn(\"Pattern library is not installed, lemmatization won't be available.\")\n",
      "c:\\Program Files\\Anaconda3\\lib\\site-packages\\funcy\\decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "c:\\Program Files\\Anaconda3\\lib\\site-packages\\funcy\\decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "c:\\Program Files\\Anaconda3\\lib\\site-packages\\funcy\\decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "c:\\Program Files\\Anaconda3\\lib\\site-packages\\funcy\\decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "c:\\Program Files\\Anaconda3\\lib\\site-packages\\funcy\\decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n",
      "c:\\Program Files\\Anaconda3\\lib\\site-packages\\funcy\\decorators.py:56: DeprecationWarning: inspect.getargspec() is deprecated, use inspect.signature() instead\n",
      "  spec = inspect.getargspec(func)\n"
     ]
    }
   ],
   "source": [
    "import metaknowledge as mk\n",
    "from stop_words import get_stop_words\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "import seaborn as sns\n",
    "import numpy\n",
    "import matplotlib as plt\n",
    "import pandas\n",
    "import os\n",
    "\n",
    "# Imports for gensim.\n",
    "import gensim\n",
    "from gensim import corpora, models\n",
    "\n",
    "# Imports for pyLDAvis.\n",
    "import pyLDAvis.gensim as gensimvis\n",
    "import pyLDAvis\n",
    "\n",
    "# Imports for sklearn.\n",
    "from __future__ import print_function\n",
    "import sklearn\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn.decomposition import NMF, LatentDirichletAllocation\n",
    "\n",
    "sns.set_style(style=\"white\") # change the default background plot colour\n",
    "sns.set(font_scale=.7)\n",
    "\n",
    "plt.rc(\"savefig\", dpi=300) # improve default resolution of graphics\n",
    "\n",
    "os.chdir('.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "RC = mk.RecordCollection('raw_data/imetrics/', cached = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic Models\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Create topic models using the Gensim package or Scikit-Learn.\n",
    "* Create interactive visualizations using Gensim models and pyLDAvis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Transform the record collection into a format for use with natural language processing applications.\n",
    "raw = RC.forNLP('generated_datasets/topic_model/topic_model.csv', lower=True, removeNumbers=True,\n",
    "         removeNonWords=True, removeWhitespace=True, extractCopyright=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Conver the raw text into a list.\n",
    "documents = raw['abstract']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SKLearn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Basic topic models using code from \n",
    "[this tutorial](http://scikit-learn.org/stable/auto_examples/applications/topics_extraction_with_nmf_lda.html) written by Olivier Grisel, Lars Buitinck, and Chyi-Kwei Yau."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For use with SKlearn, convert the raw text to a numpy array.\n",
    "docs = numpy.asarray(documents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Increasing the number of features will give a better model, but it may increase the runtime.\n",
    "features = 1000\n",
    "topics = 50\n",
    "top_words = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Initialize the tokenizer.\n",
    "tfidf_vectorizer = TfidfVectorizer(max_df=0.95, min_df=2,\n",
    "                                   max_features=features,\n",
    "                                   stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Tokenize the documents.\n",
    "tfidf = tfidf_vectorizer.fit_transform(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define the output function, code exactly as shown in tutorial.\n",
    "def print_top_words(model, feature_names, top_words):\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        print(\"Topic #%d:\" % topic_idx)\n",
    "        print(\" \".join([feature_names[i]\n",
    "                        for i in topic.argsort()[:-top_words - 1:-1]]))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Extract the features (tokens) for the models.\n",
    "tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2,\n",
    "                                max_features=features,\n",
    "                                stop_words='english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tf = tf_vectorizer.fit_transform(docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Fit the Non-negative matrix factorization model.\n",
    "nmf = NMF(n_components=topics, random_state=1, alpha=.1, l1_ratio=.5).fit(tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #0:\n",
      "research funding institutions output bibliometric analysis areas field institutes groups\n",
      "Topic #1:\n",
      "information seeking behavior use systems article health retrieval study needs\n",
      "Topic #2:\n",
      "journal jif factor subject categories journals jcr reports isi editors\n",
      "Topic #3:\n",
      "journals published oa publishing international jcr editorial open access sci\n",
      "Topic #4:\n",
      "patent patents technological patenting companies applications value relationship market paper\n",
      "Topic #5:\n",
      "chinese china database india international nanotechnology usa japan english words\n",
      "Topic #6:\n",
      "web pages sites site links page link content engines websites\n",
      "Topic #7:\n",
      "impact factor factors average measure isi influence year higher metrics\n",
      "Topic #8:\n",
      "papers published cited paper highly physics average years proportion coauthored\n",
      "Topic #9:\n",
      "network networks centrality coauthorship structure social degree analysis properties structural\n",
      "Topic #10:\n",
      "collaboration international collaborative coauthorship collaborations domestic patterns coauthored institutions national\n",
      "Topic #11:\n",
      "search engines engine searching queries results searchers searches query tasks\n",
      "Topic #12:\n",
      "knowledge management diffusion organizations transfer creation production organization development structure\n",
      "Topic #13:\n",
      "countries international country european eu output usa national cooperation developing\n",
      "Topic #14:\n",
      "model models growth process theory proposed theoretical empirical based rate\n",
      "Topic #15:\n",
      "social sciences humanities media disciplines book natural life online tagging\n",
      "Topic #16:\n",
      "hindex gindex researcher proposed indices original shown ranked single cases\n",
      "Topic #17:\n",
      "authors author authorship cocitation productivity selfcitations names coauthors productive different\n",
      "Topic #18:\n",
      "performance indicator differences using assessment italian evaluation better relative individual\n",
      "Topic #19:\n",
      "publications publication period output growth years study share total productivity\n",
      "Topic #20:\n",
      "indicators bibliometric indicator used level scientometric assessment national set paper\n",
      "Topic #21:\n",
      "articles published cited article year references years period keywords highly\n",
      "Topic #22:\n",
      "scientific production output community productivity new brazilian evolution activity country\n",
      "Topic #23:\n",
      "measures measure similarity new centrality concentration degree association properties used\n",
      "Topic #24:\n",
      "science computer scientometrics policy disciplines maps map growth library field\n",
      "Topic #25:\n",
      "databases database google scopus scholar wos coverage records bibliographic isi\n",
      "Topic #26:\n",
      "number total average increase function correlation years items coauthors large\n",
      "Topic #27:\n",
      "universities university academic education higher departments patenting institutions institutional national\n",
      "Topic #28:\n",
      "scientists work productivity working female career women professional different individual\n",
      "Topic #29:\n",
      "documents document text indexing words collection algorithm classification automatic relevant\n",
      "Topic #30:\n",
      "ranking rankings rank institutions scores based ranked results world algorithms\n",
      "Topic #31:\n",
      "quality evaluation review peer criteria assessment quantity bias process evaluations\n",
      "Topic #32:\n",
      "distribution law distributions power function productivity fit frequency statistical informetrics\n",
      "Topic #33:\n",
      "fields field physics different differences sciences chemistry mathematics interdisciplinary subfields\n",
      "Topic #34:\n",
      "citation counts cited publication citing rates reference highly analysis distributions\n",
      "Topic #35:\n",
      "researchers productivity researcher individual differences academic funding characteristics results gender\n",
      "Topic #36:\n",
      "users behavior privacy online tasks relevance use task community interaction\n",
      "Topic #37:\n",
      "index indices output new sci proposed indexes citation values researcher\n",
      "Topic #38:\n",
      "method methods proposed used clustering evaluation clusters new based propose\n",
      "Topic #39:\n",
      "analysis study approach results using different used use studies new\n",
      "Topic #40:\n",
      "digital library libraries internet use resources access services public electronic\n",
      "Topic #41:\n",
      "image images tags indexing tagging retrieval metadata visual features attributes\n",
      "Topic #42:\n",
      "innovation rd growth firms technological industry activities policy economic paper\n",
      "Topic #43:\n",
      "technology technological technologies development emerging patents diffusion st field firms\n",
      "Topic #44:\n",
      "citations received cited citing selfcitations effect year paper google references\n",
      "Topic #45:\n",
      "topics topic keywords emerging evolution communities identify trends field dynamic\n",
      "Topic #46:\n",
      "data sets collected set sources collection using bibliographic available processing\n",
      "Topic #47:\n",
      "user design interface systems satisfaction tools characteristics interaction context study\n",
      "Topic #48:\n",
      "rights elsevier reserved indices informetrics gindex paper way new based\n",
      "Topic #49:\n",
      "retrieval query queries relevance ir terms term language effectiveness approach\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the list of topics and their contents. \n",
    "# Note that the constraints can be modified by changing the number of topics or number of words in each topic.\n",
    "tfidf_feature_names = tfidf_vectorizer.get_feature_names()\n",
    "print_top_words(nmf, tfidf_feature_names, top_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LatentDirichletAllocation(batch_size=128, doc_topic_prior=None,\n",
       "             evaluate_every=-1, learning_decay=0.7,\n",
       "             learning_method='online', learning_offset=50.0,\n",
       "             max_doc_update_iter=100, max_iter=5, mean_change_tol=0.001,\n",
       "             n_jobs=1, n_topics=50, perp_tol=0.1, random_state=0,\n",
       "             topic_word_prior=None, total_samples=1000000.0, verbose=0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract topics for the LDA model, and fit the model.\n",
    "lda = LatentDirichletAllocation(n_topics=topics, max_iter=5,\n",
    "                                learning_method='online',\n",
    "                                learning_offset=50.,\n",
    "                                random_state=0)\n",
    "lda.fit(tf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #0:\n",
      "patent technology patents technological technologies china analysis development study nanotechnology\n",
      "Topic #1:\n",
      "evaluation quality criteria design environment assessment evaluations organization visualization tools\n",
      "Topic #2:\n",
      "practices discussion history historical ways cultural visual ideas reasons nature\n",
      "Topic #3:\n",
      "method approach based performance using results used measures proposed classification\n",
      "Topic #4:\n",
      "network networks structure analysis centrality structures coauthorship bibliographic study degree\n",
      "Topic #5:\n",
      "data groups sets size set scale statistical sample collected large\n",
      "Topic #6:\n",
      "hierarchical published recall lack applying impacts resulting decision projects universities\n",
      "Topic #7:\n",
      "search interface users tasks information features health models patent group\n",
      "Topic #8:\n",
      "search users user results information task tasks web study searching\n",
      "Topic #9:\n",
      "productivity academic publication publications publishing differences scholars scientific countries conference\n",
      "Topic #10:\n",
      "model ranking rankings empirical scores random test theoretical reliability lists\n",
      "Topic #11:\n",
      "citation impact journals journal citations cited factor references indicators science\n",
      "Topic #12:\n",
      "growth fields physics influence diffusion science subfields scientific communities mathematics\n",
      "Topic #13:\n",
      "social communication community online members media communities reviews evidence interaction\n",
      "Topic #14:\n",
      "databases database scientists science records scopus wos bibliographic departments biomedical\n",
      "Topic #15:\n",
      "index selfcitations indexes citation defined new citations distribution account received\n",
      "Topic #16:\n",
      "hindex elsevier rights reserved indices google scholar gindex citation indicators\n",
      "Topic #17:\n",
      "library information digital internet resources project libraries lis use electronic\n",
      "Topic #18:\n",
      "research articles published publications journals study period science countries output\n",
      "Topic #19:\n",
      "publishing databases technical literature studies scholarly world major consistent researchers\n",
      "Topic #20:\n",
      "outputs society status complex assessment robust scope environmental impact generate\n",
      "Topic #21:\n",
      "articles science publication university citations based universities health cited women\n",
      "Topic #22:\n",
      "web topics content topic pages public news analysis names sites\n",
      "Topic #23:\n",
      "documents retrieval query document relevance queries terms information ir relevant\n",
      "Topic #24:\n",
      "universities performance innovation rd economic university activities indicators industry european\n",
      "Topic #25:\n",
      "health gender medical women female editorial material environmental presence differences\n",
      "Topic #26:\n",
      "science web literature article factor examine recently indices citation hindex\n",
      "Topic #27:\n",
      "domain management relationships organizations organizational personal software experts entities expert\n",
      "Topic #28:\n",
      "values editors lower items quality standards frequently generally journals ways\n",
      "Topic #29:\n",
      "term sci visibility indian terms long coverage nature impact respectively\n",
      "Topic #30:\n",
      "research scientific funding study different field development researchers scientists new\n",
      "Topic #31:\n",
      "science research bibliometric indicators field scientific paper analysis used policy\n",
      "Topic #32:\n",
      "links external total universities social number counting selected link services\n",
      "Topic #33:\n",
      "analysis methods cocitation author citation study clusters counting intellectual authors\n",
      "Topic #34:\n",
      "coauthors consistent robust rate empirical negative ability relationship science number\n",
      "Topic #35:\n",
      "research isi social sciences practices review proceedings reuters analysis year\n",
      "Topic #36:\n",
      "collaboration international countries papers chinese collaborative scientific country china national\n",
      "Topic #37:\n",
      "information study research use article model analysis behavior framework work\n",
      "Topic #38:\n",
      "language text words english languages word formal literature process processing\n",
      "Topic #39:\n",
      "subject indicator categories mean normalization score normalized comparisons citationbased used\n",
      "Topic #40:\n",
      "distribution distributions law function power items mathematical concentration model functions\n",
      "Topic #41:\n",
      "papers authors number citations author published average paper publications total\n",
      "Topic #42:\n",
      "sciences social humanities titles disciplines correlation natural books title life\n",
      "Topic #43:\n",
      "time systems change changes profiles periods early stage standards career\n",
      "Topic #44:\n",
      "knowledge researchers core research management relationship characteristics transfer mining data\n",
      "Topic #45:\n",
      "journal journals review peer faculty selection bias psychology quality validity\n",
      "Topic #46:\n",
      "initial similarity generated model articles profile names language pairs author\n",
      "Topic #47:\n",
      "articles citations models effect web article number regression model results\n",
      "Topic #48:\n",
      "links access open external oa scholarly link internal journals advantage\n",
      "Topic #49:\n",
      "semantic image indexing concepts metadata images tags objects relations terms\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Print the list of topics and their contents. \n",
    "# Note that the constraints can be modified by changing the number of topics or number of words in each topic.\n",
    "tf_feature_names = tf_vectorizer.get_feature_names()\n",
    "print_top_words(lda, tf_feature_names, top_words)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "\n",
    "## Gensim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* For model coefficients and more analysis options.\n",
    "* Code adapted from [the gensim tutorial](https://radimrehurek.com/gensim/tutorial.html) by Radim Řehůřek.\n",
    "* Visualizations created using pyLDAvis, using code from the [notebooks]('https://github.com/bmabey/pyLDAvis/tree/master/notebooks') by Ben Mabey."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Create a list of stopwords, using the stopwords package.\n",
    "stopwords = get_stop_words('en')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Initialize the tokenizer.\n",
    "tokenizer = RegexpTokenizer(r'\\w+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Initialize a list, we will save tokens here.\n",
    "tokens = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Iterate over the documents list, and tokenize and save each entry.\n",
    "for l in documents:\n",
    "    token = tokenizer.tokenize(l)\n",
    "    tokens.append(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Initialize a list, we will save cleaned tokens here.\n",
    "cleaned_tokens = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Keep tokens only if they do not appear in the list of stopwords.\n",
    "for l in tokens:\n",
    "    cleaned_tokens.append([i for i in l if not i in stopwords])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create dictionary from the cleaned tokens.\n",
    "dictionary = corpora.Dictionary(cleaned_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Convert the cleaned tokens into a numpy array.\n",
    "array = numpy.asarray(cleaned_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Train the corpus using the array, creating a bag-of-words that contains each word in the array.\n",
    "corpus = [dictionary.doc2bow(word) for word in array]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Generate the LDA model using gensim.\n",
    "ldamodel = gensim.models.ldamodel.LdaModel(corpus=corpus, num_topics=50, id2word = dictionary, passes=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(5,\n",
       "  '0.033*\"h\" + 0.029*\"index\" + 0.028*\"number\" + 0.023*\"author\" + 0.022*\"citations\"'),\n",
       " (41,\n",
       "  '0.047*\"chinese\" + 0.021*\"financial\" + 0.020*\"swedish\" + 0.019*\"english\" + 0.019*\"languages\"'),\n",
       " (33,\n",
       "  '0.075*\"research\" + 0.019*\"researchers\" + 0.014*\"performance\" + 0.014*\"universities\" + 0.014*\"university\"'),\n",
       " (1,\n",
       "  '0.026*\"analysis\" + 0.021*\"research\" + 0.015*\"science\" + 0.013*\"data\" + 0.012*\"study\"'),\n",
       " (4,\n",
       "  '0.051*\"image\" + 0.033*\"images\" + 0.032*\"indexing\" + 0.029*\"tags\" + 0.019*\"retrieval\"'),\n",
       " (10,\n",
       "  '0.022*\"hindices\" + 0.021*\"pubmed\" + 0.021*\"names\" + 0.019*\"citer\" + 0.018*\"biomedical\"'),\n",
       " (44,\n",
       "  '0.079*\"scientific\" + 0.052*\"production\" + 0.032*\"indicators\" + 0.023*\"spanish\" + 0.021*\"institutions\"'),\n",
       " (8,\n",
       "  '0.016*\"used\" + 0.014*\"resources\" + 0.013*\"language\" + 0.012*\"usage\" + 0.012*\"ontology\"'),\n",
       " (30,\n",
       "  '0.068*\"digital\" + 0.065*\"library\" + 0.046*\"libraries\" + 0.027*\"internet\" + 0.024*\"efficiency\"'),\n",
       " (12,\n",
       "  '0.025*\"countries\" + 0.023*\"papers\" + 0.023*\"publications\" + 0.022*\"research\" + 0.018*\"number\"')]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The format for printing 50 topics is not very good, so we can print a sample of 10 topics, with the top 5 words in the content.\n",
    "# You can change the number of topics and number of words as you wish\n",
    "ldamodel.print_topics(num_topics=10, num_words=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# You also have the option of saving the corpus, dictionary, and model.\n",
    "# MmCorpus.serialize('paper_abstracts.mm', corpus)\n",
    "dictionary.save('paper_abstracts.dict')\n",
    "ldamodel.save('paper_abstracts_lda.model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using the model created with Gensim for an pyLDAvis interactive visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Prepare the visualization data.\n",
    "vis_data = gensimvis.prepare(ldamodel, corpus, dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el614824385751033361142167351\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
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       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
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       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(LDAvis) !== \"undefined\"){\n",
       "   // already loaded: just create the visualization\n",
       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el614824385751033361142167351\", ldavis_el614824385751033361142167351_data);\n",
       "   }(LDAvis);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el614824385751033361142167351\", ldavis_el614824385751033361142167351_data);\n",
       "      });\n",
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       "}else{\n",
       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.js\", function(){\n",
       "                 new LDAvis(\"#\" + \"ldavis_el614824385751033361142167351\", ldavis_el614824385751033361142167351_data);\n",
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Visualize the topic model.\n",
    "pyLDAvis.display(vis_data)"
   ]
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