LDA_CovidD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This notebook aims to cluster topics in CORD-19 DB\n",
    "# by implementing LDA Algortihm, this will help to reduce\n",
    "# the number of instances to focus in the database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This libraries will help on the preprocessing\n",
    "import gensim\n",
    "from gensim.utils import simple_preprocess\n",
    "from gensim.parsing.preprocessing import STOPWORDS\n",
    "from nltk.stem.porter import *\n",
    "np.random.seed(400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here the topic with the maximum distribution is returned\n",
    "def max_topic(dist):\n",
    "    maximum = 0\n",
    "    topic = None\n",
    "    for t in lda_model[dist]:\n",
    "        if t[1] > maximum:\n",
    "            maximum = t[1]\n",
    "            topic = t[0]\n",
    "    return topic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tokenize, stem and removing stopwords\n",
    "stemmer = PorterStemmer()\n",
    "def preprocess(text):\n",
    "    result=[]\n",
    "    for token in gensim.utils.simple_preprocess(text) :\n",
    "        if token not in gensim.parsing.preprocessing.STOPWORDS and len(token) > 3:\n",
    "            result.append(stemmer.stem(token))\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that compares a string with a given list\n",
    "def contains_covid(title):\n",
    "    lst = [\"covid\", \"covid-19\", \"ncov\", \"sars-cov-2\", \"novel coronavirus\", \"coronavirus\", \"sarscov2\", \"corona\"]\n",
    "    for x in lst:\n",
    "        title = str(title)\n",
    "        if x in title.lower():\n",
    "            return title\n",
    "    return np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_location = 'CORD-19-research-challenge/'\n",
    "filename = 'CORD-19-research-challenge/metadata.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(59887, 19)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3063: DtypeWarning: Columns (14) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>cord_uid</th>\n",
       "      <th>sha</th>\n",
       "      <th>source_x</th>\n",
       "      <th>title</th>\n",
       "      <th>doi</th>\n",
       "      <th>pmcid</th>\n",
       "      <th>pubmed_id</th>\n",
       "      <th>license</th>\n",
       "      <th>abstract</th>\n",
       "      <th>publish_time</th>\n",
       "      <th>authors</th>\n",
       "      <th>journal</th>\n",
       "      <th>Microsoft Academic Paper ID</th>\n",
       "      <th>WHO #Covidence</th>\n",
       "      <th>arxiv_id</th>\n",
       "      <th>has_pdf_parse</th>\n",
       "      <th>has_pmc_xml_parse</th>\n",
       "      <th>full_text_file</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>zjufx4fo</td>\n",
       "      <td>b2897e1277f56641193a6db73825f707eed3e4c9</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Sequence requirements for RNA strand transfer ...</td>\n",
       "      <td>10.1093/emboj/20.24.7220</td>\n",
       "      <td>PMC125340</td>\n",
       "      <td>11742998.0</td>\n",
       "      <td>unk</td>\n",
       "      <td>Nidovirus subgenomic mRNAs contain a leader se...</td>\n",
       "      <td>2001-12-17</td>\n",
       "      <td>Pasternak, Alexander O.; van den Born, Erwin; ...</td>\n",
       "      <td>The EMBO Journal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>custom_license</td>\n",
       "      <td>http://europepmc.org/articles/pmc125340?pdf=re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ymceytj3</td>\n",
       "      <td>e3d0d482ebd9a8ba81c254cc433f314142e72174</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Crystal structure of murine sCEACAM1a[1,4]: a ...</td>\n",
       "      <td>10.1093/emboj/21.9.2076</td>\n",
       "      <td>PMC125375</td>\n",
       "      <td>11980704.0</td>\n",
       "      <td>unk</td>\n",
       "      <td>CEACAM1 is a member of the carcinoembryonic an...</td>\n",
       "      <td>2002-05-01</td>\n",
       "      <td>Tan, Kemin; Zelus, Bruce D.; Meijers, Rob; Liu...</td>\n",
       "      <td>The EMBO Journal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>custom_license</td>\n",
       "      <td>http://europepmc.org/articles/pmc125375?pdf=re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>wzj2glte</td>\n",
       "      <td>00b1d99e70f779eb4ede50059db469c65e8c1469</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Synthesis of a novel hepatitis C virus protein...</td>\n",
       "      <td>10.1093/emboj/20.14.3840</td>\n",
       "      <td>PMC125543</td>\n",
       "      <td>11447125.0</td>\n",
       "      <td>no-cc</td>\n",
       "      <td>Hepatitis C virus (HCV) is an important human ...</td>\n",
       "      <td>2001-07-16</td>\n",
       "      <td>Xu, Zhenming; Choi, Jinah; Yen, T.S.Benedict; ...</td>\n",
       "      <td>EMBO J</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>custom_license</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2sfqsfm1</td>\n",
       "      <td>cf584e00f637cbd8f1bb35f3f09f5ed07b71aeb0</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Structure of coronavirus main proteinase revea...</td>\n",
       "      <td>10.1093/emboj/cdf327</td>\n",
       "      <td>PMC126080</td>\n",
       "      <td>12093723.0</td>\n",
       "      <td>unk</td>\n",
       "      <td>The key enzyme in coronavirus polyprotein proc...</td>\n",
       "      <td>2002-07-01</td>\n",
       "      <td>Anand, Kanchan; Palm, Gottfried J.; Mesters, J...</td>\n",
       "      <td>The EMBO Journal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>custom_license</td>\n",
       "      <td>http://europepmc.org/articles/pmc126080?pdf=re...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>i0zym7iq</td>\n",
       "      <td>dde02f11923815e6a16a31dd6298c46b109c5dfa</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Discontinuous and non-discontinuous subgenomic...</td>\n",
       "      <td>10.1093/emboj/cdf635</td>\n",
       "      <td>PMC136939</td>\n",
       "      <td>12456663.0</td>\n",
       "      <td>unk</td>\n",
       "      <td>Arteri-, corona-, toro- and roniviruses are ev...</td>\n",
       "      <td>2002-12-01</td>\n",
       "      <td>van Vliet, A.L.W.; Smits, S.L.; Rottier, P.J.M...</td>\n",
       "      <td>The EMBO Journal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>custom_license</td>\n",
       "      <td>http://europepmc.org/articles/pmc136939?pdf=re...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cord_uid                                       sha source_x  \\\n",
       "0  zjufx4fo  b2897e1277f56641193a6db73825f707eed3e4c9      PMC   \n",
       "1  ymceytj3  e3d0d482ebd9a8ba81c254cc433f314142e72174      PMC   \n",
       "2  wzj2glte  00b1d99e70f779eb4ede50059db469c65e8c1469      PMC   \n",
       "3  2sfqsfm1  cf584e00f637cbd8f1bb35f3f09f5ed07b71aeb0      PMC   \n",
       "4  i0zym7iq  dde02f11923815e6a16a31dd6298c46b109c5dfa      PMC   \n",
       "\n",
       "                                               title  \\\n",
       "0  Sequence requirements for RNA strand transfer ...   \n",
       "1  Crystal structure of murine sCEACAM1a[1,4]: a ...   \n",
       "2  Synthesis of a novel hepatitis C virus protein...   \n",
       "3  Structure of coronavirus main proteinase revea...   \n",
       "4  Discontinuous and non-discontinuous subgenomic...   \n",
       "\n",
       "                        doi      pmcid   pubmed_id license  \\\n",
       "0  10.1093/emboj/20.24.7220  PMC125340  11742998.0     unk   \n",
       "1   10.1093/emboj/21.9.2076  PMC125375  11980704.0     unk   \n",
       "2  10.1093/emboj/20.14.3840  PMC125543  11447125.0   no-cc   \n",
       "3      10.1093/emboj/cdf327  PMC126080  12093723.0     unk   \n",
       "4      10.1093/emboj/cdf635  PMC136939  12456663.0     unk   \n",
       "\n",
       "                                            abstract publish_time  \\\n",
       "0  Nidovirus subgenomic mRNAs contain a leader se...   2001-12-17   \n",
       "1  CEACAM1 is a member of the carcinoembryonic an...   2002-05-01   \n",
       "2  Hepatitis C virus (HCV) is an important human ...   2001-07-16   \n",
       "3  The key enzyme in coronavirus polyprotein proc...   2002-07-01   \n",
       "4  Arteri-, corona-, toro- and roniviruses are ev...   2002-12-01   \n",
       "\n",
       "                                             authors           journal  \\\n",
       "0  Pasternak, Alexander O.; van den Born, Erwin; ...  The EMBO Journal   \n",
       "1  Tan, Kemin; Zelus, Bruce D.; Meijers, Rob; Liu...  The EMBO Journal   \n",
       "2  Xu, Zhenming; Choi, Jinah; Yen, T.S.Benedict; ...            EMBO J   \n",
       "3  Anand, Kanchan; Palm, Gottfried J.; Mesters, J...  The EMBO Journal   \n",
       "4  van Vliet, A.L.W.; Smits, S.L.; Rottier, P.J.M...  The EMBO Journal   \n",
       "\n",
       "   Microsoft Academic Paper ID WHO #Covidence arxiv_id  has_pdf_parse  \\\n",
       "0                          NaN            NaN      NaN           True   \n",
       "1                          NaN            NaN      NaN           True   \n",
       "2                          NaN            NaN      NaN           True   \n",
       "3                          NaN            NaN      NaN           True   \n",
       "4                          NaN            NaN      NaN           True   \n",
       "\n",
       "   has_pmc_xml_parse  full_text_file  \\\n",
       "0               True  custom_license   \n",
       "1               True  custom_license   \n",
       "2               True  custom_license   \n",
       "3               True  custom_license   \n",
       "4               True  custom_license   \n",
       "\n",
       "                                                 url  \n",
       "0  http://europepmc.org/articles/pmc125340?pdf=re...  \n",
       "1  http://europepmc.org/articles/pmc125375?pdf=re...  \n",
       "2  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1...  \n",
       "3  http://europepmc.org/articles/pmc126080?pdf=re...  \n",
       "4  http://europepmc.org/articles/pmc136939?pdf=re...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading the db metadata\n",
    "cord_db = pd.read_csv(filename, skipinitialspace=True)\n",
    "print(cord_db.shape)\n",
    "cord_db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39676, 19)\n"
     ]
    }
   ],
   "source": [
    "# Make sure there is not NaN values\n",
    "cord_db = cord_db.dropna(subset=['sha', 'abstract','publish_time','title'])\n",
    "print(cord_db.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3290, 19)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cord_uid</th>\n",
       "      <th>sha</th>\n",
       "      <th>source_x</th>\n",
       "      <th>title</th>\n",
       "      <th>doi</th>\n",
       "      <th>pmcid</th>\n",
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       "      <th>publish_time</th>\n",
       "      <th>authors</th>\n",
       "      <th>journal</th>\n",
       "      <th>Microsoft Academic Paper ID</th>\n",
       "      <th>WHO #Covidence</th>\n",
       "      <th>arxiv_id</th>\n",
       "      <th>has_pdf_parse</th>\n",
       "      <th>has_pmc_xml_parse</th>\n",
       "      <th>full_text_file</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10173</th>\n",
       "      <td>gcj14snd</td>\n",
       "      <td>1d05dbf38f98918269d71020f0c9ff616548c2df</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Human rhinovirus spatial-temporal epidemiology...</td>\n",
       "      <td>10.12688/wellcomeopenres.14836.2</td>\n",
       "      <td>PMC6234744</td>\n",
       "      <td>30483602.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Background: Human rhinovirus (HRV) is the pred...</td>\n",
       "      <td>2019</td>\n",
       "      <td>Morobe, John Mwita; Nyiro, Joyce U.; Brand, Sa...</td>\n",
       "      <td>Wellcome Open Res</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10395</th>\n",
       "      <td>stu49tf5</td>\n",
       "      <td>584aa286fb6e7f2404ff67c5cfa025b33e110214</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Epidemiology of Imported Infectious Diseases, ...</td>\n",
       "      <td>10.3201/eid2501.180178</td>\n",
       "      <td>PMC6302593</td>\n",
       "      <td>30560778.0</td>\n",
       "      <td>no-cc</td>\n",
       "      <td>Imported infectious diseases are becoming a se...</td>\n",
       "      <td>2019</td>\n",
       "      <td>Wang, Yali; Wang, Xuan; Liu, Xiaobo; Ren, Ruiq...</td>\n",
       "      <td>Emerg Infect Dis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>custom_license</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10396</th>\n",
       "      <td>qlwzl5mq</td>\n",
       "      <td>4c7f6c52fe043745887db5406196b1d5099c9614</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Multiple Introductions of Domestic Cat Feline ...</td>\n",
       "      <td>10.3201/eid2501.181347</td>\n",
       "      <td>PMC6302599</td>\n",
       "      <td>30561312.0</td>\n",
       "      <td>no-cc</td>\n",
       "      <td>The endangered Florida panther (Puma concolor ...</td>\n",
       "      <td>2019</td>\n",
       "      <td>Chiu, Elliott S.; Kraberger, Simona; Cunningha...</td>\n",
       "      <td>Emerg Infect Dis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>custom_license</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10434</th>\n",
       "      <td>eqgc8v7y</td>\n",
       "      <td>e5c06914d0daee6e881665b23adca340d0a6271a</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Impact of glucose-6-phosphate dehydrogenase de...</td>\n",
       "      <td>10.1371/journal.pone.0209204</td>\n",
       "      <td>PMC6314580</td>\n",
       "      <td>30601843.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Glucose-6-phosphate dehydrogenase (G6PD) defic...</td>\n",
       "      <td>2019</td>\n",
       "      <td>May, Win Lai; Kyaw, Myat Phone; Blacksell, Stu...</td>\n",
       "      <td>PLoS One</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10463</th>\n",
       "      <td>hbpk850a</td>\n",
       "      <td>ca8f30a35b564945dd2588c382a0e181a1e08662</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Mental health workers perceptions of disaster ...</td>\n",
       "      <td>10.1186/s12889-018-6313-9</td>\n",
       "      <td>PMC6318987</td>\n",
       "      <td>30606149.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>BACKGROUND: The post-disaster mental health cr...</td>\n",
       "      <td>2019</td>\n",
       "      <td>Xi, Yingjun; Chen, Runsen; Gillespie, Amy L.; ...</td>\n",
       "      <td>BMC Public Health</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cord_uid                                       sha source_x  \\\n",
       "10173  gcj14snd  1d05dbf38f98918269d71020f0c9ff616548c2df      PMC   \n",
       "10395  stu49tf5  584aa286fb6e7f2404ff67c5cfa025b33e110214      PMC   \n",
       "10396  qlwzl5mq  4c7f6c52fe043745887db5406196b1d5099c9614      PMC   \n",
       "10434  eqgc8v7y  e5c06914d0daee6e881665b23adca340d0a6271a      PMC   \n",
       "10463  hbpk850a  ca8f30a35b564945dd2588c382a0e181a1e08662      PMC   \n",
       "\n",
       "                                                   title  \\\n",
       "10173  Human rhinovirus spatial-temporal epidemiology...   \n",
       "10395  Epidemiology of Imported Infectious Diseases, ...   \n",
       "10396  Multiple Introductions of Domestic Cat Feline ...   \n",
       "10434  Impact of glucose-6-phosphate dehydrogenase de...   \n",
       "10463  Mental health workers perceptions of disaster ...   \n",
       "\n",
       "                                    doi       pmcid   pubmed_id license  \\\n",
       "10173  10.12688/wellcomeopenres.14836.2  PMC6234744  30483602.0   cc-by   \n",
       "10395            10.3201/eid2501.180178  PMC6302593  30560778.0   no-cc   \n",
       "10396            10.3201/eid2501.181347  PMC6302599  30561312.0   no-cc   \n",
       "10434      10.1371/journal.pone.0209204  PMC6314580  30601843.0   cc-by   \n",
       "10463         10.1186/s12889-018-6313-9  PMC6318987  30606149.0   cc-by   \n",
       "\n",
       "                                                abstract publish_time  \\\n",
       "10173  Background: Human rhinovirus (HRV) is the pred...         2019   \n",
       "10395  Imported infectious diseases are becoming a se...         2019   \n",
       "10396  The endangered Florida panther (Puma concolor ...         2019   \n",
       "10434  Glucose-6-phosphate dehydrogenase (G6PD) defic...         2019   \n",
       "10463  BACKGROUND: The post-disaster mental health cr...         2019   \n",
       "\n",
       "                                                 authors            journal  \\\n",
       "10173  Morobe, John Mwita; Nyiro, Joyce U.; Brand, Sa...  Wellcome Open Res   \n",
       "10395  Wang, Yali; Wang, Xuan; Liu, Xiaobo; Ren, Ruiq...   Emerg Infect Dis   \n",
       "10396  Chiu, Elliott S.; Kraberger, Simona; Cunningha...   Emerg Infect Dis   \n",
       "10434  May, Win Lai; Kyaw, Myat Phone; Blacksell, Stu...           PLoS One   \n",
       "10463  Xi, Yingjun; Chen, Runsen; Gillespie, Amy L.; ...  BMC Public Health   \n",
       "\n",
       "       Microsoft Academic Paper ID WHO #Covidence arxiv_id  has_pdf_parse  \\\n",
       "10173                          NaN            NaN      NaN           True   \n",
       "10395                          NaN            NaN      NaN           True   \n",
       "10396                          NaN            NaN      NaN           True   \n",
       "10434                          NaN            NaN      NaN           True   \n",
       "10463                          NaN            NaN      NaN           True   \n",
       "\n",
       "       has_pmc_xml_parse   full_text_file  \\\n",
       "10173               True  comm_use_subset   \n",
       "10395               True   custom_license   \n",
       "10396               True   custom_license   \n",
       "10434               True  comm_use_subset   \n",
       "10463               True  comm_use_subset   \n",
       "\n",
       "                                                     url  \n",
       "10173  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...  \n",
       "10395  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...  \n",
       "10396  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...  \n",
       "10434  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...  \n",
       "10463  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eliminate publications before 2019\n",
    "cord_db['publish_time'] = cord_db['publish_time'].apply(lambda x: str(x)[0:4])\n",
    "cord_db19 = cord_db[cord_db.publish_time == '2019']\n",
    "cord_db20 = cord_db[cord_db.publish_time == '2020']\n",
    "cord_db19.append(cord_db20)\n",
    "cord_db = cord_db19\n",
    "print(cord_db.shape)\n",
    "cord_db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(265, 19)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>cord_uid</th>\n",
       "      <th>sha</th>\n",
       "      <th>source_x</th>\n",
       "      <th>title</th>\n",
       "      <th>doi</th>\n",
       "      <th>pmcid</th>\n",
       "      <th>pubmed_id</th>\n",
       "      <th>license</th>\n",
       "      <th>abstract</th>\n",
       "      <th>publish_time</th>\n",
       "      <th>authors</th>\n",
       "      <th>journal</th>\n",
       "      <th>Microsoft Academic Paper ID</th>\n",
       "      <th>WHO #Covidence</th>\n",
       "      <th>arxiv_id</th>\n",
       "      <th>has_pdf_parse</th>\n",
       "      <th>has_pmc_xml_parse</th>\n",
       "      <th>full_text_file</th>\n",
       "      <th>url</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10514</th>\n",
       "      <td>56fyy9mn</td>\n",
       "      <td>dedff395d93d8023667fc500d7c7c8451d581451</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Antiviral activity of itraconazole against typ...</td>\n",
       "      <td>10.1186/s13567-019-0625-3</td>\n",
       "      <td>PMC6339390</td>\n",
       "      <td>30658691.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Feline coronaviruses (FCoVs) are the causative...</td>\n",
       "      <td>2019</td>\n",
       "      <td>Takano, Tomomi; Akiyama, Misuzu; Doki, Tomoyos...</td>\n",
       "      <td>Vet Res</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10569</th>\n",
       "      <td>wmi583s4</td>\n",
       "      <td>cd4c984e7afa08f850f9173578fbae31d92a64a5</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Genetic Diversity and Phylodynamics of Avian C...</td>\n",
       "      <td>10.3390/v11010057</td>\n",
       "      <td>PMC6356246</td>\n",
       "      <td>30646528.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Avian coronaviruses (ACoVs) are continuously e...</td>\n",
       "      <td>2019</td>\n",
       "      <td>A. Rohaim, Mohammed; F. El Naggar, Rania; M. H...</td>\n",
       "      <td>Viruses</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10572</th>\n",
       "      <td>tjp33y35</td>\n",
       "      <td>697cf5b78c388d6e79a7a87ac9b82e4911de135a</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Development of a Whole-Virus ELISA for Serolog...</td>\n",
       "      <td>10.3390/v11010043</td>\n",
       "      <td>PMC6356407</td>\n",
       "      <td>30634419.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Known human coronaviruses are believed to have...</td>\n",
       "      <td>2019</td>\n",
       "      <td>El-Duah, Philip; Meyer, Benjamin; Sylverken, A...</td>\n",
       "      <td>Viruses</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10573</th>\n",
       "      <td>olmein3q</td>\n",
       "      <td>19babdefd145a5becbc2a0b189eabdafa697de75</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Bats and Coronaviruses</td>\n",
       "      <td>10.3390/v11010041</td>\n",
       "      <td>PMC6356540</td>\n",
       "      <td>30634396.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Bats are speculated to be reservoirs of severa...</td>\n",
       "      <td>2019</td>\n",
       "      <td>Banerjee, Arinjay; Kulcsar, Kirsten; Misra, Vi...</td>\n",
       "      <td>Viruses</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10585</th>\n",
       "      <td>2mamwrfq</td>\n",
       "      <td>e68b0f3da8a02a8f0b86869e533eaf549f70ff9a</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Potent MERS-CoV Fusion Inhibitory Peptides Ide...</td>\n",
       "      <td>10.3390/v11010056</td>\n",
       "      <td>PMC6357153</td>\n",
       "      <td>30646495.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>The Middle East respiratory syndrome coronavir...</td>\n",
       "      <td>2019</td>\n",
       "      <td>Xia, Shuai; Lan, Qiaoshuai; Pu, Jing; Wang, Co...</td>\n",
       "      <td>Viruses</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cord_uid                                       sha source_x  \\\n",
       "10514  56fyy9mn  dedff395d93d8023667fc500d7c7c8451d581451      PMC   \n",
       "10569  wmi583s4  cd4c984e7afa08f850f9173578fbae31d92a64a5      PMC   \n",
       "10572  tjp33y35  697cf5b78c388d6e79a7a87ac9b82e4911de135a      PMC   \n",
       "10573  olmein3q  19babdefd145a5becbc2a0b189eabdafa697de75      PMC   \n",
       "10585  2mamwrfq  e68b0f3da8a02a8f0b86869e533eaf549f70ff9a      PMC   \n",
       "\n",
       "                                                   title  \\\n",
       "10514  Antiviral activity of itraconazole against typ...   \n",
       "10569  Genetic Diversity and Phylodynamics of Avian C...   \n",
       "10572  Development of a Whole-Virus ELISA for Serolog...   \n",
       "10573                             Bats and Coronaviruses   \n",
       "10585  Potent MERS-CoV Fusion Inhibitory Peptides Ide...   \n",
       "\n",
       "                             doi       pmcid   pubmed_id license  \\\n",
       "10514  10.1186/s13567-019-0625-3  PMC6339390  30658691.0   cc-by   \n",
       "10569          10.3390/v11010057  PMC6356246  30646528.0   cc-by   \n",
       "10572          10.3390/v11010043  PMC6356407  30634419.0   cc-by   \n",
       "10573          10.3390/v11010041  PMC6356540  30634396.0   cc-by   \n",
       "10585          10.3390/v11010056  PMC6357153  30646495.0   cc-by   \n",
       "\n",
       "                                                abstract publish_time  \\\n",
       "10514  Feline coronaviruses (FCoVs) are the causative...         2019   \n",
       "10569  Avian coronaviruses (ACoVs) are continuously e...         2019   \n",
       "10572  Known human coronaviruses are believed to have...         2019   \n",
       "10573  Bats are speculated to be reservoirs of severa...         2019   \n",
       "10585  The Middle East respiratory syndrome coronavir...         2019   \n",
       "\n",
       "                                                 authors  journal  \\\n",
       "10514  Takano, Tomomi; Akiyama, Misuzu; Doki, Tomoyos...  Vet Res   \n",
       "10569  A. Rohaim, Mohammed; F. El Naggar, Rania; M. H...  Viruses   \n",
       "10572  El-Duah, Philip; Meyer, Benjamin; Sylverken, A...  Viruses   \n",
       "10573  Banerjee, Arinjay; Kulcsar, Kirsten; Misra, Vi...  Viruses   \n",
       "10585  Xia, Shuai; Lan, Qiaoshuai; Pu, Jing; Wang, Co...  Viruses   \n",
       "\n",
       "       Microsoft Academic Paper ID WHO #Covidence arxiv_id  has_pdf_parse  \\\n",
       "10514                          NaN            NaN      NaN           True   \n",
       "10569                          NaN            NaN      NaN           True   \n",
       "10572                          NaN            NaN      NaN           True   \n",
       "10573                          NaN            NaN      NaN           True   \n",
       "10585                          NaN            NaN      NaN           True   \n",
       "\n",
       "       has_pmc_xml_parse   full_text_file  \\\n",
       "10514               True  comm_use_subset   \n",
       "10569               True  comm_use_subset   \n",
       "10572               True  comm_use_subset   \n",
       "10573               True  comm_use_subset   \n",
       "10585               True  comm_use_subset   \n",
       "\n",
       "                                                     url  \n",
       "10514  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...  \n",
       "10569  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...  \n",
       "10572  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...  \n",
       "10573  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...  \n",
       "10585  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Maintain publications containing in title \"COVID\" OR \"COVID-19\" OR “2019- nCoV” OR “SARS-CoV-2” OR “Novel coronavirus”\n",
    "cord_db[\"title\"] = cord_db[\"title\"].apply(contains_covid)\n",
    "cord_db = cord_db.dropna(subset=['title'])\n",
    "print(cord_db.shape)\n",
    "cord_db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Once the dataset is reduced this cell is focus on preprocessing\n",
    "processed_docs = []\n",
    "\n",
    "for doc in cord_db['abstract']:\n",
    "    processed_docs.append(preprocess(doc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary from 'processed_docs' containing the number of times a word appears \n",
    "# in the training set using gensim.corpora.Dictionary\n",
    "dictionary = gensim.corpora.Dictionary(processed_docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove very rare and very common words:\n",
    "# words appearing less than 15 times\n",
    "# words appearing in more than 10% of all documents\n",
    "dictionary.filter_extremes(no_below=15, no_above=0.1, keep_n= 100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the Bag-of-words model for each document i.e for each document we create a dictionary reporting how many\n",
    "# words and how many times those words appear. Save this to 'bow_corpus'\n",
    "bow_corpus = [dictionary.doc2bow(doc) for doc in processed_docs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here the aim is to find the best parameters by evaluating its coherence with UMass score\n",
    "max_model = None\n",
    "max_n = 2\n",
    "max_coherence = 0\n",
    "coherence_values = []\n",
    "for n in range(2, 14, 2):\n",
    "    model = gensim.models.LdaMulticore(corpus = bow_corpus, num_topics = n, id2word = dictionary, passes = 10)\n",
    "    coherence_model = gensim.models.CoherenceModel(model = model, texts = processed_docs, dictionary = dictionary, coherence='c_v')\n",
    "    coherence_score = coherence_model.get_coherence()\n",
    "    if coherence_score > max_coherence:\n",
    "        max_coherence = coherence_score\n",
    "        max_model = model\n",
    "        max_n = n\n",
    "    coherence_values.append(coherence_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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s093fDpypZsux8zxQmENkN+pq9cSj8yeSNCiWb9ncknZZIumm6dlDSRscy+u24FHAKypxF2mc6WWRRm8tK8zhaNVlio+e79Pzmu55priMmKgIPlXQ/bpa3TVkYDSP35XHnopLPL3puM/fL9hYIummiAhhweQM3jpURa11vgWspuYW3jzgYk43izR6Y9GUDIYMiLbJan5UU9fIKztOcteU4ST1oK5WT9w1JYObx6fwr2sPcvrS1X55z2BhiaQHFlrzVsDbXnaBC1camZfX8drsPRUXHcknp2ey1llAyfS/V3ae5HJDMw/2oq5Wd4kITy6ZTLOqzS1pw6eJRETmi8hBETkiIo+2s/8RESkRkT0iskFEcjz2/VBE9otIqYj8RJzeUhGZISJ7nXN+sL0/FeQMJTUhltXWvBWwikpcREcKN49P9sn5lxZm09isvLjNOt37m6ry9KYypmQOYWpW7+pqdVdW0kC+cvt41u53sc6W4P6AzxKJiEQCPwUWAHnA/SKS1+awnUCBqk4BXgJ+6Lz2BuBGYAowGZgJ3OK85mfA54FxzmO+r66hI62jtzYerLSx5QFIVSkqdXH9mOQeFWn0xtjUeApHJbFySxkt1vnar4qPnudIZS0PzO6/uxFPf3HTKCamJ/CPq/Zb87bDl3cks4AjqnpUVRuA54C7PQ9Q1Y2qesV5Wgxktu4C4oAYIBaIBlwikgEMVtVN6h4y8zSwxIfX0KGF+RnUN7Ww8aA1bwWaI5W1lJ270ieTEDuzbHYO5eev8u6Rsz59H/NRy4uPM2RANIuvG+6X94+OjODJe/I5U13Hv6+zuSXg20QyAij3eF7hbOvIw8AaAFXdBGwETjuPtapa6rzesy2hw3OKyOdFZJuIbKuqqurxRXRk5sgkkuNjrfZWAGpdN2Zerm8TyR2T0hg2KIaV1uneb1zVdazd7+LTBZl9VlerJ2bkDGVZYTa/+dMx9lbY3BJfJpL2+i7abQMQkQeAAuAp5/lYIBf3HcoIYI6I3Nydc6rqL1S1QFULUlL6frJSZIQwf3IaGw9U2bKcAaaoxMWUzCGkD4nz6fvERkVyb0Em60srOXPJKsT2h5WbT9Ci6rdmLU9fu2Miw+Jj+eYre2hqbvF3OH7ly0RSAXgO8M4Erln4WkTmAo8Bi1W1dQjMPUCxqtaqai3uO5XZzjkzPV7e7jn7y8L8DK42NvPWwb6/4zE9U1ntFGn08d1Iq6WzsmluUZ7fWt71waZXGpvddbVuGZ9CzrBB/g6HIQOi+c5dk9h3sprfbgrvu1JfJpKtwDgRGSUiMcB9wCrPA0RkGvBz3EnEs7PhBHCLiESJSDTujvZSVT0N1IjIbGe01kPA7314DZ2aNTKJYYNieN2atwLG+lL3n1Fv1h7pjpxhg7hpXDLPbT0R9t9KfW3dfheVNfU8GAB3I60W5qdz24QU/m3dQU5eDN+5JT5LJKraBHwRWAuUAi+o6n4R+a6ILHYOewqIB14UkV0i0ppoXgLeB/YCu4HdqvoHZ99fA78CjjjHrPHVNXQlKjKCOyans/FAJVcbmv0VhvFQVHKGzKF9U6TRW8sKszl9qc7uTH3s6U3HyRw6gFsnpPo7lA+ICN+9ezKq8I+/3xe2ZXOifHlyVV0NrG6z7XGPn+d28Lpm4K862LcN95DggLAoP4OVm0/w9qFK5k/um9XZTM9crm/ivffPsawwu8+KNHrj9tw0UhNiWbnlRL/dCYWbQ64aNh87zzfmT/R5Xa3uykoayFfnjeP7qw+wdv+ZsPwcsJntvVQ4KomkQTG8vtcmJ/nbu4eraGhq8fmw37aiIyP4zMwsNh6spOLCla5fYLpt+SZ3Xa3PzPR9Xa2e+NyNo8jNGMw/rtoflpXBLZH0UlRkBHdMSuPNUhd1jda85U/rSlwMGRDNrJF9W6TRG/fNykaA57ZYp3tfq61v4uUdFdyZn9FvdbW6Kzoygh98Ip/Kmnr+LQznllgi6QML8zO43NDM24esjdxf3EUaK5kzMZWoPi7S6I0RiQO4bUIqz28rp9E63fvUKzsq+r2uVk9MzUrkodk5/HbTcXaVX/R3OP3KEkkfmD16GIkDo21yoh9tK7vAxSuNzO2nYb/tWVqYTVVNva3v3YdUleXFZUweMbjf62r1xN/fMYHUhFi++fLesBrFZ4mkD0RHRnBHXjobSiutectPikpcxERGcMsE362U15VbJ6QyfEgcKzbbmu59ZfOx8xxy1fLQ7JH9OoCipxLionli8SRKT1fz6/eO+zucfmOJpI8syE+ntr6Jdw9b3aX+pqqsL3Vx/ZhhxMf6dCBipyIjhPtnZfPHI2c5fvay3+IIJcuLyxgyIJq7/FRXqyfumJTO3NxU/r3oEOXnw2PwhSWSPnLj2GSGDIhmjTVv9bvD/VSk0RufnplFZITw7Ba7K+mtyuo61u47w6dmZDIgxn91tbpLRHji7smIwONhMrfEEkkfiY6M4ON5aRSVuKhvsuat/lTUWqQxABJJ2uA45uWm8eL2Cvs76KVnt5TT1KIsC6CZ7N4akTiAR+aNZ+PBKtbsC/2pAZZI+tDC/Axq6pv4ozVv9at1JS6uyxxC2mDfFmn01tLCbM5fbuCNMPgA8ZXG5hZWbinj5vEpjEr2f12tnvizG0YyafhgvrNqP9UhPrfEEkkfunFsMoPjolhtkxP7jau6jt3lF/06Wqutj41NJjtpoHW698L6Eheu6noeCsK7kVZRztySs7X1PPXGQX+H41OWSPpQTFQE8/LSKSo5Q0NT+Az986f1pU6z1qTASSQREcLSwmy2HDvPkcoaf4cTlJ7eVOaemzMxcOpq9cSUzEQ+e8NIntlcxo4TF/wdjs9YIuljC/PTqa5r4j1bNa9frC9xkZU0gAlp/Vek0Rv3zsgkOlLsrqQHDrtq2HT0HMtmZwdcXa2e+LuPTyAtIY5vvbw3ZCerWiLpYx8bl0xCbJRNTuwHrUUa5+WmB9wcg+T4WOZPzuB32ytsblE3PVNcRkxkBJ8pCMy6Wt0VHxvFE3dP4sCZGv73j8f8HY5PWCLpY7FRkczLS2NdiStkv30EincO+adIo7eWFWZTXdfEa3vsS4W3auub+N2OkyyaksGw+Fh/h9Nn7piUzsfz0vjx+tCcW2KJxAcW5Gdw6WqjNW/5WFGJi8SB0cwcOdTfobSrcFQSY1IGscLWdPfaqztPUlvfFBBL6fa17yyeRKQI33419OaWWCLxgZvGJRMfG8UaG73lM03NLbx5sJI5E/xTpNEbIsLSwhx2nrhIyalqf4cT8FSV5ZvKmDR8MNOzA7+uVncNTxzA3318Am8fqgq5u1Sv/g8UkQEiMsHXwYSKuOhI5uamsrbkjDVv+cjW406RxgBt1mr1yekjiI2KYOUWuyvpytbjFzjoquHB2TkB1+fVVz57w0jyRwzhiT+UcOlq6Mwt6TKRiMhdwC7gDef5VI8lcU0HFuRncPFKI5veP+fvUELS+lJ3kcabx/uvSKM3EgfGsGhKBq/scDfZmI49vek4CXFR3D11hL9D8ZnICOEHn8jn/OV6/uWNA/4Op894c0fyHWAWcBFAVXcBI30XUmi4ZXwKg2IiWbMvtG5hA4GqUlTi4oax/i3S6K1lhTlcbmhm1a5T/g4lYFXW1PHGvjN8akZWUNXV6onJI4bwuRtHsXLzCbaXnfd3OH3Cm0TSpKqXfB5JiImLjmRObhpr97vCal2C/nDIVcuJ84FRpNEb07MTmZiewIrNZSHXydpXnnPqaj0wO9vfofSLR+aNZ/iQOL718r6QaP72JpHsE5GlQKSIjBOR/wT+5OO4QsKi/HTOX25g87HQ+NYRKIpK3IMYAqksSmdEhGWF2ew/Vc2eCvtO1lZTcwsrN5/gpnHJjE6J93c4/WJQbBTfvXsyB101/OKdo/4Op9e8SSRfAiYB9cBK4BLwt74MKlTcOiGVgTGRvG6TE/tUUYmL67ISA6ZIozeWTBvBwJhIGwrcjvWlLs5U1/FgCA757czcvDTmT0rnJxsOU3YuuNev6TSRiEgk8ISqPqaqM53Ht1W1zpuTi8h8ETkoIkdE5NF29j8iIiUiskdENohIjrP9NhHZ5fGoE5Elzr7fiMgxj31Te3Dd/SIuOpLbJqaydt8Za97qI67qOnZXXGJebnDVYEqIi2bxdcP5w+7TITVapy8sL3bX1bo9SO4w+9J3Fk8iOjIi6OeWdJpIVLUZmNGTEztJ6KfAAiAPuF9E8tocthMoUNUpwEvAD5333aiqU1V1KjAHuAKs83jd11r3O53/AWtRfgbnLjew5bg1b/WFD4o05qX7OZLuW1aYw9XGZl7dedLfoQSMI5W1vHfkHEsLQ6OuVnelD4nja3dM4N3DZ1m1O3gHY3jTtLVTRFaJyIMi8onWhxevmwUcUdWjqtoAPAfc7XmAkzBa6wUUA5ntnOdeYI3HcUHltgmpDIiOtNpbfaSoxEV20kDGpwVfW3p+5hCmZA5h5eYTQf3tsy89U1xGdKTwmZmhUVerJx6YncN1WYn802slXLzS4O9wesSbRJIEnMN9Z3CX87jTi9eNAMo9nlc42zryMLCmne33Ac+22fak0xz2IxFptyCPiHxeRLaJyLaqqiovwvWNATGR3DYxhTf2uWhusQ+P3qitb+JPR84xLy8taCesLZ2VzUFXDdvLQrekuLcu1zfxu+0VLMzPIDmE6mp1V2SE8P17JnPhSiP/vCY455Z0mUhU9XPtPP7ci3O39396u5+kIvIAUAA81WZ7BpAPrPXY/E1gIjATd5L7Rgdx/0JVC1S1ICXFv5PWFuZncLa2nq3WvNUr7xyqoqE5cIs0euOu64aTEBtl5eWBV3edpKa+iYeuD69O9vZMGj6Ehz82iue2lrMlCEd5ejOzPVNEXhGRShFxicjvRKS9Jqi2KgDP+9VM4JpGQBGZCzwGLFbV+ja7Pw28oqof9E6q6ml1qwd+jbsJLaDdNiGVuOgIa97qpdYijQU5gVmk0RuDYqNYMm0Er+89zYXLwdmM0Rda62rlZgxmenbw/j770t/OHceIxAF865W9QbcwnjdNW78GVgHDcTdN/cHZ1pWtwDgRGSUiMbibqD5SWkVEpgE/x51EKts5x/20adZy7lIQd9vGEmCfF7H41aDYKG4dn8qafWeseauHGptbePNAYBdp9NbSwmwamlr43Y4Kf4fiN9vKLnDgTA0PXR+6dbW6a2BMFN9bMpkjlbX8/O33/R1Ot3jzf2SKqv5aVZucx2+ALtuKVLUJ+CLuZqlS4AVV3S8i3xWRxc5hTwHxwIvOUN4PEo2IjMR9R/N2m1OvEJG9wF4gGfieF9fgdwunZFBVU29t4z207fgFLl1tDOpmrVa5GYOZkTM0rDvdl28qc+pqDfd3KAHltompLMrP4D83HuHY2eCZW+JNIjkrIg+ISKTzeAB353uXVHW1qo5X1TGq+qSz7XFVXeX8PFdV0zyG8i72eO1xVR2hqi1tzjlHVfNVdbKqPqCqtd5frv/MmZhKbJQ1b/VUUYmLmKjAL9LoraWzsjl69jKbjoZfUc+qmnrW7DvNvTMyGRgT+LXS+tvjd+URGxnBt1/dGzRfNLxJJH+Ou6/iDHAa93BcbzrbjYf42ChuGZ/Cmn2nabHmrW5RVYpKz3DjmGEMCoIijd5YNCWDIQOiw7LT/fmtJ2hs1pBcvKovpA2O4+sLJvLekXO8EiRzjrwZtXVCVReraoqqpqrqElW1Og89sGhKBq7qenacsOat7jjoqqH8/NWgnITYkbjoSO6dkcnafWeoqmk7xiR0NTW3sGLzCT42NpkxYVJXqyeWzcpmalYi33u9NCgGZXgzauu3IpLo8XyoiPyfb8MKTXMmphITFWG1t7qpaL97NvvcICuL0pX7Z2XT1KK8uL2864NDxIYDlZy+VGd3I12IcNYtqb7ayA/WlPo7nC5507Q1RVUvtj5R1QvANN+FFLoS4qK5eVwKb+w7Y81b3VBU6i7SmBpERRq9MTY1ntmjk3h2y4mw+XtYvqmM4UPiQu5LgS/kZgzmL24azQvbKigO8L40bxJJhIh8MNBbRJKA0Gio9oNFU9I5famOneUXuz7YcOZSHXsqLvHxEBit1Z5lhTmUn7/Ku0fO+jsUn3to7gGyAAAgAElEQVS/qpY/HjnL0sLsoB/C3V++cvs4Moe655bUNzX7O5wOefPb/DfgTyLyTyLyT7jXIvmhb8MKXbfnphETaaO3vPVhkcbQTCR3TEpn2KAYVhSHfrfjh3W1wmPxqr4wICaS7y2ZzNGqy/zPW4G7bok3ne1PA58EXEAl8AlVXe7rwELV4LhobhqXzJq9p4NmaJ8/FZW4yBk2kHGpodkxGxMVwacKsthwoJIzl7xanSEoXWlo4qXtFSyYnEFKQvjW1eqJWyekctd1w/npxiO8XxWYsx286WwfA7yvqv+FexLgXM/Od9N9C/MzOHWpjl3WvNWp2vomNr1/jnm5wVuk0Rv3z8qiuUV5fmvodrr/ftcpauqaeNDqavXIP9yZS2x0BI+9EphzS7xp2vod0CwiY4FfAaNwr5RoemhuXhrRkWLNW114+2DwF2n0Rs6wQdw0Lpnntp4IyQXQVJWnN5UxMT0hqOuk+VNqQhyPLphI8dHz/G5H4M0t8SaRtDjlTj4B/IeqfhXI8G1YoW3IgGg+NjaZ1XvPBOS3i0BRVHKGxIHRzAiDD59lhTmcvlTHxoP+W/LAV3acuEDp6WoetLpavXL/zGxm5AzlyddLOB9gc0u8SSSNInI/8BDwmrMt2nchhYeF+RmcvHiVPRWX/B1KQPqgSOPE4C/S6I3bc1NJTYhlZQiu6f70pjISYqNYMrWz5YhMVyIihO/fk09NXRNPvh5Yc0u8+T/0c8D1wJOqekxERgHP+Das0DcvL42oCGve6sjW4+eprmsK2WG/bUVHRnDfzCzeOlRF+fmgXAy0XWdr61m99zSfnJEZMuVt/GlCegJ/dctofrejgj8F0JBxb0Ztlajql1X1Wef5MVX9Z9+HFtoSB8Zw49hkVu+z0VvtaS3SeNO40CjS6I3PzMpGgOe2hk79ree3lltdrT72pTnjyE4ayGOv7qOuMTDmloR+m0EAW5ifTvn5q+w7We3vUAKKqlJU4uJjY5PD6lvsiMQB3DYhlee3VtAYAp3uzS3KiuIybhgzjLEhOnzbH+KiI3nynskcO3uZ/34rMNYtsUTiRx/PSycyQli9z5q3PB04U0PFhashP1qrPctmZ3O2tp6iEpe/Q+m1DaUuTl2qs6V0feCmcSksmTqcn711hCOVNf4Ox/tEIiKDfBlIOBo6KIYbxgxjtU1O/IjWD9HbJ4ZfPaZbxqcyInEAK0Kg0315cRnpg+OYmxt+Xwj6w7fvzGNgTBTfenmf32u1eTMh8QYRKcG9yiEicp2I/LfPIwsTC/MzKDt3hf2nrHmr1fpSF1NDsEijNyIjhPtmZvHekXNBtUJeW0erann3sNXV8qXk+Fi+uWAiW46f56Xt/l222Zvf8I+AO3BWRVTV3cDNvgwqnNwxyd28tcaat4APizSGY7NWq8/MzCIyQnh2S/B2uq/YfIIoJyka3/l0QRazRibx5OpSztb6b10br74qqGrb2g2BMVQgBCQNiuH60cNscqKjyCnSGC7DftuTOjiOeblpvLitPKArvnbkakMzL24rZ/7k9LC8q+xPERHCk/dM5kqDf+eWeJNIykXkBkBFJEZE/h6nmcv0jQX56Rw7e5nS0/7vNPO3ohIXI4cNDPtRPstmZ3PhSiNv7Dvj71C6bdXuk1TXNfHQ9SP9HUpYGJeWwBduGcMrO0/yx8P+mVviTSL5AvA3wAigApjqPDd95I5J6UQIYd+8VVPXyKb3zzIvL7SLNHrjxjHJ5AwbyIri4Greaq2rNSEtgZkjQ7+0TaD4m9vGMnLYQB57da9f5pZ4MyHxrKouU9U0Z832B1Q1sJfrCjLJ8bHMHj2M18N89Nbbh6pobFYb5YO7yeL+WdlsOX6ew67guVPdceIi+09ZXa3+5p5bkk/ZuSv815tH+v39fbpmu4jMF5GDInJERB5tZ/8jIlIiIntEZIOI5DjbbxORXR6POhFZ4uwbJSKbReSwiDwvIjHeX27gWpCfwdGqyxwMog+Nvra+xMXQMCnS6I1PzcgkOlJYsTl47kqeKS4jPjaKJdOsrlZ/u3FsMp+YNoKfv/M+h/r5c8Rna7aLSCTwU2ABkAfcLyJ5bQ7bCRSo6hTgJZyVF1V1o6pOVdWpwBzgCrDOec2/AD9S1XHABeBhL64h4M13mrdW7w2+NvG+8GGRxjQbLuoYFh/LgskZ/G5HBVcbAr/T/WxtPa/vOc0np48gPowqEgSSxxblMig2im+9vLdf55b4cs32WcARVT2qqg3Ac8Ddngc4CaO1Ql0xkNnOee4F1qjqFXHfK8/BnXQAfgss8SKWgJeSEMusUUlhW8Rx6zF3kcZwHvbbnqWF2dTUNfHanlP+DqVLL2wrp6G5xepq+dGw+Fi+tTCXbWUXeH5b/y2U5ss120cAnldS4WzryMPAmna23wc86/w8DLjorI/S6TlF5PMisk1EtlVVBccaDwvzMzhSWdvvt6WBYF2Ji9ioCG4en+zvUAJK4agkxqQMCvjmLXddrRNcP3oY49IS/B1OWPvUjEwKRyXxg9WlVNX0z9wSb9dsv5fur9neXk9bu/daIvIAUAA81WZ7BpAPrO3uOVX1F6paoKoFKSnBUUF2/qR0RAi7uxLPIo0DY6xJxJOIsKwwh13lF9l/KnDXrtl4oJKTF6/aUroBQER48p586hpb+KfXSvrlPb1tjD4AvAz8HqgVkWwvXlMBeE5rzQSuuT8XkbnAY8BiVW2bPj8NvKKqjc7zs0CiiLR+2rR7zmCVOjiOmTnh17xVerqGkxfDs0ijNz45PZPYqAhWBvBdydPFZaQNjrXfYYAYmxrPX986hlW7T7HjxAWfv583o7a+hPtupAj3Comv8+FKiZ3ZCoxzRlnF4G6iWtXm3NOAn+NOIpXtnON+PmzWQt1jYzfivkMC+Czu5BYyFuanc8hVGxAVPfvL+lIXIjAnN/yKNHpjyMBo7pwynFd3nqS2vqnrF/Sz42cv886hKu6flU20DZQIGH996xh+tmw607ISuz64l7z5rX8FmKCqk1R1iqrmO6OsOuX0Y3wRd7NUKfCCqu4Xke+KyGLnsKeAeOBFZ5jvB4lGREbivqN5u82pvwE8IiJHcPeZ/K8X1xA0FuRnAOE1equoxCnSmGDlNDqytDCbyw3N/H7XSX+Hco1nisuIcua9mMARFx3JgvyMfpnP402DdDnQo8ZZVV0NrG6z7XGPn+d28trjtNORrqpHcY8IC0lpg+MoyBnK6r2n+fLt4/wdjs+dvnSVvScv8fX5E/wdSkCbnp3IxPQEVm4+wdJZ2QEz2e9qQzMvbq/gjknppFldrbDlzR3JUeAtEfmmM4HwERF5xNeBhbOF+RkcOFPD+1W1/g7F59aXWJFGb4gIy2bnsP9UNbsrAqfT/Q+7T3HpaqN1soc5bxLJCdz9IzFAgsfD+MiC/HQA1oRBp/u6EhejkgcxJiW8izR6Y8nU4QyMiWRlgCx6pao8XXyc8WnxFI5K8nc4xo+6bNpS1SfAvUKiqgbvSjtBJGPIAKZnJ/L63jN8cU7oNm9V1zVSfPQcn7txVMA01QSyhLho7p46nFd2nuSxRXkMGRDt13h2lV9k38lq/unuSfb7C3PejNq63lZI7H8L8zMoPV0d1KvkdeUdK9LYbUtn5VDX2MIrO/y7Ih7A8k1lDIqJ5J7p7RWkMOHEm6atH2MrJPa7D0dvhW7zVlGJi6RBMVaksRvyM4cwJXMIK7ec8Gul6POXG3htz2k+MT3T6moZWyExUI1IHMDUrMSQTSSNzS1sPFDJnImpREZYs0h3LCvM5pCrlm1lvp9o1pHnt7rralknuwFbITGgLcrPYP+pasrOhV7z1hYr0thjd103nITYKFYU+6fTvblFWbG5jMJRSYy3uloGWyExoM2f7B69FYqTE4ucIo03jbMijd01MCaKe6aPYPW+M5y/3NDv7//WwUoqLly1pXTNBzpNJM6aIg/aCon+kZU0kOsyh4Rc81ZrkcabxlmRxp5aWphNQ1MLv9ve/53uy4vLSE2I5eOT7G7SuHWaSFS1mTZriJj+tTA/g70nL1F+/krXBweJ1iKNNlqr5yamD2ZGztB+73QvO3eZt62ulmnDm7+E90Tkv0TkJhGZ3vrweWQGcCcSCK3RW0Ul7iKNt1si6ZVlhdkcO3uZTe/3XwPBis0niBCrq2U+yptEcgMwCfgu7kWu/g34V18GZT6UlTSQ/BFDWL0vdPpJikrPMC0rkZSEWH+HEtQW5meQODC63xa9qmts5oVt5dwxKY30IVZXy3zIm4WtbmvnMac/gjNuC/Mz2F1+kYoLwd+8deriVfadrGZeXrq/Qwl6cdGRfHJ6Jmv3n+mXlfD+sPsUF6802lK65hrezGxPE5H/FZE1zvM8EXnY96GZVgs/qL0V/Hcl60vdRRpt2G/fWFqYTVOL8kI/rM+9vLiMcanxXD96mM/fywQXb5q2foN7TZHhzvNDwN/6KiBzrZxhg5g0fDCr9wV/P0lRiYvRyYMYm2pFGvvCmBT3B/uzW07Q0uK7Tvfd5RfZU3GJB6/Psbpa5hreJJJkVX0BaIEPFqyyme39bGF+BjtPXOTUxav+DqXHWos0zrW7kT61tDCbigtXeedwlc/e4+nWulrTrlkiyBivEsllERkGKICIzKaHC12ZnguF0VtvH3QXabRmrb51x6R0hg2K8Vmn+/nLDfxhzynumT6ChDj/Vhw2gcmbRPII7rXWx4jIe8DTwJd8GpW5xqjkQeRmDGZNEI/eKipxMWxQDNOzrUhjX4qJiuBTBVlsKHVx+lLf37G+uK2chqYWHpw9ss/PbUKDN6O2dgC34B4G/FfAJFXd4+vAzLUW5aezveyCTz4sfK2xuYWNB61Io68snZVNi7qLKfal5hblmc1lzBqVxIR0q6tl2uft1NRZwHXAdOB+EXnIdyGZjrSWlg/G0Vubj56nxoo0+kz2sIHcPD6F57aU09Tc0mfnfedQFeXnr/KgDfk1nfBm+O9y3BMQPwbMdB4FPo7LtGNMSjwT0xNYE4Sjt4pKzjhFGlP8HUrIWjormzPVdbx5oLLPzvn0puOkJMRyxySb92M65s0dSQFwo6r+P1X9kvP4sjcnF5H5InJQRI6IyKPt7H9EREpEZI+IbBCRHI992SKyTkRKnWNGOtt/IyLHRGSX85jq3aWGhgWTM9hWdgFXdZ2/Q/GaqrK+tJKbxiUzICbS3+GErNtzU0kbHMvKLX3T6X7i3BXeOlTF/TOziImyulqmY978dewDuv11xKkc/FNgAZCHu0ksr81hO4ECVZ0CvAT80GPf08BTqpqLu2nN82vW11R1qvPY1d3YgtmiKemowpogGr1VcrqakxevWrOWj0VHRvCZmdm8faiqT4p8rthc5q6rVWh1tUznOkwkIvIHEVkFJAMlIrJWRFa1Prw49yzgiKoeVdUG4DnaVBJW1Y2q2voXXwxkOu+dB0SpapFzXK3HcWFtbGoC49Pig6r2VmuRxjkTLZH42n0zsxDg2V7eldQ1NvP8tnLm5aaRMWRA3wRnQlZni0H0tjDjCMBzCEkFUNjJ8Q8Da5yfxwMXReRlYBSwHnjUKWsP8KSIPA5scLZfU2hIRD4PfB4gOzu0vlEtmJzBT948TGV1HamDA794XlGJi+nZQ61IYz8YnjiAORNTeWFbBX87d3yPm6Re23Oai1caeciW0jVe6PCvTFXfbn0AB4AE51HqbOtKe2M8263hICIP4O6LecrZFAXcBPw97s790cCfOfu+CUx0ticB3+gg/l+oaoGqFqSkhFYH76IpGajC2v2Bf1dy8uJV9p+qtmatfrSsMIeztfUUlbh6fI7lxWWMSRnE9WOsrpbpmjejtj4NbAE+BXwa2Cwi93px7gogy+N5JnCqnfPPBR4DFnvcWVQAO51msSbgVdxDj1HV0+pWD/wadxNaWBmflsDY1HheD4J+kvUlVqSxv908PoURiQNYuaVna7rvqbjI7vKLPDjb6moZ73hz3/sYMFNVP6uqD+H+4P4HL163FRgnIqNEJAa4D/cM+Q+IyDTg57iTSGWb1w4VkdZbiTlAifOaDOdfAZbgHgwQdhZOTmfLsfP9Uj68N9aXuos0jkmxIo39JTJCuH9WFu8dOcfRqtpuv375pjIGxkTyiRmZPojOhCJvEklEmw/5c968zrmT+CLuysGlwAuqul9Evisii53DngLigRedobyrnNc2427W2iAie3E3k/3Sec0KZ9te3AMBvufFNYSchVMyaAnw5q3WIo12N9L/Pl2QRVSEdLvT/cLlBlbtPsWSaSMYbHW1jJc662xv9YaIrAWedZ5/hg87xTulqquB1W22Pe7x89xOXlsETGlnuy2qBUxIS2B0yiBW7z0dsAsNvWVFGv0mdXAc8/LSeGl7BX/38QnERXs3f+fF7eXUN7XYTHbTLd7cWXwNd/PTFNxlUn6hql/3dWCmcyLCwskZFB89x9nawGzeai3SOM2KNPrFssIcLlxp5A0vh4q3tCjPFJ9g5sih5GYM9nF0JpR0No9krIjcCKCqL6vqI6r6VeCciIzptwhNhxbmu5u31u3v+egcX2loauGtA5XcnmtFGv3lhjHDyBk2kBWbvet0f/twFSfOX+HB60f6NjATcjq7I/kxUNPO9ivOPuNnuRkJjEoeFJBrlGw+do6a+iZbm92PIiKEpbOy2Xr8Aodc7f2v/FHPbCojOT6W+VZXy3RTZ4lkZHvl4lV1GzDSZxEZr4kICyans+noOc5fbvB3OB+xvsRFXHQEHxub7O9Qwtq9MzKJiYxgZReLXpWfv8KbByu5f5bV1TLd19lfTGdTpq1mQoBYmJ9Bc4uyLoBGb6kqRSUuPjY2xYo0+tmw+FjmT07ndzsquNrQ8QrZKzafQID7Z4VWFQjTPzpLJFtF5C/bbhSRh4HtvgvJdMek4YPJThoYUJMT95+q5tSlOj5uo7UCwrLCbGrqmvjDnmvmAwNOXa2tJ5iXl8bwRPuOaLqvs+G/fwu8IiLL+DBxFAAxwD2+Dsx4R0RYmJ/BL989yoXLDQwdFOPvkD4s0pib6u9QDDBrVBJjU+NZsfkEny7Iumb/6r2nuXCl0ZbSNT3WWa0tl6reADwBHHceT6jq9aoaOO0ohkVO81Zvaiv1paISFzOyh5Icb0UaA4GIu9N9d/lF9p28dM3+pzeVMTplEDeOtbpapme8mUeyUVX/03m82R9Bme6ZPGIwmUMHBETzVsWFK5SctiKNgeaT0zOJjYq4ZtGrvRWX2FV+kQcKra6W6TkbnhECRIRF+Rm8d+Qsl640+jWWDaXuajqWSALLkIHR3DllOL/feZLa+qYPti8vPs6A6Eg+aXW1TC9YIgkRC/MzaGpR1pX4t9WxqMTF6JRBjLYijQFn2exsLjc08/tdJwG4dKWR3+9y19UaMsDqapmes0QSIqZkDmFE4gC/Tk68dNWKNAayaVmJ5GYM5pniE6iq1dUyfcYSSYhwj95K549HznLpqn+at946WElTi9qw3wAlIiwtzKb0dDU7yy/yTHEZBTlDyRtudbVM71giCSEL8zNobNYPFpPqb0UlLpLjY5iaZUUaA9WSqcMZGBPJ117czfFzV3jQltI1fcASSQiZmpXI8CFxfmneamhq4e2DVdw+Mc2KNAawhLho7p46gverLpMcH8P8yVZXy/SeJZIQIiIsyM/g3cNnqa7r3+atD4s0WrNWoFtW6C6D8pmZWcRGWQkb03uWSELMwvx0Gppb2FDav81bRU6RxhutSGPAmzxiCC994Xq+NGecv0MxIcISSYiZljWU9MFxvL6n/4YBq7r7ZW4aZ0Uag0XByCSvV000piuWSEJMRISwID+ddw5XUdNPzVutRRqtWcuY8GSJJAQtzM+goamFNw9U9sv7rStxESFw+0Qr0mhMOLJEEoJmZA8lbXBsv43eKipxMSNnKMOsSKMxYckSSQiKiBAWTM7grYNVXPaoq+QLFReuUGpFGo0Jaz5NJCIyX0QOisgREXm0nf2PiEiJiOwRkQ0ikuOxL1tE1olIqXPMSGf7KBHZLCKHReR5EfH/AhwBaMHkdOqbWtjg4+at1smPc3MtkRgTrnyWSEQkEvgpsADIA+4Xkbw2h+0EClR1CvAS8EOPfU8DT6lqLjALaP1E/BfgR6o6DrgAPOyrawhmBSOTSEmIZY2Pm7eKSl2MsSKNxoQ1X96RzAKOqOpRVW0AngPu9jzAWevkivO0GMgEcBJOlKoWOcfVquoVcS+YMAd30gH4LbDEh9cQtCIjhAWT09l4sJIrDb5p3rp0tZHNR88zL89mRxsTznyZSEYA5R7PK5xtHXkYWOP8PB64KCIvi8hOEXnKucMZBlxU1dZPxg7PKSKfF5FtIrKtqqqqVxcSrBZMzqCu0Xejt1qLNFr/iDHhzZeJpL2CS9rugSIP4F4P/ilnUxRwE/D3wExgNPBn3Tmnqv5CVQtUtSAlJaV7kYeIWaOSSI6PZc1e30xOXFfiIjk+lmlZiT45vzEmOPgykVQAWR7PM4FTbQ8SkbnAY8BiVa33eO1Op1msCXgVmA6cBRJFJKqzcxq3yAhh/uQ03jxQydWG5j49d2uRxrm5qURYkUZjwpovE8lWYJwzyioGuA9Y5XmAiEwDfo47iVS2ee1QEWm9lZgDlKiqAhuBe53tnwV+78NrCHoLJ2dwtbGZjQf7tnmr+Og5auubbLSWMcZ3icS5k/gisBYoBV5Q1f0i8l0RWewc9hQQD7woIrtEZJXz2mbczVobRGQv7iatXzqv+QbwiIgcwd1n8r++uoZQMGtUEsMGxfT55MSiEhcDoiP52Dgr0mhMuIvq+pCeU9XVwOo22x73+HluJ68tAqa0s/0o7hFhxgtRkRF8fFI6v991krrG5j4p1KeqrC91cdO4ZCv8Z4yxme3hYFF+Blcamnmrj5q39p2s5rQVaTTGOCyRhIHZo5MYOjCa1X00equo5Iy7SKP1jxhjsEQSFqIiI7hjUjobSl3UNfZ+9FZRaSUFOUkkDbLqNMYYSyRhY2F+BpcbmnnnUO8mZ5afdxdpnJtnJeONMW6WSMLE9WOGkTgwutejt9Y7S/haWRRjTCtLJGEiOjKCj+elsb60slfNW0UlLsamxjMqeVAfRmeMCWaWSMLIwvwMauub+OPhsz16/aUrjWw+dt5GaxljPsISSRi5cWwyQwb0vHlr48FKmq1IozGmDUskYSQ6MoJ5eWkUlbiob+p+81ZRqbtI49RMK9JojPmQJZIwsyg/g5r6Jt470r3mrfqmZivSaIxplyWSMHPj2GQS4qJ4fU/3JicWHz1PbX2TNWsZY65hiSTMxES1Nm+doaGpxevXFZWcYUB0JDeOtSKNxpiPskQShhblZ1Bd18R773vXvKWqrC+p5ObxVqTRGHMtSyRh6GPjkkmIjWL1Hu9Gb+09eYkz1XU2CdEY0y5LJGEoNiqSuXlprCtx0djcdfPW+hIXEQJzJlpZFGPMtSyRhKmF+RlcutrIn94/1+Wx60pcVqTRGNMhSyRh6qZxycR70bxVfv4KB87U2GgtY0yHLJGEqbjoSG7PTWVtyZlOm7eKSlqLNFoiMca0zxJJGFuYn8HFK40UH+24eauoxMW41HhGWpFGY0wHLJGEsVvGpzAoJrLD2lsXrzSw5bgVaTTGdM4SSRiLi45kTm4aa/e7aGqneeutg1VWpNEY0yVLJGFuUX465y83sPnY+Wv2FZW4SEmI5Tor0miM6YRPE4mIzBeRgyJyREQebWf/IyJSIiJ7RGSDiOR47GsWkV3OY5XH9t+IyDGPfVN9eQ2h7pbxqQyIjuT1Ns1b9U3NvHWw0oo0GmO65LNEIiKRwE+BBUAecL+I5LU5bCdQoKpTgJeAH3rsu6qqU53H4jav+5rHvl2+uoZwMCAmkjm5qazdd4bmFv1g+6b3z3G5odmatYwxXfLlHcks4IiqHlXVBuA54G7PA1R1o6pecZ4WA5k+jMd0YOHkDM5dbmDzsQ9HbxWVuBgYE8kNY6xIozGmc75MJCOAco/nFc62jjwMrPF4Hici20SkWESWtDn2Sac57EciEtveyUTk887rt1VVVfXoAsLFbRNTiIuO+GD0VkuLsr7Uxc3jUqxIozGmS75MJO01rGs72xCRB4AC4CmPzdmqWgAsBX4sImOc7d8EJgIzgSTgG+2dU1V/oaoFqlqQkpLSw0sIDwNjopgzMZU39rloblH2nbqEq7remrWMMV7xZSKpALI8nmcCp9oeJCJzgceAxapa37pdVU85/x4F3gKmOc9Pq1s98GvcTWimlxZMzuBsbT1bj5+nyIo0GmO6wZeJZCswTkRGiUgMcB+wyvMAEZkG/Bx3Eqn02D60tclKRJKBG4ES53mG868AS4B9PryGsDFnYiqxURGs2XuaohIXBSOTGGpFGo0xXojy1YlVtUlEvgisBSKB/1PV/SLyXWCbqq7C3ZQVD7zozguccEZo5QI/F5EW3Mnun1W1xDn1ChFJwd10tgv4gq+uIZwMio3itgmpvLzzJDV1TXx7Ua6/QzLGBAmfJRIAVV0NrG6z7XGPn+d28Lo/Afkd7JvTlzGaDy3IT+eN/e613K1/xBjjLZ8mEhNcbs9NIyYqgpHDBpIzzIo0GmO8Y4nEfCA+NoonFk8ifXCcv0MxxgQRSyTmI+6fle3vEIwxQcaKNhpjjOkVSyTGGGN6xRKJMcaYXrFEYowxplcskRhjjOkVSyTGGGN6xRKJMcaYXrFEYowxpldEtd0lQkKKiFQBZT18eTJwtg/DCQZ2zeHBrjn09fZ6c1S1ywWdwiKR9IaIbHMW2Aobds3hwa459PXX9VrTljHGmF6xRGKMMaZXLJF07Rf+DsAP7JrDg11z6OuX67U+EmOMMb1idyTGGGN6xRKJMcaYXrFE0gERyRKRjSJSKiL7ReQr/o6pP4hIpIjsFJHX/B1LfxCRRBF5SUQOOL/r6/0dk6/N7zMAAAUCSURBVK+JyFedv+l9IvKsiITckpgi8n8iUiki+zy2JYlIkYgcdv4d6s8Y+1oH1/yU87e9R0ReEZFEX7y3JZKONQF/p6q5wGzgb0Qkz88x9YevAKX+DqIf/QfwhqpOBK4jxK9dREYAXwYKVHUyEAnc59+ofOI3wPw22x4FNqjqOGCD8zyU/IZrr7kImKyqU4BDwDd98caWSDqgqqdVdYfzcw3uD5gR/o3Kt0QkE1gE/MrfsfQHERkM3Az8L4CqNqjqRf9G1S+igAEiEgUMBE75OZ4+p6rvAOfbbL4b+K3z82+BJf0alI+1d82quk5Vm5ynxUCmL97bEokXRGQkMA3Y7N9IfO7HwNeBFn8H0k9GA1XAr53mvF+JyCB/B+VLqnoS+FfgBHAauKSq6/wbVb9JU9XT4P6iCKT6OZ7+9ufAGl+c2BJJF0QkHvgd8LeqWu3veHxFRO4EKlV1u79j6UdRwHTgZ6o6DbhM6DV3fITTL3A3MAoYDgwSkQf8G5XxNRF5DHdz/QpfnN8SSSdEJBp3Elmhqi/7Ox4fuxFYLCLHgeeAOSLyjH9D8rkKoEJVW+80X8KdWELZXOCYqlapaiPwMnCDn2PqLy4RyQBw/q30czz9QkQ+C9wJLFMfTRy0RNIBERHcbeelqvrv/o7H11T1m6qaqaojcXe+vqmqIf1NVVXPAOUiMsHZdDtQ4seQ+sMJYLaIDHT+xm8nxAcYeFgFfNb5+bPA7/0YS78QkfnAN4DFqnrFV+9jiaRjNwIP4v5mvst5LPR3UKbPfQlYISJ7gKnA9/0cj085d18vATuAvbg/A0KubIiIPAtsAiaISIWIPAz8MzBPRA4D85znIaODa/4vIAEocj7D/scn720lUowxxvSG3ZEYY4zpFUskxhhjesUSiTHGmF6xRGKMMaZXLJEYY4zpFUskxvSAiPxARG4VkSUics1seBF5zGPYeLPHz1/uwXsVisiP+iZyY/qeDf81pgdE5E3cBS6/D7ykqu91cmytqsb3W3DG9DO7IzGmG5z1HfYAM3FP/voL4Gci8ng3zjHKWetmj7MuRqaz/RkR+ZmIvCsih0RkgbN9roi86vycICK/FZG9zuuXiEiUiCx3tu3ryV2PMb0R5e8AjAkmqvo1EXkRd9WDR4C3VPXGbp7mv4FfqeoKEfk87qrL9zr7soBb4P+3d8eqUURRHMa/8wYKamWpEoTISgpJYScEa9HKJk2qvICFJHmEFLFLYxktrEUsrCy3sBHTpQoagpUgmL/FHWSZancmaJDvV83MZZg7zRzOvcM53ATeVdWN3r3bwNcky12Jk0vACnAlyTK0Zl0DXk0azIxEWtxdYAosMaw21z1aYUyAl8D9mbGDJGdJPgNHtIAy6wGwB5DmFDiklcXYrao14PuAOUmDmZFIc6qqCa0L3XXgG60pVFXVFFhN8uMcHtPftOyfV/9akpOqugM8pHU/fARsnMNcpLmYkUhzSjJNMqG1LL0NvAfWkkwWDCIfgSfd8VPgw8zY42pu0Za5vvTufQtsQotgVXW5qq7Sfpx5BWzx/5fC1wVjRiItoPtonyY5q6qlJEOWtjaB/ap6BhwD6zNjh7TAcg3YSPKzbYX8sQO8qKpPwC/gOa2vyn63ZxJa2XDpr/H3X+mC6BqJvU7y5l/PRVqES1uSpFHMSCRJo5iRSJJGMZBIkkYxkEiSRjGQSJJGMZBIkkb5DbfpXzzxpny4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(2, 14, 2)\n",
    "plt.plot(x, coherence_values)\n",
    "plt.xlabel(\"# Topics\")\n",
    "plt.ylabel(\"Coherence score\")\n",
    "plt.title(\"Tunning num_topics\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Running LDA Using bag of words\n",
    "# Using number of topics found in the coherence score\n",
    "lda_model = max_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coherence score: %d \n",
      " 0.28605378510348994\n"
     ]
    }
   ],
   "source": [
    "coherence_model = gensim.models.CoherenceModel(model = lda_model, texts = processed_docs, dictionary = dictionary, coherence='c_v')\n",
    "print(\"Coherence score: %d \\n\", coherence_model.get_coherence())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic: 0 \n",
      "Words: 0.059*\"mice\" + 0.043*\"membran\" + 0.033*\"inhibit\" + 0.031*\"lung\" + 0.030*\"fusion\" + 0.027*\"pathogenesi\" + 0.023*\"vitro\" + 0.022*\"domain\" + 0.021*\"cellular\" + 0.021*\"neutral\"\n",
      "\n",
      "\n",
      "Topic: 1 \n",
      "Words: 0.070*\"revers\" + 0.056*\"felin\" + 0.044*\"virul\" + 0.037*\"attenu\" + 0.036*\"real\" + 0.031*\"mutant\" + 0.028*\"transcript\" + 0.026*\"conserv\" + 0.021*\"previous\" + 0.020*\"antigen\"\n",
      "\n",
      "\n",
      "Topic: 2 \n",
      "Words: 0.065*\"innat\" + 0.058*\"pathway\" + 0.051*\"signal\" + 0.039*\"knowledg\" + 0.027*\"agent\" + 0.027*\"regul\" + 0.027*\"research\" + 0.019*\"interferon\" + 0.019*\"natur\" + 0.018*\"evolut\"\n",
      "\n",
      "\n",
      "Topic: 3 \n",
      "Words: 0.122*\"hcov\" + 0.048*\"neg\" + 0.040*\"entri\" + 0.039*\"screen\" + 0.031*\"hospit\" + 0.026*\"known\" + 0.022*\"depend\" + 0.022*\"challeng\" + 0.021*\"swab\" + 0.021*\"rang\"\n",
      "\n",
      "\n",
      "Topic: 4 \n",
      "Words: 0.032*\"china\" + 0.030*\"arabia\" + 0.029*\"circul\" + 0.028*\"hospit\" + 0.026*\"contact\" + 0.022*\"laboratori\" + 0.021*\"measur\" + 0.020*\"popul\" + 0.020*\"shed\" + 0.019*\"event\"\n",
      "\n",
      "\n",
      "Topic: 5 \n",
      "Words: 0.051*\"hcov\" + 0.045*\"materi\" + 0.034*\"drug\" + 0.027*\"articl\" + 0.025*\"electron\" + 0.024*\"diagnosi\" + 0.023*\"amino\" + 0.023*\"tissu\" + 0.022*\"diagnost\" + 0.021*\"obtain\"\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Explore the words occuring for each topic and its relative weight\n",
    "for idx, topic in lda_model.print_topics(-1):\n",
    "    print(\"Topic: {} \\nWords: {}\".format(idx, topic ))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0.016908083),\n",
       " (1, 0.25202325),\n",
       " (2, 0.68082917),\n",
       " (3, 0.016792312),\n",
       " (4, 0.016705839),\n",
       " (5, 0.016741343)]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The distribution of each of the trained documents to tbe given topics\n",
    "lda_model[bow_corpus[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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",
       "    }\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>cord_uid</th>\n",
       "      <th>sha</th>\n",
       "      <th>source_x</th>\n",
       "      <th>title</th>\n",
       "      <th>doi</th>\n",
       "      <th>pmcid</th>\n",
       "      <th>pubmed_id</th>\n",
       "      <th>license</th>\n",
       "      <th>abstract</th>\n",
       "      <th>publish_time</th>\n",
       "      <th>authors</th>\n",
       "      <th>journal</th>\n",
       "      <th>Microsoft Academic Paper ID</th>\n",
       "      <th>WHO #Covidence</th>\n",
       "      <th>arxiv_id</th>\n",
       "      <th>has_pdf_parse</th>\n",
       "      <th>has_pmc_xml_parse</th>\n",
       "      <th>full_text_file</th>\n",
       "      <th>url</th>\n",
       "      <th>topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10514</th>\n",
       "      <td>56fyy9mn</td>\n",
       "      <td>dedff395d93d8023667fc500d7c7c8451d581451</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Antiviral activity of itraconazole against typ...</td>\n",
       "      <td>10.1186/s13567-019-0625-3</td>\n",
       "      <td>PMC6339390</td>\n",
       "      <td>30658691.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Feline coronaviruses (FCoVs) are the causative...</td>\n",
       "      <td>2019</td>\n",
       "      <td>Takano, Tomomi; Akiyama, Misuzu; Doki, Tomoyos...</td>\n",
       "      <td>Vet Res</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10569</th>\n",
       "      <td>wmi583s4</td>\n",
       "      <td>cd4c984e7afa08f850f9173578fbae31d92a64a5</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Genetic Diversity and Phylodynamics of Avian C...</td>\n",
       "      <td>10.3390/v11010057</td>\n",
       "      <td>PMC6356246</td>\n",
       "      <td>30646528.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Avian coronaviruses (ACoVs) are continuously e...</td>\n",
       "      <td>2019</td>\n",
       "      <td>A. Rohaim, Mohammed; F. El Naggar, Rania; M. H...</td>\n",
       "      <td>Viruses</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10572</th>\n",
       "      <td>tjp33y35</td>\n",
       "      <td>697cf5b78c388d6e79a7a87ac9b82e4911de135a</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Development of a Whole-Virus ELISA for Serolog...</td>\n",
       "      <td>10.3390/v11010043</td>\n",
       "      <td>PMC6356407</td>\n",
       "      <td>30634419.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Known human coronaviruses are believed to have...</td>\n",
       "      <td>2019</td>\n",
       "      <td>El-Duah, Philip; Meyer, Benjamin; Sylverken, A...</td>\n",
       "      <td>Viruses</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10573</th>\n",
       "      <td>olmein3q</td>\n",
       "      <td>19babdefd145a5becbc2a0b189eabdafa697de75</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Bats and Coronaviruses</td>\n",
       "      <td>10.3390/v11010041</td>\n",
       "      <td>PMC6356540</td>\n",
       "      <td>30634396.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Bats are speculated to be reservoirs of severa...</td>\n",
       "      <td>2019</td>\n",
       "      <td>Banerjee, Arinjay; Kulcsar, Kirsten; Misra, Vi...</td>\n",
       "      <td>Viruses</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10585</th>\n",
       "      <td>2mamwrfq</td>\n",
       "      <td>e68b0f3da8a02a8f0b86869e533eaf549f70ff9a</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Potent MERS-CoV Fusion Inhibitory Peptides Ide...</td>\n",
       "      <td>10.3390/v11010056</td>\n",
       "      <td>PMC6357153</td>\n",
       "      <td>30646495.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>The Middle East respiratory syndrome coronavir...</td>\n",
       "      <td>2019</td>\n",
       "      <td>Xia, Shuai; Lan, Qiaoshuai; Pu, Jing; Wang, Co...</td>\n",
       "      <td>Viruses</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       cord_uid                                       sha source_x  \\\n",
       "10514  56fyy9mn  dedff395d93d8023667fc500d7c7c8451d581451      PMC   \n",
       "10569  wmi583s4  cd4c984e7afa08f850f9173578fbae31d92a64a5      PMC   \n",
       "10572  tjp33y35  697cf5b78c388d6e79a7a87ac9b82e4911de135a      PMC   \n",
       "10573  olmein3q  19babdefd145a5becbc2a0b189eabdafa697de75      PMC   \n",
       "10585  2mamwrfq  e68b0f3da8a02a8f0b86869e533eaf549f70ff9a      PMC   \n",
       "\n",
       "                                                   title  \\\n",
       "10514  Antiviral activity of itraconazole against typ...   \n",
       "10569  Genetic Diversity and Phylodynamics of Avian C...   \n",
       "10572  Development of a Whole-Virus ELISA for Serolog...   \n",
       "10573                             Bats and Coronaviruses   \n",
       "10585  Potent MERS-CoV Fusion Inhibitory Peptides Ide...   \n",
       "\n",
       "                             doi       pmcid   pubmed_id license  \\\n",
       "10514  10.1186/s13567-019-0625-3  PMC6339390  30658691.0   cc-by   \n",
       "10569          10.3390/v11010057  PMC6356246  30646528.0   cc-by   \n",
       "10572          10.3390/v11010043  PMC6356407  30634419.0   cc-by   \n",
       "10573          10.3390/v11010041  PMC6356540  30634396.0   cc-by   \n",
       "10585          10.3390/v11010056  PMC6357153  30646495.0   cc-by   \n",
       "\n",
       "                                                abstract publish_time  \\\n",
       "10514  Feline coronaviruses (FCoVs) are the causative...         2019   \n",
       "10569  Avian coronaviruses (ACoVs) are continuously e...         2019   \n",
       "10572  Known human coronaviruses are believed to have...         2019   \n",
       "10573  Bats are speculated to be reservoirs of severa...         2019   \n",
       "10585  The Middle East respiratory syndrome coronavir...         2019   \n",
       "\n",
       "                                                 authors  journal  \\\n",
       "10514  Takano, Tomomi; Akiyama, Misuzu; Doki, Tomoyos...  Vet Res   \n",
       "10569  A. Rohaim, Mohammed; F. El Naggar, Rania; M. H...  Viruses   \n",
       "10572  El-Duah, Philip; Meyer, Benjamin; Sylverken, A...  Viruses   \n",
       "10573  Banerjee, Arinjay; Kulcsar, Kirsten; Misra, Vi...  Viruses   \n",
       "10585  Xia, Shuai; Lan, Qiaoshuai; Pu, Jing; Wang, Co...  Viruses   \n",
       "\n",
       "       Microsoft Academic Paper ID WHO #Covidence arxiv_id  has_pdf_parse  \\\n",
       "10514                          NaN            NaN      NaN           True   \n",
       "10569                          NaN            NaN      NaN           True   \n",
       "10572                          NaN            NaN      NaN           True   \n",
       "10573                          NaN            NaN      NaN           True   \n",
       "10585                          NaN            NaN      NaN           True   \n",
       "\n",
       "       has_pmc_xml_parse   full_text_file  \\\n",
       "10514               True  comm_use_subset   \n",
       "10569               True  comm_use_subset   \n",
       "10572               True  comm_use_subset   \n",
       "10573               True  comm_use_subset   \n",
       "10585               True  comm_use_subset   \n",
       "\n",
       "                                                     url  topic  \n",
       "10514  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...      2  \n",
       "10569  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...      0  \n",
       "10572  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...      3  \n",
       "10573  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...      2  \n",
       "10585  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...      2  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here is given the topics assigned to each document\n",
    "topic_list = []\n",
    "for i in range(0,len(cord_db)):\n",
    "    topic_list.append(max_topic(bow_corpus[i]))\n",
    "# Now lets add it to the dataframe\n",
    "cord_db.insert (19, \"topic\", topic_list)\n",
    "cord_db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Representation of topic frequency\n",
    "freq_list = []\n",
    "for i in range(0,max_n):\n",
    "    freq_list.append(topic_list.count(i))\n",
    "# Plot\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "topics = list(range(0, max_n))\n",
    "ax.bar(topics,freq_list)\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_xlabel('Topic')\n",
    "ax.set_title('Literature on CORD-19')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[56, 27, 33, 35, 79, 35]\n"
     ]
    }
   ],
   "source": [
    "print(freq_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.7/site-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  return pd.concat([default_term_info] + list(topic_dfs))\n"
     ]
    },
    {
     "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_el38651112317392120258190746\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el38651112317392120258190746_data = {\"mdsDat\": {\"x\": [-0.1372950781038228, 0.06410267182094469, -0.046977026720047194, -0.1498944486743606, 0.11868964955884646, 0.15137423211843923], \"y\": [-0.06698004724285586, 0.12005111546071133, -0.030385713189544677, 0.03862402136361394, 0.09940076081246399, -0.1607101372043885], \"topics\": [1, 2, 3, 4, 5, 6], \"cluster\": [1, 1, 1, 1, 1, 1], \"Freq\": [26.029348373413086, 23.526992797851562, 14.178289413452148, 12.69991397857666, 12.163971900939941, 11.401488304138184]}, \"tinfo\": {\"Category\": [\"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Default\", \"Topic1\", \"Topic1\", \"Topic1\", 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\"distribut\", \"distribut\", \"domain\", \"domain\", \"domain\", \"drug\", \"drug\", \"drug\", \"econom\", \"econom\", \"econom\", \"econom\", \"econom\", \"efficaci\", \"efficaci\", \"efficaci\", \"efficaci\", \"efficaci\", \"electron\", \"electron\", \"electron\", \"encod\", \"encod\", \"encod\", \"encod\", \"endem\", \"endem\", \"endem\", \"endem\", \"endem\", \"enhanc\", \"enhanc\", \"enhanc\", \"enhanc\", \"enhanc\", \"entri\", \"entri\", \"entri\", \"event\", \"event\", \"event\", \"event\", \"event\", \"evolut\", \"evolut\", \"examin\", \"examin\", \"examin\", \"examin\", \"examin\", \"facilit\", \"facilit\", \"facilit\", \"facilit\", \"facilit\", \"facilit\", \"famili\", \"famili\", \"famili\", \"famili\", \"felin\", \"felin\", \"felin\", \"felin\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"follow\", \"form\", \"form\", \"form\", \"form\", \"furthermor\", \"furthermor\", \"furthermor\", \"furthermor\", \"furthermor\", \"furthermor\", \"fusion\", \"fusion\", 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\"long\", \"long\", \"long\", \"long\", \"loss\", \"loss\", \"loss\", \"loss\", \"lower\", \"lower\", \"lower\", \"lower\", \"lower\", \"lung\", \"lung\", \"lung\", \"materi\", \"materi\", \"materi\", \"mean\", \"mean\", \"mean\", \"mean\", \"mean\", \"measur\", \"measur\", \"measur\", \"measur\", \"measur\", \"membran\", \"membran\", \"membran\", \"membran\", \"membran\", \"mice\", \"mice\", \"mild\", \"mild\", \"mild\", \"mild\", \"molecular\", \"molecular\", \"molecular\", \"molecular\", \"molecular\", \"mous\", \"mous\", \"mous\", \"mutant\", \"mutant\", \"mutant\", \"natur\", \"natur\", \"natur\", \"natur\", \"neg\", \"neg\", \"neg\", \"neutral\", \"neutral\", \"neutral\", \"nucleocapsid\", \"nucleocapsid\", \"nucleocapsid\", \"nucleocapsid\", \"nucleocapsid\", \"nucleotid\", \"nucleotid\", \"nucleotid\", \"nucleotid\", \"obtain\", \"obtain\", \"obtain\", \"occur\", \"occur\", \"occur\", \"occur\", \"organ\", \"organ\", \"organ\", \"organ\", \"organ\", \"organ\", \"pathogenesi\", \"pathogenesi\", \"pathogenesi\", \"pathogenesi\", \"pathogenesi\", \"pathway\", \"pathway\", \"period\", \"period\", \"period\", \"period\", \"pig\", \"pig\", \"pig\", \"pig\", \"play\", \"play\", \"play\", \"play\", \"pneumonia\", \"pneumonia\", \"pneumonia\", \"pneumonia\", \"pneumonia\", \"polymeras\", \"polymeras\", \"polymeras\", \"polymeras\", \"popul\", \"popul\", \"popul\", \"popul\", \"post\", \"post\", \"post\", \"post\", \"post\", \"preval\", \"preval\", \"previous\", \"previous\", \"previous\", \"previous\", \"previous\", \"product\", \"product\", \"product\", \"product\", \"product\", \"protect\", \"protect\", \"protect\", \"public\", \"public\", \"public\", \"public\", \"public\", \"rang\", \"rang\", \"rang\", \"rang\", \"real\", \"real\", \"real\", \"real\", \"refer\", \"refer\", \"refer\", \"regul\", \"regul\", \"regul\", \"relev\", \"relev\", \"relev\", \"research\", \"research\", \"research\", \"research\", \"revers\", \"revers\", \"screen\", \"screen\", \"screen\", 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\"transcript\", \"unknown\", \"unknown\", \"unknown\", \"unknown\", \"unknown\", \"use\", \"use\", \"use\", \"use\", \"virul\", \"virul\", \"virul\", \"virul\", \"virul\", \"virul\", \"vitro\", \"vitro\", \"vivo\", \"vivo\", \"vivo\", \"vivo\", \"wild\", \"wild\", \"wild\", \"wild\", \"world\", \"world\", \"world\", \"worldwid\", \"worldwid\", \"worldwid\", \"worldwid\", \"worldwid\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [5, 1, 6, 4, 3, 2]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  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_el38651112317392120258190746\", ldavis_el38651112317392120258190746_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_el38651112317392120258190746\", ldavis_el38651112317392120258190746_data);\n",
       "      });\n",
       "    });\n",
       "}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_el38651112317392120258190746\", ldavis_el38651112317392120258190746_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Some visualization using Python library for interactive topic model visualization\n",
    "import pyLDAvis\n",
    "import pyLDAvis.gensim\n",
    "for_viz = pyLDAvis.gensim.prepare(lda_model, bow_corpus, dictionary)\n",
    "pyLDAvis.display(for_viz)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Once the topics are clustered, now papers will be label as threatments or vaccines\n",
    "# based on frequency of words appearing on ontology dictionaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topics and their respective words:\n",
      "0::['mice', 'membran', 'inhibit', 'lung', 'fusion', 'pathogenesi', 'vitro', 'domain', 'cellular', 'neutral']\n",
      "1::['revers', 'felin', 'virul', 'attenu', 'real', 'mutant', 'transcript', 'conserv', 'previous', 'antigen']\n",
      "2::['innat', 'pathway', 'signal', 'knowledg', 'agent', 'regul', 'research', 'interferon', 'natur', 'evolut']\n",
      "3::['hcov', 'neg', 'entri', 'screen', 'hospit', 'known', 'depend', 'challeng', 'swab', 'rang']\n",
      "4::['china', 'arabia', 'circul', 'hospit', 'contact', 'laboratori', 'measur', 'popul', 'shed', 'event']\n",
      "5::['hcov', 'materi', 'drug', 'articl', 'electron', 'diagnosi', 'amino', 'tissu', 'diagnost', 'obtain']\n"
     ]
    }
   ],
   "source": [
    "# Explore the words occuring for each topic and its relative weight\n",
    "#for idx, topic in lda_model.print_topics(-1):\n",
    "#    print(\"Topic: {} \\nWords: {}\".format(idx, topic))\n",
    "#    #print(type(topic))\n",
    "#    #print(len(topic))\n",
    "#    print(\"\")\n",
    "x = lda_model.show_topics(num_topics = 8, num_words=10, formatted=False)\n",
    "topics_words = [(tp[0], [wd[0] for wd in tp[1]]) for tp in x]\n",
    "\n",
    "#Below Code Prints Topics and Words\n",
    "print('Topics and their respective words:')\n",
    "for topic,words in topics_words:\n",
    "    print(str(topic)+ \"::\"+ str(words))\n",
    "    #for i in words:\n",
    "    #    print(i)\n",
    "#print()\n",
    "\n",
    "#Below Code Prints Only Words \n",
    "#for topic,words in topics_words:\n",
    "#    print(\" \".join(words))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Every topic for every document:\n",
      "2\n",
      "0\n",
      "3\n",
      "2\n",
      "2\n",
      "4\n",
      "3\n",
      "2\n",
      "3\n",
      "3\n",
      "3\n",
      "2\n",
      "4\n",
      "0\n",
      "0\n",
      "5\n",
      "1\n",
      "2\n",
      "4\n",
      "1\n",
      "2\n",
      "0\n",
      "4\n",
      "5\n",
      "5\n",
      "4\n",
      "2\n",
      "2\n",
      "4\n",
      "0\n",
      "3\n",
      "0\n",
      "5\n",
      "5\n",
      "5\n",
      "2\n",
      "4\n",
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      "0\n",
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      "5\n",
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      "4\n",
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      "0\n",
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      "2\n",
      "0\n",
      "0\n",
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      "4\n",
      "1\n",
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      "2\n",
      "3\n",
      "4\n",
      "0\n",
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      "3\n",
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      "0\n",
      "1\n",
      "0\n",
      "5\n",
      "4\n",
      "3\n",
      "0\n",
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      "0\n",
      "0\n",
      "3\n",
      "0\n",
      "3\n",
      "3\n",
      "0\n",
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      "0\n",
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      "0\n",
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      "4\n",
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      "5\n",
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      "0\n",
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      "5\n",
      "1\n",
      "0\n",
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      "0\n",
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      "1\n",
      "2\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "1\n",
      "0\n",
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      "1\n",
      "3\n",
      "0\n",
      "2\n",
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      "3\n",
      "5\n",
      "4\n",
      "1\n",
      "0\n",
      "1\n",
      "4\n",
      "4\n",
      "5\n",
      "3\n",
      "1\n",
      "1\n",
      "3\n",
      "2\n",
      "4\n",
      "0\n",
      "4\n",
      "1\n",
      "4\n",
      "4\n",
      "2\n",
      "2\n",
      "3\n",
      "5\n",
      "1\n",
      "1\n",
      "5\n",
      "0\n",
      "0\n",
      "4\n",
      "2\n",
      "4\n",
      "4\n",
      "3\n",
      "0\n",
      "4\n",
      "5\n",
      "3\n",
      "4\n",
      "2\n",
      "2\n",
      "5\n",
      "4\n",
      "4\n",
      "4\n",
      "3\n",
      "3\n",
      "4\n",
      "1\n",
      "4\n",
      "2\n",
      "0\n",
      "5\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "# Every topic for every document\n",
    "print('Every topic for every document:')\n",
    "for i in cord_db['topic']:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Every possible word in a dictionary:\n",
      "Dictionary(161 unique tokens: ['agent', 'exhibit', 'felin', 'inhibit', 'system']...)\n"
     ]
    }
   ],
   "source": [
    "print('Every possible word in a dictionary:')\n",
    "print(dictionary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vaccines listerm loaded\n",
      "Therapeutics listerm loaded\n"
     ]
    }
   ],
   "source": [
    "# Vaccines\n",
    "# Therapeutics\n",
    "with open('CORD-19-research-challenge/vaccines.txt', 'r') as f:\n",
    "    x = f.readlines()\n",
    "with open('CORD-19-research-challenge/drugs.txt', 'r') as f:\n",
    "    y = f.readlines()\n",
    "print('Vaccines listerm loaded')\n",
    "print('Therapeutics listerm loaded')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vaccines listerm:\n",
      "121 words\n",
      "['acellular vaccine', 'acquired immune deficiency syndrome', 'active immunity', 'acute', 'adjuvant', 'adverse events', 'allergy', 'anaphylaxis', 'anthrax', 'antibiotic', 'antibody', 'antigens', 'antitoxin', 'antiviral', 'arthralgia', 'arthritis', 'association', 'asthma', 'asymptomatic infection', 'autism', 'b cells', 'bias', 'biological plausibility', 'bone marrow', 'booster shots', 'brachial neuritis', 'breakthrough infection', 'combination vaccine', 'communicable', 'crohn’s disease', 'conjugate vaccine', 'contraindication', 'convulsion', 'crib or cot death', 'deltoid', 'diabetes', 'diphtheria', 'disease', 'efficacy rate', 'encephalitis', 'encephalopathy', 'epidemic', 'endemic', 'erythema multiforme', 'etiology', 'exposure', 'febrile', 'herd immunity', 'hives', 'hypersensitivity', 'hyposensitivity', 'immune globulin', 'immune system', 'immunity', 'immunization', 'immunosupression', 'inactivated vaccine', 'inapparent infection', 'incidence', 'incubation period', 'infectious', 'infectious agents', 'inflammation', 'inflammatory bowel disease', 'intussusception', 'investigational vaccine', 'jaundice', 'lesion', 'live vaccine', 'lupus', 'lymphocytes', 'macrophage', 'macular', 'memory cell', 'meningoencephalitis', 'microbes', 'mucosal membranes', 'neuritis', 'neuropathy', 'optic neuritis', 'orchitis', 'outbreak', 'pandemicn', 'papular', 'pathogens', 'petechiae', 'placebo', 'pneumonia', 'polysaccharide vaccines', 'potency', 'precaution', 'prevalence', 'prodromal', 'quarantine', 'recombinant', 'reye syndrome', 'residual seizure disorder', 'risk', 'rubella', 'seroconversion', 'serology', 'serosurvey', 'severe combined immune deficiency', 'side effect', 'strain', 'subclinical infection', 'susceptible', 'teratogenic', 'tetanus', 'thimerosal', 'titer', 'transverse myelitis', 'urticaria', 'vaccination', 'vaccinia', 'vaccine', 'vaccine adverse event reporting', 'vaccine safety datalink project', 'variola', 'viremia', 'virulence']\n",
      "\n",
      "Therapeutics listerm:\n",
      "128 words\n",
      "['a', 'absorption rate constant', 'accumulation, accumulation ratio', 'accuracy', 'activity, intrinsic', 'addiction', 'affinity', 'agonist', 'agonist, partial', 'agonist, full', 'agonist, inverse', 'allergic response', 'amplification', 'analgesic', 'anesthetic', 'antagonism', 'area under the curve', 'auc', 'availability', 'b', 'b', 'bioassay or biological assay', 'bioavailability', 'biopharmaceutics', 'biotransformation', 'biotranslocation', 'blind experiment', 'c', 'cl', 'ceiling', 'chemotherapy', 'clearance', 'clinical therapeutic index', 'compartment(s)', 'compliance', 'cross-over experiment', 'cross-tolerance', 'ct index', 'd*', 'd', 'dependence', 'desensitization', 'disintegration time', 'dissolution time', 'distribution', 'dosage form', 'dose', 'dose-duration curve', 'dose-effect curve', 'drug', 'drug abuse', 'drug dependence', 'dummy', 'ec', 'ed', 'effective', 'efficacy', 'elimination rate constant', 'equipotent', 'equivalence', 'experiment', 'f', 'f', 'first-order kinetics', 'first pass effect', 'food and drug administration', 'generic drugs', 'habituation', 'half-life', 'harrison act', 'hazard', 'hypersensitivity', 'hypnotic', 'idiosyncratic response', 'infusion kinetics', 'intrinsic efficacy', 'k', 'latent period or latency', 'loading dose', 'maintenance dose', 'median effective dose', 'metameter', 'multiple dose regimens', 'n', 'narcotic', 'national formulary', 'negative control drug or negative control procedure', 'parameter', 'pharmacodynamics', 'pharmacogenetics', 'pharmacokinetics', 'pharmacology', 'placebo', 'positive control drug', 'potency', 'potentiation', 'priming dose', 'prodrug', 'precision', 'r', 'receptors', 'reference standard', 'reliability', 'risk', 'selectivity', 'sensitivity', 'side effects', 'spare receptors', 'specificity', 'standard drug', 'standardized safety margin', 'supersensitivity', 'synergy', 't', 'τ', 'tachyphylaxis', 'therapeutic index', 'therapeutics', 'threshold dose', 'time-concentration curve', 'tolerance', 'toxic effects', 'toxicology', 'two-state model', 'united states pharmacopoeia', 'validity', 'volume of distribution', 'zero-order kinetics']\n"
     ]
    }
   ],
   "source": [
    "x = [strelement.strip() for strelement in x]\n",
    "y = [strelement.strip() for strelement in y]\n",
    "\n",
    "x = [strelement.lower() for strelement in x]\n",
    "y = [strelement.lower() for strelement in y]\n",
    "\n",
    "print('Vaccines listerm:')\n",
    "print(str(len(x)) + ' words')\n",
    "print(x)\n",
    "print('')\n",
    "print('Therapeutics listerm:')\n",
    "print(str(len(y)) + ' words')\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "mice\n",
      "membran\n",
      "inhibit\n",
      "lung\n",
      "fusion\n",
      "pathogenesi\n",
      "vitro\n",
      "domain\n",
      "cellular\n",
      "neutral\n",
      "-> 0\n",
      "-> 0\n",
      "1\n",
      "revers\n",
      "felin\n",
      "virul\n",
      "attenu\n",
      "real\n",
      "mutant\n",
      "transcript\n",
      "conserv\n",
      "previous\n",
      "antigen\n",
      "-> 0\n",
      "-> 0\n",
      "2\n",
      "innat\n",
      "pathway\n",
      "signal\n",
      "knowledg\n",
      "agent\n",
      "regul\n",
      "research\n",
      "interferon\n",
      "natur\n",
      "evolut\n",
      "-> 0\n",
      "-> 0\n",
      "3\n",
      "hcov\n",
      "neg\n",
      "entri\n",
      "screen\n",
      "hospit\n",
      "known\n",
      "depend\n",
      "challeng\n",
      "swab\n",
      "rang\n",
      "-> 0\n",
      "-> 0\n",
      "4\n",
      "china\n",
      "arabia\n",
      "circul\n",
      "hospit\n",
      "contact\n",
      "laboratori\n",
      "measur\n",
      "popul\n",
      "shed\n",
      "event\n",
      "-> 0\n",
      "-> 0\n",
      "5\n",
      "hcov\n",
      "materi\n",
      "drug\n",
      "articl\n",
      "electron\n",
      "diagnosi\n",
      "amino\n",
      "tissu\n",
      "diagnost\n",
      "obtain\n",
      "-> 0\n",
      "-> 1\n",
      "['None', 'None', 'None', 'None', 'None', 'Therapeutic']\n",
      "265\n",
      "['None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'Therapeutic', 'None', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'None', 'None', 'None', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'None', 'None', 'Therapeutic', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'None', 'None', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'None', 'None', 'None', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'None', 'None', 'Therapeutic', 'None', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'None', 'None', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'None', 'None', 'None', 'None', 'Therapeutic', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'None', 'Therapeutic', 'None']\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cord_uid</th>\n",
       "      <th>sha</th>\n",
       "      <th>source_x</th>\n",
       "      <th>title</th>\n",
       "      <th>doi</th>\n",
       "      <th>pmcid</th>\n",
       "      <th>pubmed_id</th>\n",
       "      <th>license</th>\n",
       "      <th>abstract</th>\n",
       "      <th>publish_time</th>\n",
       "      <th>...</th>\n",
       "      <th>journal</th>\n",
       "      <th>Microsoft Academic Paper ID</th>\n",
       "      <th>WHO #Covidence</th>\n",
       "      <th>arxiv_id</th>\n",
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       "      <th>has_pmc_xml_parse</th>\n",
       "      <th>full_text_file</th>\n",
       "      <th>url</th>\n",
       "      <th>topic</th>\n",
       "      <th>topic_classification</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10514</th>\n",
       "      <td>56fyy9mn</td>\n",
       "      <td>dedff395d93d8023667fc500d7c7c8451d581451</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Antiviral activity of itraconazole against typ...</td>\n",
       "      <td>10.1186/s13567-019-0625-3</td>\n",
       "      <td>PMC6339390</td>\n",
       "      <td>30658691.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Feline coronaviruses (FCoVs) are the causative...</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>Vet Res</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10569</th>\n",
       "      <td>wmi583s4</td>\n",
       "      <td>cd4c984e7afa08f850f9173578fbae31d92a64a5</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Genetic Diversity and Phylodynamics of Avian C...</td>\n",
       "      <td>10.3390/v11010057</td>\n",
       "      <td>PMC6356246</td>\n",
       "      <td>30646528.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Avian coronaviruses (ACoVs) are continuously e...</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>Viruses</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10572</th>\n",
       "      <td>tjp33y35</td>\n",
       "      <td>697cf5b78c388d6e79a7a87ac9b82e4911de135a</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Development of a Whole-Virus ELISA for Serolog...</td>\n",
       "      <td>10.3390/v11010043</td>\n",
       "      <td>PMC6356407</td>\n",
       "      <td>30634419.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Known human coronaviruses are believed to have...</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>Viruses</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10573</th>\n",
       "      <td>olmein3q</td>\n",
       "      <td>19babdefd145a5becbc2a0b189eabdafa697de75</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Bats and Coronaviruses</td>\n",
       "      <td>10.3390/v11010041</td>\n",
       "      <td>PMC6356540</td>\n",
       "      <td>30634396.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Bats are speculated to be reservoirs of severa...</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>Viruses</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10585</th>\n",
       "      <td>2mamwrfq</td>\n",
       "      <td>e68b0f3da8a02a8f0b86869e533eaf549f70ff9a</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Potent MERS-CoV Fusion Inhibitory Peptides Ide...</td>\n",
       "      <td>10.3390/v11010056</td>\n",
       "      <td>PMC6357153</td>\n",
       "      <td>30646495.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>The Middle East respiratory syndrome coronavir...</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>Viruses</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       cord_uid                                       sha source_x  \\\n",
       "10514  56fyy9mn  dedff395d93d8023667fc500d7c7c8451d581451      PMC   \n",
       "10569  wmi583s4  cd4c984e7afa08f850f9173578fbae31d92a64a5      PMC   \n",
       "10572  tjp33y35  697cf5b78c388d6e79a7a87ac9b82e4911de135a      PMC   \n",
       "10573  olmein3q  19babdefd145a5becbc2a0b189eabdafa697de75      PMC   \n",
       "10585  2mamwrfq  e68b0f3da8a02a8f0b86869e533eaf549f70ff9a      PMC   \n",
       "\n",
       "                                                   title  \\\n",
       "10514  Antiviral activity of itraconazole against typ...   \n",
       "10569  Genetic Diversity and Phylodynamics of Avian C...   \n",
       "10572  Development of a Whole-Virus ELISA for Serolog...   \n",
       "10573                             Bats and Coronaviruses   \n",
       "10585  Potent MERS-CoV Fusion Inhibitory Peptides Ide...   \n",
       "\n",
       "                             doi       pmcid   pubmed_id license  \\\n",
       "10514  10.1186/s13567-019-0625-3  PMC6339390  30658691.0   cc-by   \n",
       "10569          10.3390/v11010057  PMC6356246  30646528.0   cc-by   \n",
       "10572          10.3390/v11010043  PMC6356407  30634419.0   cc-by   \n",
       "10573          10.3390/v11010041  PMC6356540  30634396.0   cc-by   \n",
       "10585          10.3390/v11010056  PMC6357153  30646495.0   cc-by   \n",
       "\n",
       "                                                abstract publish_time  \\\n",
       "10514  Feline coronaviruses (FCoVs) are the causative...         2019   \n",
       "10569  Avian coronaviruses (ACoVs) are continuously e...         2019   \n",
       "10572  Known human coronaviruses are believed to have...         2019   \n",
       "10573  Bats are speculated to be reservoirs of severa...         2019   \n",
       "10585  The Middle East respiratory syndrome coronavir...         2019   \n",
       "\n",
       "               ...           journal Microsoft Academic Paper ID  \\\n",
       "10514          ...           Vet Res                         NaN   \n",
       "10569          ...           Viruses                         NaN   \n",
       "10572          ...           Viruses                         NaN   \n",
       "10573          ...           Viruses                         NaN   \n",
       "10585          ...           Viruses                         NaN   \n",
       "\n",
       "       WHO #Covidence arxiv_id has_pdf_parse  has_pmc_xml_parse  \\\n",
       "10514             NaN      NaN          True               True   \n",
       "10569             NaN      NaN          True               True   \n",
       "10572             NaN      NaN          True               True   \n",
       "10573             NaN      NaN          True               True   \n",
       "10585             NaN      NaN          True               True   \n",
       "\n",
       "        full_text_file                                                url  \\\n",
       "10514  comm_use_subset  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "10569  comm_use_subset  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "10572  comm_use_subset  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "10573  comm_use_subset  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "10585  comm_use_subset  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "\n",
       "      topic  topic_classification  \n",
       "10514     2                  None  \n",
       "10569     0                  None  \n",
       "10572     3                  None  \n",
       "10573     2                  None  \n",
       "10585     2                  None  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#vaccineCounter = 0\n",
    "#drugsCounter = 0\n",
    "\n",
    "topicClassification = []\n",
    "\n",
    "for topic,words in topics_words:\n",
    "    print(str(topic))\n",
    "    vaccineCounter = 0\n",
    "    drugsCounter = 0\n",
    "    for word in words:\n",
    "        print(str(word))\n",
    "        if word in x:\n",
    "            #print(word)\n",
    "            vaccineCounter += 1\n",
    "        if word in y:\n",
    "            #print(word)\n",
    "            drugsCounter += 1\n",
    "    \n",
    "    print('-> ' + str(vaccineCounter))\n",
    "    print('-> ' + str(drugsCounter))\n",
    "    \n",
    "    \n",
    "    \n",
    "    if vaccineCounter > drugsCounter:\n",
    "        topicClassification.append('Vaccine')\n",
    "        #cord_db.insert (20, 'topic classification', 'Vaccine')\n",
    "    elif vaccineCounter < drugsCounter:\n",
    "        topicClassification.append('Therapeutic')\n",
    "        #cord_db.insert (20, 'topic classification', 'Therapeutic')\n",
    "    elif vaccineCounter == 0 and drugsCounter == 0:\n",
    "        topicClassification.append('None')\n",
    "        #cord_db.insert (20, 'topic classification', 'None')\n",
    "    elif vaccineCounter == drugsCounter:\n",
    "        topicClassification.append('Tie')\n",
    "        #cord_db.insert (20, 'topic classification', 'None')\n",
    "        \n",
    "    # cord_db.insert (20, \"topic classification\", )\n",
    "    #cord_db.insert (21, 'topic vaccine counter', vaccineCounter)\n",
    "    #cord_db.insert (22, 'topic therapeutic counter', drugsCounter)\n",
    "\n",
    "print(topicClassification)\n",
    "\n",
    "documentClassification = []\n",
    "\n",
    "for i in cord_db['topic']:\n",
    "    if i == 0:\n",
    "        documentClassification.append(topicClassification[0])\n",
    "        #temporal = topicClassification[0]\n",
    "    elif i == 1:\n",
    "        documentClassification.append(topicClassification[1])\n",
    "        #temporal = topicClassification[1]\n",
    "    elif i == 2:\n",
    "        documentClassification.append(topicClassification[2])\n",
    "        #temporal = topicClassification[2]\n",
    "    elif i == 3:\n",
    "        documentClassification.append(topicClassification[3])\n",
    "        #temporal = topicClassification[3]\n",
    "    elif i == 4:\n",
    "        documentClassification.append(topicClassification[4])\n",
    "        #temporal = topicClassification[4]\n",
    "    elif i == 5:\n",
    "        documentClassification.append(topicClassification[5])\n",
    "        #temporal = topicClassification[5]\n",
    "    elif i == 6:\n",
    "        documentClassification.append(topicClassification[6])\n",
    "        #temporal = topicClassification[6]\n",
    "    elif i == 7:\n",
    "        documentClassification.append(topicClassification[7])\n",
    "        #temporal = topicClassification[7]\n",
    "\n",
    "cord_db.insert(20, 'topic_classification', documentClassification)\n",
    "\n",
    "print(len(documentClassification))        \n",
    "print(documentClassification)\n",
    "\n",
    "#print(vaccineCounter)\n",
    "#print(drugsCounter)\n",
    "\n",
    "cord_db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "265\n",
      "['Vaccine', 'Vaccine', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Tie', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Vaccine', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Tie', 'Therapeutic', 'Vaccine', 'Vaccine', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Tie', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Tie', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Vaccine', 'Vaccine', 'Vaccine', 'Vaccine', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Tie', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Tie', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Tie', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Vaccine', 'Vaccine', 'Therapeutic', 'Tie', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Tie', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Tie', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Tie', 'None', 'Therapeutic', 'Tie', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Tie', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Tie', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Vaccine', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Tie', 'Therapeutic', 'Therapeutic', 'Tie', 'Vaccine', 'Vaccine', 'Tie', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Tie', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Tie', 'Vaccine', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Tie', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Tie', 'Therapeutic', 'Vaccine', 'Vaccine', 'Tie', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Vaccine', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Tie', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Vaccine', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Vaccine', 'Vaccine', 'Vaccine', 'Therapeutic', 'Tie', 'Vaccine', 'Vaccine', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Vaccine', 'Therapeutic', 'Vaccine', 'Tie', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Tie', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Vaccine', 'Therapeutic', 'Vaccine', 'Tie', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Tie', 'Therapeutic', 'Therapeutic', 'Therapeutic', 'Vaccine', 'Vaccine', 'Tie', 'Therapeutic', 'Therapeutic', 'Therapeutic']\n"
     ]
    }
   ],
   "source": [
    "absctractsClassification = []\n",
    "abstractVaccinesCounter = []\n",
    "abstractDrugsCounter = []\n",
    "for i in cord_db['abstract']:\n",
    "    temporal = i.split()\n",
    "    #print(temporal)\n",
    "    temporal = [strelement.lower() for strelement in temporal]\n",
    "    #print(temporal)\n",
    "    vaccineCounter = 0\n",
    "    drugsCounter = 0\n",
    "    for word in temporal:\n",
    "        if word in x:\n",
    "            vaccineCounter += 1\n",
    "        if word in y:\n",
    "            drugsCounter += 1\n",
    "    # print('-> ' + str(vaccineCounter))\n",
    "    # print('-> ' + str(drugsCounter))\n",
    "    \n",
    "    abstractVaccinesCounter.append(vaccineCounter)\n",
    "    abstractDrugsCounter.append(drugsCounter)\n",
    "    \n",
    "    if vaccineCounter > drugsCounter:\n",
    "        absctractsClassification.append('Vaccine')\n",
    "    elif vaccineCounter < drugsCounter:\n",
    "        absctractsClassification.append('Therapeutic')\n",
    "    elif vaccineCounter == 0 and drugsCounter == 0:\n",
    "        absctractsClassification.append('None')\n",
    "    elif vaccineCounter == drugsCounter:\n",
    "        absctractsClassification.append('Tie')\n",
    "print(len(absctractsClassification))\n",
    "print(absctractsClassification)\n",
    "\n",
    "cord_db.insert (21, 'abstract_classification', absctractsClassification)\n",
    "cord_db.insert (22, 'abstract_vaccines_counter', abstractVaccinesCounter)\n",
    "cord_db.insert (23, 'abstract_drugs_counter', abstractDrugsCounter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>cord_uid</th>\n",
       "      <th>sha</th>\n",
       "      <th>source_x</th>\n",
       "      <th>title</th>\n",
       "      <th>doi</th>\n",
       "      <th>pmcid</th>\n",
       "      <th>pubmed_id</th>\n",
       "      <th>license</th>\n",
       "      <th>abstract</th>\n",
       "      <th>publish_time</th>\n",
       "      <th>...</th>\n",
       "      <th>arxiv_id</th>\n",
       "      <th>has_pdf_parse</th>\n",
       "      <th>has_pmc_xml_parse</th>\n",
       "      <th>full_text_file</th>\n",
       "      <th>url</th>\n",
       "      <th>topic</th>\n",
       "      <th>topic_classification</th>\n",
       "      <th>abstract_classification</th>\n",
       "      <th>abstract_vaccines_counter</th>\n",
       "      <th>abstract_drugs_counter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10514</th>\n",
       "      <td>56fyy9mn</td>\n",
       "      <td>dedff395d93d8023667fc500d7c7c8451d581451</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Antiviral activity of itraconazole against typ...</td>\n",
       "      <td>10.1186/s13567-019-0625-3</td>\n",
       "      <td>PMC6339390</td>\n",
       "      <td>30658691.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Feline coronaviruses (FCoVs) are the causative...</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>Vaccine</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10569</th>\n",
       "      <td>wmi583s4</td>\n",
       "      <td>cd4c984e7afa08f850f9173578fbae31d92a64a5</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Genetic Diversity and Phylodynamics of Avian C...</td>\n",
       "      <td>10.3390/v11010057</td>\n",
       "      <td>PMC6356246</td>\n",
       "      <td>30646528.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Avian coronaviruses (ACoVs) are continuously e...</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>Vaccine</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10572</th>\n",
       "      <td>tjp33y35</td>\n",
       "      <td>697cf5b78c388d6e79a7a87ac9b82e4911de135a</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Development of a Whole-Virus ELISA for Serolog...</td>\n",
       "      <td>10.3390/v11010043</td>\n",
       "      <td>PMC6356407</td>\n",
       "      <td>30634419.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Known human coronaviruses are believed to have...</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>Therapeutic</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10573</th>\n",
       "      <td>olmein3q</td>\n",
       "      <td>19babdefd145a5becbc2a0b189eabdafa697de75</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Bats and Coronaviruses</td>\n",
       "      <td>10.3390/v11010041</td>\n",
       "      <td>PMC6356540</td>\n",
       "      <td>30634396.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Bats are speculated to be reservoirs of severa...</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>Vaccine</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10585</th>\n",
       "      <td>2mamwrfq</td>\n",
       "      <td>e68b0f3da8a02a8f0b86869e533eaf549f70ff9a</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Potent MERS-CoV Fusion Inhibitory Peptides Ide...</td>\n",
       "      <td>10.3390/v11010056</td>\n",
       "      <td>PMC6357153</td>\n",
       "      <td>30646495.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>The Middle East respiratory syndrome coronavir...</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>Therapeutic</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       cord_uid                                       sha source_x  \\\n",
       "10514  56fyy9mn  dedff395d93d8023667fc500d7c7c8451d581451      PMC   \n",
       "10569  wmi583s4  cd4c984e7afa08f850f9173578fbae31d92a64a5      PMC   \n",
       "10572  tjp33y35  697cf5b78c388d6e79a7a87ac9b82e4911de135a      PMC   \n",
       "10573  olmein3q  19babdefd145a5becbc2a0b189eabdafa697de75      PMC   \n",
       "10585  2mamwrfq  e68b0f3da8a02a8f0b86869e533eaf549f70ff9a      PMC   \n",
       "\n",
       "                                                   title  \\\n",
       "10514  Antiviral activity of itraconazole against typ...   \n",
       "10569  Genetic Diversity and Phylodynamics of Avian C...   \n",
       "10572  Development of a Whole-Virus ELISA for Serolog...   \n",
       "10573                             Bats and Coronaviruses   \n",
       "10585  Potent MERS-CoV Fusion Inhibitory Peptides Ide...   \n",
       "\n",
       "                             doi       pmcid   pubmed_id license  \\\n",
       "10514  10.1186/s13567-019-0625-3  PMC6339390  30658691.0   cc-by   \n",
       "10569          10.3390/v11010057  PMC6356246  30646528.0   cc-by   \n",
       "10572          10.3390/v11010043  PMC6356407  30634419.0   cc-by   \n",
       "10573          10.3390/v11010041  PMC6356540  30634396.0   cc-by   \n",
       "10585          10.3390/v11010056  PMC6357153  30646495.0   cc-by   \n",
       "\n",
       "                                                abstract publish_time  \\\n",
       "10514  Feline coronaviruses (FCoVs) are the causative...         2019   \n",
       "10569  Avian coronaviruses (ACoVs) are continuously e...         2019   \n",
       "10572  Known human coronaviruses are believed to have...         2019   \n",
       "10573  Bats are speculated to be reservoirs of severa...         2019   \n",
       "10585  The Middle East respiratory syndrome coronavir...         2019   \n",
       "\n",
       "                ...           arxiv_id has_pdf_parse  has_pmc_xml_parse  \\\n",
       "10514           ...                NaN          True               True   \n",
       "10569           ...                NaN          True               True   \n",
       "10572           ...                NaN          True               True   \n",
       "10573           ...                NaN          True               True   \n",
       "10585           ...                NaN          True               True   \n",
       "\n",
       "        full_text_file                                                url  \\\n",
       "10514  comm_use_subset  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "10569  comm_use_subset  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "10572  comm_use_subset  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "10573  comm_use_subset  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "10585  comm_use_subset  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "\n",
       "       topic  topic_classification abstract_classification  \\\n",
       "10514      2                  None                 Vaccine   \n",
       "10569      0                  None                 Vaccine   \n",
       "10572      3                  None             Therapeutic   \n",
       "10573      2                  None                 Vaccine   \n",
       "10585      2                  None             Therapeutic   \n",
       "\n",
       "      abstract_vaccines_counter  abstract_drugs_counter  \n",
       "10514                         4                       1  \n",
       "10569                         3                       2  \n",
       "10572                         1                       4  \n",
       "10573                         4                       1  \n",
       "10585                         1                       3  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cord_db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "265\n"
     ]
    }
   ],
   "source": [
    "# cord_db\n",
    "print(len(cord_db))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>cord_uid</th>\n",
       "      <th>sha</th>\n",
       "      <th>source_x</th>\n",
       "      <th>title</th>\n",
       "      <th>doi</th>\n",
       "      <th>pmcid</th>\n",
       "      <th>pubmed_id</th>\n",
       "      <th>license</th>\n",
       "      <th>abstract</th>\n",
       "      <th>publish_time</th>\n",
       "      <th>...</th>\n",
       "      <th>arxiv_id</th>\n",
       "      <th>has_pdf_parse</th>\n",
       "      <th>has_pmc_xml_parse</th>\n",
       "      <th>full_text_file</th>\n",
       "      <th>url</th>\n",
       "      <th>topic</th>\n",
       "      <th>topic_classification</th>\n",
       "      <th>abstract_classification</th>\n",
       "      <th>abstract_vaccines_counter</th>\n",
       "      <th>abstract_drugs_counter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10514</th>\n",
       "      <td>56fyy9mn</td>\n",
       "      <td>dedff395d93d8023667fc500d7c7c8451d581451</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Antiviral activity of itraconazole against typ...</td>\n",
       "      <td>10.1186/s13567-019-0625-3</td>\n",
       "      <td>PMC6339390</td>\n",
       "      <td>30658691.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Feline coronaviruses (FCoVs) are the causative...</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>Vaccine</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10569</th>\n",
       "      <td>wmi583s4</td>\n",
       "      <td>cd4c984e7afa08f850f9173578fbae31d92a64a5</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Genetic Diversity and Phylodynamics of Avian C...</td>\n",
       "      <td>10.3390/v11010057</td>\n",
       "      <td>PMC6356246</td>\n",
       "      <td>30646528.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Avian coronaviruses (ACoVs) are continuously e...</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>Vaccine</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10572</th>\n",
       "      <td>tjp33y35</td>\n",
       "      <td>697cf5b78c388d6e79a7a87ac9b82e4911de135a</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Development of a Whole-Virus ELISA for Serolog...</td>\n",
       "      <td>10.3390/v11010043</td>\n",
       "      <td>PMC6356407</td>\n",
       "      <td>30634419.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Known human coronaviruses are believed to have...</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>3</td>\n",
       "      <td>None</td>\n",
       "      <td>Therapeutic</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10573</th>\n",
       "      <td>olmein3q</td>\n",
       "      <td>19babdefd145a5becbc2a0b189eabdafa697de75</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Bats and Coronaviruses</td>\n",
       "      <td>10.3390/v11010041</td>\n",
       "      <td>PMC6356540</td>\n",
       "      <td>30634396.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>Bats are speculated to be reservoirs of severa...</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>Vaccine</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10585</th>\n",
       "      <td>2mamwrfq</td>\n",
       "      <td>e68b0f3da8a02a8f0b86869e533eaf549f70ff9a</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Potent MERS-CoV Fusion Inhibitory Peptides Ide...</td>\n",
       "      <td>10.3390/v11010056</td>\n",
       "      <td>PMC6357153</td>\n",
       "      <td>30646495.0</td>\n",
       "      <td>cc-by</td>\n",
       "      <td>The Middle East respiratory syndrome coronavir...</td>\n",
       "      <td>2019</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>comm_use_subset</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>Therapeutic</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       cord_uid                                       sha source_x  \\\n",
       "10514  56fyy9mn  dedff395d93d8023667fc500d7c7c8451d581451      PMC   \n",
       "10569  wmi583s4  cd4c984e7afa08f850f9173578fbae31d92a64a5      PMC   \n",
       "10572  tjp33y35  697cf5b78c388d6e79a7a87ac9b82e4911de135a      PMC   \n",
       "10573  olmein3q  19babdefd145a5becbc2a0b189eabdafa697de75      PMC   \n",
       "10585  2mamwrfq  e68b0f3da8a02a8f0b86869e533eaf549f70ff9a      PMC   \n",
       "\n",
       "                                                   title  \\\n",
       "10514  Antiviral activity of itraconazole against typ...   \n",
       "10569  Genetic Diversity and Phylodynamics of Avian C...   \n",
       "10572  Development of a Whole-Virus ELISA for Serolog...   \n",
       "10573                             Bats and Coronaviruses   \n",
       "10585  Potent MERS-CoV Fusion Inhibitory Peptides Ide...   \n",
       "\n",
       "                             doi       pmcid   pubmed_id license  \\\n",
       "10514  10.1186/s13567-019-0625-3  PMC6339390  30658691.0   cc-by   \n",
       "10569          10.3390/v11010057  PMC6356246  30646528.0   cc-by   \n",
       "10572          10.3390/v11010043  PMC6356407  30634419.0   cc-by   \n",
       "10573          10.3390/v11010041  PMC6356540  30634396.0   cc-by   \n",
       "10585          10.3390/v11010056  PMC6357153  30646495.0   cc-by   \n",
       "\n",
       "                                                abstract publish_time  \\\n",
       "10514  Feline coronaviruses (FCoVs) are the causative...         2019   \n",
       "10569  Avian coronaviruses (ACoVs) are continuously e...         2019   \n",
       "10572  Known human coronaviruses are believed to have...         2019   \n",
       "10573  Bats are speculated to be reservoirs of severa...         2019   \n",
       "10585  The Middle East respiratory syndrome coronavir...         2019   \n",
       "\n",
       "                ...           arxiv_id has_pdf_parse  has_pmc_xml_parse  \\\n",
       "10514           ...                NaN          True               True   \n",
       "10569           ...                NaN          True               True   \n",
       "10572           ...                NaN          True               True   \n",
       "10573           ...                NaN          True               True   \n",
       "10585           ...                NaN          True               True   \n",
       "\n",
       "        full_text_file                                                url  \\\n",
       "10514  comm_use_subset  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "10569  comm_use_subset  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "10572  comm_use_subset  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "10573  comm_use_subset  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "10585  comm_use_subset  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6...   \n",
       "\n",
       "       topic  topic_classification abstract_classification  \\\n",
       "10514      2                  None                 Vaccine   \n",
       "10569      0                  None                 Vaccine   \n",
       "10572      3                  None             Therapeutic   \n",
       "10573      2                  None                 Vaccine   \n",
       "10585      2                  None             Therapeutic   \n",
       "\n",
       "      abstract_vaccines_counter  abstract_drugs_counter  \n",
       "10514                         4                       1  \n",
       "10569                         3                       2  \n",
       "10572                         1                       4  \n",
       "10573                         4                       1  \n",
       "10585                         1                       3  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cord_db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "35\n",
      "0\n",
      "230\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cord_db\n",
    "\n",
    "# topic_classification\n",
    "\n",
    "# abstract_classification\n",
    "# abstract_vaccines_counter\n",
    "# abstract_drugs_counter\n",
    "\n",
    "vaccineFrequency = 0\n",
    "therapeuticsFrecuency = 0\n",
    "tieFrequency = 0\n",
    "noneFrequency = 0\n",
    "\n",
    "for i in cord_db['topic_classification']:\n",
    "    if i == 'Vaccine':\n",
    "        vaccineFrequency += 1\n",
    "    elif i == 'Therapeutic':\n",
    "        therapeuticsFrecuency += 1\n",
    "    elif i == 'Tie':\n",
    "        tieFrequency += 1\n",
    "    elif i == 'None':\n",
    "        noneFrequency += 1\n",
    "\n",
    "print(vaccineFrequency)\n",
    "print(therapeuticsFrecuency)\n",
    "print(tieFrequency)\n",
    "print(noneFrequency)\n",
    "\n",
    "topics_list = ['Vaccine','Therapeutic','Tie','None']\n",
    "frequency_list = [vaccineFrequency, therapeuticsFrecuency, tieFrequency, noneFrequency]\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(topics_list,frequency_list)\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_xlabel('Classification')\n",
    "ax.set_title('Topic classification based on topic words')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73\n",
      "162\n",
      "29\n",
      "1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "vaccineFrequency = 0\n",
    "therapeuticsFrecuency = 0\n",
    "tieFrequency = 0\n",
    "noneFrequency = 0\n",
    "\n",
    "for i in cord_db['abstract_classification']:\n",
    "    if i == 'Vaccine':\n",
    "        vaccineFrequency += 1\n",
    "    elif i == 'Therapeutic':\n",
    "        therapeuticsFrecuency += 1\n",
    "    elif i == 'Tie':\n",
    "        tieFrequency += 1\n",
    "    elif i == 'None':\n",
    "        noneFrequency += 1\n",
    "\n",
    "print(vaccineFrequency)\n",
    "print(therapeuticsFrecuency)\n",
    "print(tieFrequency)\n",
    "print(noneFrequency)\n",
    "\n",
    "topics_list = ['Vaccine','Therapeutic','Tie','None']\n",
    "frequency_list = [vaccineFrequency, therapeuticsFrecuency, tieFrequency, noneFrequency]\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(topics_list,frequency_list)\n",
    "ax.set_ylabel('Frequency')\n",
    "ax.set_xlabel('Classification')\n",
    "ax.set_title('Abstract classification based on topic words')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "x = cord_db['abstract_vaccines_counter']\n",
    "x = x.values.tolist()\n",
    "y = cord_db['abstract_drugs_counter']\n",
    "y = y.values.tolist()\n",
    "\n",
    "#print(x[0])\n",
    "\n",
    "\"\"\"\n",
    "print(type(x))\n",
    "print(x)\n",
    "print(type(y))\n",
    "print(y)\n",
    "\"\"\"\n",
    "\n",
    "\"\"\"\n",
    "plt.scatter(x,y)\n",
    "plt.title(\"Classification tendency of document abstracts\")\n",
    "plt.xlabel(\"Vaccines\")\n",
    "plt.ylabel(\"Therapeutics\")\n",
    "plt.show()\n",
    "\"\"\"\n",
    "\n",
    "label = []\n",
    "\n",
    "for i in range(len(x)):\n",
    "    \n",
    "    if x[i] > y[i]:\n",
    "        label.append(0)\n",
    "    elif x[i] < y[i]:\n",
    "        label.append(1)\n",
    "    elif x[i] == y[i]:\n",
    "        label.append(2)\n",
    "    elif x[i] == 0 and y[i] == 0:\n",
    "        label.append(3)\n",
    "    \n",
    "    # print(i)\n",
    "\n",
    "\n",
    "colors = ['red','green','blue','purple']\n",
    "\n",
    "fig = plt.figure(figsize=(5,5))\n",
    "plt.scatter(x, y, c=label, cmap=matplotlib.colors.ListedColormap(colors))\n",
    "plt.title(\"Classification tendency of document abstracts\")\n",
    "plt.xlabel(\"Vaccines\")\n",
    "plt.ylabel(\"Therapeutics\")\n",
    "cb = plt.colorbar()\n",
    "loc = np.arange(0,max(label),max(label)/float(len(colors)))\n",
    "cb.set_ticks(loc)\n",
    "cb.set_ticklabels(colors)\n",
    "\n",
    "\n",
    "# abstract_vaccines_counter > abstract_drugs_counter\n",
    "# abstract_vaccines_counter < abstract_drugs_counter\n",
    "# abstract_vaccines_counter == abstract_drugs_counter\n",
    "# abstract_vaccines_counter == 0 and abstract_drugs_counter == 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "265\n",
      "265\n",
      "[2, 0, 3, 4, 5, 1]\n",
      "[0, 1, 2, 3, 4, 5]\n",
      "{0: {'Vaccine': 13, 'Therapeutic': 36, 'Tie': 7}, 1: {'Therapeutic': 17, 'Vaccine': 8, 'Tie': 2}, 2: {'Vaccine': 10, 'Therapeutic': 19, 'Tie': 4}, 3: {'Therapeutic': 23, 'Vaccine': 7, 'Tie': 5}, 4: {'Vaccine': 25, 'Therapeutic': 44, 'Tie': 9, 'None': 1}, 5: {'Vaccine': 10, 'Therapeutic': 23, 'Tie': 2}}\n",
      "{0: {'Vaccine': 13, 'Therapeutic': 36, 'Tie': 7}, 1: {'Therapeutic': 17, 'Vaccine': 8, 'Tie': 2}, 2: {'Vaccine': 10, 'Therapeutic': 19, 'Tie': 4}, 3: {'Therapeutic': 23, 'Vaccine': 7, 'Tie': 5}, 4: {'Vaccine': 25, 'Therapeutic': 44, 'Tie': 9, 'None': 1}, 5: {'Vaccine': 10, 'Therapeutic': 23, 'Tie': 2}}\n",
      "{0: {'Vaccine': 13, 'Therapeutic': 36}, 1: {'Therapeutic': 17, 'Vaccine': 8}, 2: {'Vaccine': 10, 'Therapeutic': 19}, 3: {'Therapeutic': 23, 'Vaccine': 7}, 4: {'Vaccine': 25, 'Therapeutic': 44}, 5: {'Vaccine': 10, 'Therapeutic': 23}}\n",
      "6\n",
      "6\n",
      "6\n",
      "[0, 1, 2, 3, 4, 5]\n",
      "[13, 8, 10, 7, 25, 10]\n",
      "[36, 17, 19, 23, 44, 23]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Gráfica\n",
    "# x = x.values.tolist()\n",
    "topics = cord_db['topic']\n",
    "topics = topics.values.tolist()\n",
    "classifications = cord_db['abstract_classification']\n",
    "classifications = classifications.values.tolist()\n",
    "print(len(topics))\n",
    "print(len(classifications))\n",
    "\n",
    "notRepeatedTopics = []\n",
    "for i in topics:\n",
    "    if i not in notRepeatedTopics:\n",
    "        notRepeatedTopics.append(i)\n",
    "print(notRepeatedTopics)\n",
    "notRepeatedTopics.sort()\n",
    "print(notRepeatedTopics)\n",
    "generalDictionary = {}\n",
    "\n",
    "for i in notRepeatedTopics:\n",
    "    generalDictionary[i] = {}\n",
    "\n",
    "#print(generalDictionary)\n",
    "for i in range(len(topics)):\n",
    "    if classifications[i] not in generalDictionary[topics[i]]:\n",
    "        generalDictionary[topics[i]][classifications[i]] = 1\n",
    "    else:\n",
    "        generalDictionary[topics[i]][classifications[i]] += 1\n",
    "\n",
    "print(generalDictionary)\n",
    "\n",
    "for i in generalDictionary:\n",
    "    sorted(generalDictionary[i])\n",
    "\n",
    "print(generalDictionary)\n",
    "\n",
    "for i in generalDictionary:\n",
    "    if 'Tie' in generalDictionary[i]:\n",
    "        del(generalDictionary[i]['Tie'])\n",
    "    if 'None' in generalDictionary[i]:\n",
    "        del(generalDictionary[i]['None'])\n",
    "\n",
    "print(generalDictionary)\n",
    "\n",
    "for i in generalDictionary:\n",
    "    sorted(generalDictionary[i].items())\n",
    "\n",
    "finalTopicsList = []\n",
    "finalVaccinesList = []\n",
    "finalTherapeuticsList = []\n",
    "\n",
    "for i in generalDictionary:\n",
    "    finalTopicsList.append(i)\n",
    "    finalVaccinesList.append(generalDictionary[i]['Vaccine'])\n",
    "    finalTherapeuticsList.append(generalDictionary[i]['Therapeutic'])\n",
    "\n",
    "print(len(finalTopicsList))\n",
    "print(len(finalVaccinesList))\n",
    "print(len(finalTherapeuticsList))\n",
    "\n",
    "print(finalTopicsList)\n",
    "print(finalVaccinesList)\n",
    "print(finalTherapeuticsList)\n",
    "\n",
    "#labels = ['G1', 'G2', 'G3', 'G4', 'G5']\n",
    "#men_means = [20, 34, 30, 35, 27]\n",
    "#women_means = [25, 32, 34, 20, 25]\n",
    "\n",
    "x = np.arange(len(finalTopicsList))  # the label locations\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(x - width/2, finalVaccinesList, width, label='Vaccines')\n",
    "rects2 = ax.bar(x + width/2, finalTherapeuticsList, width, label='Therapeutics')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_xlabel('Topics')\n",
    "ax.set_ylabel('Occurrences')\n",
    "ax.set_title('Occurrences of vaccines and therapeutics by topic')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(finalTopicsList)\n",
    "ax.legend()\n",
    "\n",
    "\n",
    "def autolabel(rects):\n",
    "    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n",
    "    for rect in rects:\n",
    "        height = rect.get_height()\n",
    "        ax.annotate('{}'.format(height),\n",
    "                    xy=(rect.get_x() + rect.get_width() / 2, height),\n",
    "                    xytext=(0, 3),  # 3 points vertical offset\n",
    "                    textcoords=\"offset points\",\n",
    "                    ha='center', va='bottom')\n",
    "\n",
    "\n",
    "autolabel(rects1)\n",
    "autolabel(rects2)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function will return the list of file paths of an specific topic\n",
    "def path_from_topic(topic):\n",
    "    path_list = []\n",
    "    cord_topic = cord_db[cord_db.topic == topic]\n",
    "    for r in range(1,len(cord_topic)):\n",
    "        sha = cord_topic.iloc[r].sha\n",
    "        if ';' in sha:\n",
    "            sha = sha.split(';')[0]\n",
    "        path_list.append(current_location+cord_topic.iloc[r].full_text_file+'/'+cord_topic.iloc[r].full_text_file+'/'+'pdf_json/'+sha+'.json')\n",
    "    return path_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['CORD-19-research-challenge/custom_license/custom_license/pdf_json/cc6833142021d7b1b0aeb03e102b7fb29269a426.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/27c24bc25e5f22f0771e1fcb3276c728fb446828.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/a799d4baa0f252468dcbeaa466fbed7736bf20c5.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/5b626729d78325a28a47be0f1b847ad6925d3473.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/5581ec03ebdca073e13b0df6329779940908f038.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/3f8beb2781bb92d1e2fffd8361b1feec3fd834e9.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/f93195034f642c748faae11cb58300881eb7b2bb.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/7e2cac5308e11493311bc4a3e8d15c9b9e7885ad.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/cbb9ec2328fdea46172cc0dd7f8fa8be82256c93.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/a25c99e62ee57a207f77bcba9f484a2416f11849.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/2af60a6edf5657d366fc1f0ee1f3fda18b9418a3.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/8eacedf54b52b89b270a2e148459b6d0ca6898ee.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/6673835e81ea115480bd68bc4e2d90c742bb77c1.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/7aaffdb8cf80ab9dd167a26a44abfa21d30d2af5.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/c66b53fa3a31eb9fcc20c56830c89061b7caed49.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/35906518687a56bbe629ae4b4c9002b0267d8a93.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/f98bb6e4c1800088df79a34e56f3731bee6c20c2.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/27ee82e4db05cf49d789f25d14660daffe743cfd.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/58bb89f7722922bd9a47cd49e172c498d5cd73cf.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/59346a784647136a6502277e4c78cf912c551e8f.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/4bf70dfd0cd0819f2eb424d977abedc85f5444d1.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/ba44a0c7d9af83e5fe417c92353af491ce4dc035.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/3c9db7ee30074667f9fe308de9910b013aa1f163.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/0dcf0124cf00dbf2e2bf0025fee1bba8a57f81da.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/284053d25cd426f6121c3fed5ea6a017717853ed.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/d3618a7956fc136c7183df5b9c665e96a3df8748.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/9235abaf9b34976f68a8d0031a94201fe371d364.json', 'CORD-19-research-challenge/noncomm_use_subset/noncomm_use_subset/pdf_json/a94fd30a8dfce024118696e454bbe0f0e41840d9.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/a912f654028e787673b235c9ba2eb2d161e372aa.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/3cf41b596058f2d1b07760563aa6c5316b014af2.json', 'CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/6091444b4f7495b1b4cb0c72b6f30da73ccf867d.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/435b2c1a312dad2b0d70591efe688612cc47fe19.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/b5f25901015340a5312e3c9f6b4fc7b1a6744df9.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/209360b738197a63e9c3a7c3f2360cbf2a4901e2.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/ca9cf0e9944de44362d7464ef6eccac41b97a118.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/f5dde1a2a4014c9d44a6ad4c59a9606c9040e8ea.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/9b2209190acc4d0c8adf1893681612471fb0aa5b.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/408a0047425c5d198c15a6282d0468642bd78730.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/593d2c760dc9b6081415084fef822aa1197567c7.json', 'CORD-19-research-challenge/noncomm_use_subset/noncomm_use_subset/pdf_json/79f0b2ac5c9b5132d0dd901ce509ad106b1ff570.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/4f16dfb454e871fddc2252b9a072b5d0d15bc05e.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/bc857b3d3f00b258346c09ec347d70d3e942f156.json', 'CORD-19-research-challenge/biorxiv_medrxiv/biorxiv_medrxiv/pdf_json/73d80c8f5780d70bd8d343188c56e898e91557b6.json', 'CORD-19-research-challenge/biorxiv_medrxiv/biorxiv_medrxiv/pdf_json/c08c3b98a956287a6eaf99f6d90969d7dd1410d1.json', 'CORD-19-research-challenge/biorxiv_medrxiv/biorxiv_medrxiv/pdf_json/f863247dc84916a96448088399badefd09d54fb8.json', 'CORD-19-research-challenge/biorxiv_medrxiv/biorxiv_medrxiv/pdf_json/3c12e50b9da4b1fde21d68040610b73b8c216bf3.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/a73149de69132032bc769a2d43c6a98cf663a069.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/9bbcdc1db7feeedcd55ba7f4e429f2c8358b4221.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/af13909503333083aafe453960a51245b0486595.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/cd90be0889655b15519305753b9433e3ce66a5c0.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/5f1985c2a2b2dbb9c75d9197d0d4a83186579711.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/ceb22d138e9de7cd16dc6f7e53c5e8357e25a775.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/6a6f2e1a09c7bf12adb288a9adaa9925d158da5b.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/a73b3282c16605244dafb77300b9c5ae000e84c0.json', 'CORD-19-research-challenge/custom_license/custom_license/pdf_json/cca222b4308f19b432c1d9ebbdafa7bb584595c7.json']\n"
     ]
    }
   ],
   "source": [
    "# Read literature documents as JSON\n",
    "topic_zero = path_from_topic(0)\n",
    "topic_four = path_from_topic(4)\n",
    "print(topic_zero)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function will return the plain text on a specific document\n",
    "def json_to_text(file):\n",
    "    title = file['metadata']['title']\n",
    "    txt = title + \"\\n\"\n",
    "    if len(file['abstract']) >= 1:\n",
    "        txt = txt + file['abstract'][0]['text'] + \"\\n\"\n",
    "    for k in file['body_text']:\n",
    "        if k['text'] is not None:\n",
    "            txt = txt + k['text'] + \"\\n\"\n",
    "    return title, txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Data:\n",
    "  def __init__(self, title, text, label, predicted=None):\n",
    "    self.title = str(title)\n",
    "    self.text = str(text)\n",
    "    self.label = label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/cc6833142021d7b1b0aeb03e102b7fb29269a426.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/27c24bc25e5f22f0771e1fcb3276c728fb446828.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/a799d4baa0f252468dcbeaa466fbed7736bf20c5.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/5b626729d78325a28a47be0f1b847ad6925d3473.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/5581ec03ebdca073e13b0df6329779940908f038.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/3f8beb2781bb92d1e2fffd8361b1feec3fd834e9.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/f93195034f642c748faae11cb58300881eb7b2bb.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/7e2cac5308e11493311bc4a3e8d15c9b9e7885ad.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/cbb9ec2328fdea46172cc0dd7f8fa8be82256c93.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/a25c99e62ee57a207f77bcba9f484a2416f11849.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/2af60a6edf5657d366fc1f0ee1f3fda18b9418a3.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/8eacedf54b52b89b270a2e148459b6d0ca6898ee.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/6673835e81ea115480bd68bc4e2d90c742bb77c1.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/7aaffdb8cf80ab9dd167a26a44abfa21d30d2af5.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/c66b53fa3a31eb9fcc20c56830c89061b7caed49.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/35906518687a56bbe629ae4b4c9002b0267d8a93.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/f98bb6e4c1800088df79a34e56f3731bee6c20c2.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/27ee82e4db05cf49d789f25d14660daffe743cfd.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/58bb89f7722922bd9a47cd49e172c498d5cd73cf.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/59346a784647136a6502277e4c78cf912c551e8f.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/4bf70dfd0cd0819f2eb424d977abedc85f5444d1.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/ba44a0c7d9af83e5fe417c92353af491ce4dc035.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/3c9db7ee30074667f9fe308de9910b013aa1f163.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/0dcf0124cf00dbf2e2bf0025fee1bba8a57f81da.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/284053d25cd426f6121c3fed5ea6a017717853ed.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/d3618a7956fc136c7183df5b9c665e96a3df8748.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/9235abaf9b34976f68a8d0031a94201fe371d364.json\n",
      "CORD-19-research-challenge/noncomm_use_subset/noncomm_use_subset/pdf_json/a94fd30a8dfce024118696e454bbe0f0e41840d9.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/a912f654028e787673b235c9ba2eb2d161e372aa.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/3cf41b596058f2d1b07760563aa6c5316b014af2.json\n",
      "CORD-19-research-challenge/comm_use_subset/comm_use_subset/pdf_json/6091444b4f7495b1b4cb0c72b6f30da73ccf867d.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/435b2c1a312dad2b0d70591efe688612cc47fe19.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/b5f25901015340a5312e3c9f6b4fc7b1a6744df9.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/209360b738197a63e9c3a7c3f2360cbf2a4901e2.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/ca9cf0e9944de44362d7464ef6eccac41b97a118.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/f5dde1a2a4014c9d44a6ad4c59a9606c9040e8ea.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/9b2209190acc4d0c8adf1893681612471fb0aa5b.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/408a0047425c5d198c15a6282d0468642bd78730.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/593d2c760dc9b6081415084fef822aa1197567c7.json\n",
      "CORD-19-research-challenge/noncomm_use_subset/noncomm_use_subset/pdf_json/79f0b2ac5c9b5132d0dd901ce509ad106b1ff570.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/4f16dfb454e871fddc2252b9a072b5d0d15bc05e.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/bc857b3d3f00b258346c09ec347d70d3e942f156.json\n",
      "CORD-19-research-challenge/biorxiv_medrxiv/biorxiv_medrxiv/pdf_json/73d80c8f5780d70bd8d343188c56e898e91557b6.json\n",
      "CORD-19-research-challenge/biorxiv_medrxiv/biorxiv_medrxiv/pdf_json/c08c3b98a956287a6eaf99f6d90969d7dd1410d1.json\n",
      "CORD-19-research-challenge/biorxiv_medrxiv/biorxiv_medrxiv/pdf_json/f863247dc84916a96448088399badefd09d54fb8.json\n",
      "CORD-19-research-challenge/biorxiv_medrxiv/biorxiv_medrxiv/pdf_json/3c12e50b9da4b1fde21d68040610b73b8c216bf3.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/a73149de69132032bc769a2d43c6a98cf663a069.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/9bbcdc1db7feeedcd55ba7f4e429f2c8358b4221.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/af13909503333083aafe453960a51245b0486595.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/cd90be0889655b15519305753b9433e3ce66a5c0.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/5f1985c2a2b2dbb9c75d9197d0d4a83186579711.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/ceb22d138e9de7cd16dc6f7e53c5e8357e25a775.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/6a6f2e1a09c7bf12adb288a9adaa9925d158da5b.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/a73b3282c16605244dafb77300b9c5ae000e84c0.json\n",
      "CORD-19-research-challenge/custom_license/custom_license/pdf_json/cca222b4308f19b432c1d9ebbdafa7bb584595c7.json\n"
     ]
    }
   ],
   "source": [
    "# This module read the json files in a topic and tokenize them\n",
    "# For topic zero considered as Therapeutics label as 0\n",
    "import json\n",
    "\n",
    "topic_docs_zero = []\n",
    "\n",
    "for p in topic_zero:\n",
    "    print(p)\n",
    "    with open(p) as f:\n",
    "        file = json.load(f)\n",
    "        title, txt = json_to_text(file)\n",
    "        if title is \"\":\n",
    "            title = p\n",
    "        topic_docs_zero.append(Data(title,txt,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# This module read the json files in a topic and tokenize them\n",
    "# For topic zero considered as Vaccines label as 1\n",
    "import json\n",
    "\n",
    "topic_docs_four = []\n",
    "\n",
    "for p in topic_four:\n",
    "    print(p)\n",
    "    with open(p) as f:\n",
    "        file = json.load(f)\n",
    "        title, txt = json_to_text(file)\n",
    "        if title is \"\":\n",
    "            title = p\n",
    "        topic_docs_four.append(Data(title,txt,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get input text from reduced literature only \"Therapeutics and Vaccines\"\n",
    "data = topic_docs_zero + topic_docs_four"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
    "\n",
    "# Builds a One-vs-rest model using Logistic Regression with TfidfVectorizer on Threatments and Vaccines literature\n",
    "def sklearn_ovr_model(data_train, data_validation):\n",
    "    y_train = [t.label for t in data_train]\n",
    "    y_test = [v.label for v in data_validation]\n",
    "    docs_train = [t.text for t in data_train]\n",
    "    docs_test = [v.text for v in data_validation]\n",
    "\n",
    "    vectorizer = TfidfVectorizer()\n",
    "    X_train = vectorizer.fit_transform(docs_train)\n",
    "    logisReg = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr').fit(X_train, y_train)\n",
    "    X_test = vectorizer.transform(docs_test)\n",
    "    y_hat = logisReg.predict(X_test)\n",
    "    \n",
    "    # Updates docs_test prediction\n",
    "    for i in range(0,len(data_validation)):\n",
    "        data_validation[i].predicted = y_hat[i]\n",
    "    \n",
    "    print(\"Number of trainning documents: \", len(y_train))\n",
    "    print(\"Number of validation documents: \", len(y_test))\n",
    "    print(\"Logistic Regression accuracy is: \", accuracy_score(y_test, y_hat))\n",
    "    print(\"Confusion Matrix:\\n\", confusion_matrix(y_test, y_hat))\n",
    "    print(\"Classification Report:\\n\", classification_report(y_test, y_hat))\n",
    "    \n",
    "    return y_test, y_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function plots a visual confusion matrix\n",
    "from sklearn.utils.multiclass import unique_labels\n",
    "\n",
    "def plot_confusion_matrix(y_true, y_pred, classes,\n",
    "                          normalize=False,\n",
    "                          title=None,\n",
    "                          cmap=plt.cm.Blues):\n",
    "    \"\"\"\n",
    "    This function prints and plots the confusion matrix.\n",
    "    Normalization can be applied by setting `normalize=True`.\n",
    "    \"\"\"\n",
    "    if not title:\n",
    "        if normalize:\n",
    "            title = 'Normalized confusion matrix'\n",
    "        else:\n",
    "            title = 'Confusion matrix, without normalization'\n",
    "\n",
    "    # Compute confusion matrix\n",
    "    cm = confusion_matrix(y_true, y_pred)\n",
    "    if normalize:\n",
    "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "        print(\"Normalized confusion matrix\")\n",
    "    else:\n",
    "        print('Confusion matrix, without normalization')\n",
    "\n",
    "    print(cm)\n",
    "\n",
    "    fig, ax = plt.subplots()\n",
    "    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    ax.figure.colorbar(im, ax=ax)\n",
    "    # We want to show all ticks...\n",
    "    ax.set(xticks=np.arange(cm.shape[1]),\n",
    "           yticks=np.arange(cm.shape[0]),\n",
    "           # ... and label them with the respective list entries\n",
    "           xticklabels=classes, yticklabels=classes,\n",
    "           title=title,\n",
    "           ylabel='True label',\n",
    "           xlabel='Predicted label')\n",
    "\n",
    "    # Rotate the tick labels and set their alignment.\n",
    "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
    "             rotation_mode=\"anchor\")\n",
    "\n",
    "    # Loop over data dimensions and create text annotations.\n",
    "    fmt = '.2f' if normalize else 'd'\n",
    "    thresh = cm.max() / 2.\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            ax.text(j, i, format(cm[i, j], fmt),\n",
    "                    ha=\"center\", va=\"center\",\n",
    "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    fig.tight_layout()\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of trainning documents:  106\n",
      "Number of validation documents:  27\n",
      "Logistic Regression accuracy is:  0.6296296296296297\n",
      "Confusion Matrix:\n",
      " [[ 5  9]\n",
      " [ 1 12]]\n",
      "Classification Report:\n",
      "               precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.36      0.50        14\n",
      "           1       0.57      0.92      0.71        13\n",
      "\n",
      "   micro avg       0.63      0.63      0.63        27\n",
      "   macro avg       0.70      0.64      0.60        27\n",
      "weighted avg       0.71      0.63      0.60        27\n",
      "\n",
      "Normalized confusion matrix\n",
      "[[0.35714286 0.64285714]\n",
      " [0.07692308 0.92307692]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot normalized confusion matrix using sklearn model\n",
    "data_train, data_validation = train_test_split(data, test_size=0.2)\n",
    "y_test, y_hat = sklearn_ovr_model(data_train, data_validation)\n",
    "plot_confusion_matrix(y_test, y_hat, classes=[\"Therapeutics\",\"Vaccines\"], normalize=True,\n",
    "                      title='Confusion matrix')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "|    | Title                                                                                                                                                                                                                                            |   Label |   Predicted |\n",
      "+====+==================================================================================================================================================================================================================================================+=========+=============+\n",
      "|  0 | Clinical predictors of mortality of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) infection: A cohort study                                                                                                                            |       1 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "|  1 | The coronavirus macrodomain is required to prevent PARP-mediated inhibition of virus replication and enhancement of IFN expression                                                                                                               |       0 |           0 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "|  2 | Ca 2+ ions promote fusion of Middle East Respiratory Syndrome coronavirus with host cells and increase infectivity                                                                                                                               |       0 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "|  3 | Molecular Study and Phylogenetic Analysis of Middle East Respiratory Syndrome Corona Virus (MERSCoV) in Camel and Human                                                                                                                          |       1 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "|  4 | Bactrian camels shed large quantities of Middle East respiratory syndrome coronavirus (MERS-CoV) after experimental infection*                                                                                                                   |       1 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "|  5 | viruses Human Coronaviruses and Other Respiratory Viruses: Underestimated Opportunistic Pathogens of the Central Nervous System?                                                                                                                 |       1 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "|  6 | Emerging lethal infectious bronchitis coronavirus variants with multiorgan tropism                                                                                                                                                               |       1 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "|  7 | The human coronavirus HCoV-229E S-protein structure and receptor binding                                                                                                                                                                         |       0 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "|  8 | Clinical outcomes among hospital patients with Middle East respiratory syndrome coronavirus (MERS-CoV) infection                                                                                                                                 |       1 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "|  9 | Relationship between circulating levels of angiotensin-converting enzyme 2-angiotensin-(1-7)-MAS axis and coronary heart disease                                                                                                                 |       1 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 10 | Coronavirus Endoribonuclease Activity in Porcine Epidemic Diarrhea Virus Suppresses Type I and Type III Interferon Responses                                                                                                                     |       0 |           0 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 11 | The Journal of Infectious Diseases A Highly Immunogenic, Protective, and Safe Adenovirus- Based Vaccine Expressing Middle East Respiratory Syndrome Coronavirus S1-CD40L Fusion Protein in a Transgenic Human Dipeptidyl Peptidase 4 Mouse Model |       0 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 12 | Synthesis and anti-coronavirus activity of a series of 1-thia-4-azaspiro[4.5]decan-3-one derivatives                                                                                                                                             |       0 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 13 | The Infectious Bronchitis Coronavirus Envelope Protein Alters Golgi pH To Protect the Spike Protein and Promote the Release of Infectious Virus                                                                                                  |       0 |           0 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 14 | CORD-19-research-challenge/noncomm_use_subset/noncomm_use_subset/pdf_json/e52ecf4b22e9dcb340ccf67f0251b5a3f9373f02.json                                                                                                                          |       1 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 15 | Chapter 8 Quantification of the Middle East Respiratory Syndrome- Coronavirus RNA in Tissues by Quantitative Real-Time RT-PCR                                                                                                                    |       0 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 16 | Towards a solution to MERS: protective human monoclonal antibodies targeting different domains and functions of the MERS-coronavirus spike glycoprotein                                                                                          |       0 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 17 | Characterization and Pathogenicity of the Porcine Deltacoronavirus Isolated in Southwest China                                                                                                                                                   |       1 |           0 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 18 | Characteristics of the Life Cycle of Porcine Deltacoronavirus (PDCoV) In Vitro: Replication Kinetics, Cellular Ultrastructure and Virion Morphology, and Evidence of Inducing Autophagy                                                          |       0 |           0 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 19 | Increasing the translation of mouse models of MERS coronavirus pathogenesis through kinetic hematological analysis                                                                                                                               |       0 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 20 | MERS coronavirus outbreak: Implications for emerging viral infections                                                                                                                                                                            |       1 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 21 | Lack of Middle East Respiratory Syndrome Coronavirus Transmission in Rabbits                                                                                                                                                                     |       1 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 22 | The deubiquitinating activity of Middle East respiratory syndrome 2 coronavirus papain-like protease delays the innate immune response 3 and enhances virulence in a mouse model 4 5 Robert                                                      |       0 |           0 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 23 | Molecular epidemiology, evolution and phylogeny of SARS coronavirus                                                                                                                                                                              |       1 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 24 | High-Throughput Screening and Identification of Potent Broad-Spectrum Inhibitors of Coronaviruses                                                                                                                                                |       0 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 25 | Enhanced Ability of Oligomeric Nanobodies Targeting MERS Coronavirus Receptor-Binding Domain                                                                                                                                                     |       0 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n",
      "| 26 | Diversity of Dromedary Camel Coronavirus HKU23 in African Camels Revealed Multiple Recombination Events among Closely Related Betacoronaviruses of the Subgenus Embecovirus                                                                      |       1 |           1 |\n",
      "+----+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------+\n"
     ]
    }
   ],
   "source": [
    "# Print results of classification according to title\n",
    "from tabulate import tabulate\n",
    "\n",
    "lst_row = [[x.title, x.label, x.predicted] for x in data_validation]\n",
    "\n",
    "print(tabulate(lst_row, headers=['Title', 'Label', 'Predicted'], showindex=range(0,len(data_validation)), tablefmt='grid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.83 (+/- 0.14)\n"
     ]
    }
   ],
   "source": [
    "# Print acccuracy from Cross-validation\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.pipeline import Pipeline\n",
    "docs = [t.text for t in data]\n",
    "y = [v.label for v in data]\n",
    "pipeline = Pipeline([\n",
    "    ('tfidf', TfidfVectorizer()),\n",
    "    ('clf', LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr')),\n",
    "])\n",
    "scores = cross_val_score(pipeline, docs, y, cv=5)\n",
    "print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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