Notebook_3_metadata_overview.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metadata overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# magics and warnings\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import warnings; warnings.simplefilter('ignore')\n",
    "\n",
    "import os, random, codecs, json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "seed = 99\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "\n",
    "import nltk, sklearn\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\")\n",
    "sns.set_context(\"notebook\", font_scale=1.2, rc={\"lines.linewidth\": 2.5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load metadata\n",
    "\n",
    "df_meta = pd.read_csv(\"datasets_output/df_pub.csv\",compression=\"gzip\")\n",
    "df_cord = pd.read_csv(\"datasets_input/CORD19_01_07_2020/metadata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>pub_id</th>\n",
       "      <th>title</th>\n",
       "      <th>abstract</th>\n",
       "      <th>publication_year</th>\n",
       "      <th>publication_month</th>\n",
       "      <th>journal</th>\n",
       "      <th>volume</th>\n",
       "      <th>issue</th>\n",
       "      <th>pages</th>\n",
       "      <th>doi</th>\n",
       "      <th>pmid</th>\n",
       "      <th>pmcid</th>\n",
       "      <th>dimensions_id</th>\n",
       "      <th>arxiv_id</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Coronavirus disease 2019 (COVID-19) and simult...</td>\n",
       "      <td>Coronavirus disease 2019 (COVID-19) is a recen...</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>CardioVascular Journal of Africa</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1-4</td>\n",
       "      <td>10-5830-cvja-2020-016</td>\n",
       "      <td>32490506.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1128171889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-07-02 10:54:27.347501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>[Ten hot issues of breast cancer under the nov...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Chinese medical journal</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>e002</td>\n",
       "      <td>10.0376/cma.j.issn.0376-2491.2020.0002</td>\n",
       "      <td>32036640.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1124777091</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-07-02 10:54:27.347501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Cohesion in Distancing.</td>\n",
       "      <td>In isolation, we are physically apart; in soli...</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>The AMA Journal of Ethic</td>\n",
       "      <td>22</td>\n",
       "      <td>4</td>\n",
       "      <td>e344-345</td>\n",
       "      <td>10.1001/amajethics.2020.344</td>\n",
       "      <td>32345430.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1127182341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-07-02 10:54:27.347501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Salvation in a Time of Plague.</td>\n",
       "      <td>Health workers offer their skills and care to ...</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>The AMA Journal of Ethic</td>\n",
       "      <td>22</td>\n",
       "      <td>5</td>\n",
       "      <td>e441-445</td>\n",
       "      <td>10.1001/amajethics.2020.441</td>\n",
       "      <td>32449663.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>pub.1127905371</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-07-02 10:54:27.347501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>What Should Health Science Journalists Do in E...</td>\n",
       "      <td>Journalists have long covered outbreaks of inf...</td>\n",
       "      <td>2020.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>AMA journal of ethics</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.1001/amajethics.2020.55</td>\n",
       "      <td>31958392.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-07-02 10:54:27.347501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pub_id                                              title  \\\n",
       "0       0  Coronavirus disease 2019 (COVID-19) and simult...   \n",
       "1       1  [Ten hot issues of breast cancer under the nov...   \n",
       "2       2                            Cohesion in Distancing.   \n",
       "3       3                     Salvation in a Time of Plague.   \n",
       "4       4  What Should Health Science Journalists Do in E...   \n",
       "\n",
       "                                            abstract  publication_year  \\\n",
       "0  Coronavirus disease 2019 (COVID-19) is a recen...            2020.0   \n",
       "1                                                NaN            2020.0   \n",
       "2  In isolation, we are physically apart; in soli...            2020.0   \n",
       "3  Health workers offer their skills and care to ...            2020.0   \n",
       "4  Journalists have long covered outbreaks of inf...            2020.0   \n",
       "\n",
       "   publication_month                           journal volume issue     pages  \\\n",
       "0                5.0  CardioVascular Journal of Africa     31   NaN       1-4   \n",
       "1                2.0           Chinese medical journal    100     0      e002   \n",
       "2                4.0          The AMA Journal of Ethic     22     4  e344-345   \n",
       "3                5.0          The AMA Journal of Ethic     22     5  e441-445   \n",
       "4                1.0             AMA journal of ethics    NaN   NaN       NaN   \n",
       "\n",
       "                                      doi        pmid pmcid   dimensions_id  \\\n",
       "0                   10-5830-cvja-2020-016  32490506.0   NaN  pub.1128171889   \n",
       "1  10.0376/cma.j.issn.0376-2491.2020.0002  32036640.0   NaN  pub.1124777091   \n",
       "2             10.1001/amajethics.2020.344  32345430.0   NaN  pub.1127182341   \n",
       "3             10.1001/amajethics.2020.441  32449663.0   NaN  pub.1127905371   \n",
       "4              10.1001/amajethics.2020.55  31958392.0   NaN             NaN   \n",
       "\n",
       "  arxiv_id                   timestamp  \n",
       "0      NaN  2020-07-02 10:54:27.347501  \n",
       "1      NaN  2020-07-02 10:54:27.347501  \n",
       "2      NaN  2020-07-02 10:54:27.347501  \n",
       "3      NaN  2020-07-02 10:54:27.347501  \n",
       "4      NaN  2020-07-02 10:54:27.347501  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pub_id', 'title', 'abstract', 'publication_year', 'publication_month',\n",
       "       'journal', 'volume', 'issue', 'pages', 'doi', 'pmid', 'pmcid',\n",
       "       'dimensions_id', 'arxiv_id', 'timestamp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(121278, 15)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta[pd.notnull(df_meta.abstract)].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Publication years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def clean_year(s):\n",
    "    if pd.isna(s):\n",
    "        return np.nan\n",
    "    if not (s>1900):\n",
    "        return np.nan\n",
    "    elif s>2020:\n",
    "        return 2020\n",
    "    return s\n",
    "\n",
    "df_meta[\"publication_year\"] = df_meta[\"publication_year\"].apply(clean_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    160644.000000\n",
       "mean       2015.180822\n",
       "std           7.054152\n",
       "min        1901.000000\n",
       "25%        2012.000000\n",
       "50%        2018.000000\n",
       "75%        2020.000000\n",
       "max        2020.000000\n",
       "Name: publication_year, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.publication_year.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a44576f90>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_meta.publication_year.tolist(), bins=60, kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a39b58390>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_meta[(pd.notnull(df_meta.publication_year)) & (df_meta.publication_year > 2000)].publication_year.tolist(), bins=20, hist=True, kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_meta[\"abstract_length\"] = df_meta.abstract.str.len()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(121278, 16)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta[df_meta.abstract_length>0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(160656, 16)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "121278"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(pd.notnull(df_meta.abstract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "147256"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(pd.notnull(df_meta.doi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "79234"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(pd.notnull(df_meta.pmcid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "112913"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(pd.notnull(df_meta.pmid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "156325"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(pd.notnull(df_meta.journal))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Journals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "medRxiv                                                                            4387\n",
       "SSRN Electronic Journal                                                            2925\n",
       "Research Square                                                                    1782\n",
       "bioRxiv                                                                            1717\n",
       "PLoS One                                                                           1669\n",
       "Journal of virology                                                                1607\n",
       "arXiv                                                                              1345\n",
       "Virology                                                                            935\n",
       "Surgical endoscopy                                                                  918\n",
       "Nature                                                                              792\n",
       "Emerg Infect Dis                                                                    772\n",
       "Science                                                                             744\n",
       "The BMJ                                                                             726\n",
       "Viruses                                                                             687\n",
       "Sci Rep                                                                             660\n",
       "Arch Virol                                                                          648\n",
       "ChemRxiv                                                                            623\n",
       "The Journal of general virology                                                     589\n",
       "Vaccine                                                                             570\n",
       "The Lancet                                                                          562\n",
       "JMIR Preprints                                                                      540\n",
       "JAMA                                                                                483\n",
       "Lancet                                                                              445\n",
       "Journal of neurointerventional surgery                                              418\n",
       "Journal of Medical Virology                                                         417\n",
       "Virol J                                                                             403\n",
       "PLoS Pathog                                                                         392\n",
       "BMC Infect Dis                                                                      391\n",
       "Journal of Virological Methods                                                      386\n",
       "Proceedings of the National Academy of Sciences of the United States of America     367\n",
       "Name: journal, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_meta.journal.value_counts()[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sub = df_meta[df_meta.journal.isin(df_meta.journal.value_counts()[:20].index.tolist())]\n",
    "b = sns.countplot(y=\"journal\", data=df_sub, order=df_sub['journal'].value_counts().index)\n",
    "#b.axes.set_title(\"Title\",fontsize=50)\n",
    "b.set_xlabel(\"Count\",fontsize=15)\n",
    "b.set_ylabel(\"Journal\",fontsize=15)\n",
    "b.tick_params(labelsize=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sources and licenses\n",
    "\n",
    "For CORD19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>authors</th>\n",
       "      <th>journal</th>\n",
       "      <th>mag_id</th>\n",
       "      <th>who_covidence_id</th>\n",
       "      <th>arxiv_id</th>\n",
       "      <th>pdf_json_files</th>\n",
       "      <th>pmc_json_files</th>\n",
       "      <th>url</th>\n",
       "      <th>s2_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ug7v899j</td>\n",
       "      <td>d1aafb70c066a2068b02786f8929fd9c900897fb</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Clinical features of culture-proven Mycoplasma...</td>\n",
       "      <td>10.1186/1471-2334-1-6</td>\n",
       "      <td>PMC35282</td>\n",
       "      <td>1.14726e+07</td>\n",
       "      <td>no-cc</td>\n",
       "      <td>OBJECTIVE: This retrospective chart review des...</td>\n",
       "      <td>2001-07-04</td>\n",
       "      <td>Madani, Tariq A; Al-Ghamdi, Aisha A</td>\n",
       "      <td>BMC Infect Dis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>document_parses/pdf_json/d1aafb70c066a2068b027...</td>\n",
       "      <td>document_parses/pmc_json/PMC35282.xml.json</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02tnwd4m</td>\n",
       "      <td>6b0567729c2143a66d737eb0a2f63f2dce2e5a7d</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Nitric oxide: a pro-inflammatory mediator in l...</td>\n",
       "      <td>10.1186/rr14</td>\n",
       "      <td>PMC59543</td>\n",
       "      <td>1.1668e+07</td>\n",
       "      <td>no-cc</td>\n",
       "      <td>Inflammatory diseases of the respiratory tract...</td>\n",
       "      <td>2000-08-15</td>\n",
       "      <td>Vliet, Albert van der; Eiserich, Jason P; Cros...</td>\n",
       "      <td>Respir Res</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>document_parses/pdf_json/6b0567729c2143a66d737...</td>\n",
       "      <td>document_parses/pmc_json/PMC59543.xml.json</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ejv2xln0</td>\n",
       "      <td>06ced00a5fc04215949aa72528f2eeaae1d58927</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Surfactant protein-D and pulmonary host defense</td>\n",
       "      <td>10.1186/rr19</td>\n",
       "      <td>PMC59549</td>\n",
       "      <td>1.1668e+07</td>\n",
       "      <td>no-cc</td>\n",
       "      <td>Surfactant protein-D (SP-D) participates in th...</td>\n",
       "      <td>2000-08-25</td>\n",
       "      <td>Crouch, Erika C</td>\n",
       "      <td>Respir Res</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>document_parses/pdf_json/06ced00a5fc04215949aa...</td>\n",
       "      <td>document_parses/pmc_json/PMC59549.xml.json</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2b73a28n</td>\n",
       "      <td>348055649b6b8cf2b9a376498df9bf41f7123605</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Role of endothelin-1 in lung disease</td>\n",
       "      <td>10.1186/rr44</td>\n",
       "      <td>PMC59574</td>\n",
       "      <td>1.16869e+07</td>\n",
       "      <td>no-cc</td>\n",
       "      <td>Endothelin-1 (ET-1) is a 21 amino acid peptide...</td>\n",
       "      <td>2001-02-22</td>\n",
       "      <td>Fagan, Karen A; McMurtry, Ivan F; Rodman, David M</td>\n",
       "      <td>Respir Res</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>document_parses/pdf_json/348055649b6b8cf2b9a37...</td>\n",
       "      <td>document_parses/pmc_json/PMC59574.xml.json</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9785vg6d</td>\n",
       "      <td>5f48792a5fa08bed9f56016f4981ae2ca6031b32</td>\n",
       "      <td>PMC</td>\n",
       "      <td>Gene expression in epithelial cells in respons...</td>\n",
       "      <td>10.1186/rr61</td>\n",
       "      <td>PMC59580</td>\n",
       "      <td>1.16869e+07</td>\n",
       "      <td>no-cc</td>\n",
       "      <td>Respiratory syncytial virus (RSV) and pneumoni...</td>\n",
       "      <td>2001-05-11</td>\n",
       "      <td>Domachowske, Joseph B; Bonville, Cynthia A; Ro...</td>\n",
       "      <td>Respir Res</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>document_parses/pdf_json/5f48792a5fa08bed9f560...</td>\n",
       "      <td>document_parses/pmc_json/PMC59580.xml.json</td>\n",
       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cord_uid                                       sha source_x  \\\n",
       "0  ug7v899j  d1aafb70c066a2068b02786f8929fd9c900897fb      PMC   \n",
       "1  02tnwd4m  6b0567729c2143a66d737eb0a2f63f2dce2e5a7d      PMC   \n",
       "2  ejv2xln0  06ced00a5fc04215949aa72528f2eeaae1d58927      PMC   \n",
       "3  2b73a28n  348055649b6b8cf2b9a376498df9bf41f7123605      PMC   \n",
       "4  9785vg6d  5f48792a5fa08bed9f56016f4981ae2ca6031b32      PMC   \n",
       "\n",
       "                                               title                    doi  \\\n",
       "0  Clinical features of culture-proven Mycoplasma...  10.1186/1471-2334-1-6   \n",
       "1  Nitric oxide: a pro-inflammatory mediator in l...           10.1186/rr14   \n",
       "2    Surfactant protein-D and pulmonary host defense           10.1186/rr19   \n",
       "3               Role of endothelin-1 in lung disease           10.1186/rr44   \n",
       "4  Gene expression in epithelial cells in respons...           10.1186/rr61   \n",
       "\n",
       "      pmcid    pubmed_id license  \\\n",
       "0  PMC35282  1.14726e+07   no-cc   \n",
       "1  PMC59543   1.1668e+07   no-cc   \n",
       "2  PMC59549   1.1668e+07   no-cc   \n",
       "3  PMC59574  1.16869e+07   no-cc   \n",
       "4  PMC59580  1.16869e+07   no-cc   \n",
       "\n",
       "                                            abstract publish_time  \\\n",
       "0  OBJECTIVE: This retrospective chart review des...   2001-07-04   \n",
       "1  Inflammatory diseases of the respiratory tract...   2000-08-15   \n",
       "2  Surfactant protein-D (SP-D) participates in th...   2000-08-25   \n",
       "3  Endothelin-1 (ET-1) is a 21 amino acid peptide...   2001-02-22   \n",
       "4  Respiratory syncytial virus (RSV) and pneumoni...   2001-05-11   \n",
       "\n",
       "                                             authors         journal  mag_id  \\\n",
       "0                Madani, Tariq A; Al-Ghamdi, Aisha A  BMC Infect Dis     NaN   \n",
       "1  Vliet, Albert van der; Eiserich, Jason P; Cros...      Respir Res     NaN   \n",
       "2                                    Crouch, Erika C      Respir Res     NaN   \n",
       "3  Fagan, Karen A; McMurtry, Ivan F; Rodman, David M      Respir Res     NaN   \n",
       "4  Domachowske, Joseph B; Bonville, Cynthia A; Ro...      Respir Res     NaN   \n",
       "\n",
       "  who_covidence_id arxiv_id  \\\n",
       "0              NaN      NaN   \n",
       "1              NaN      NaN   \n",
       "2              NaN      NaN   \n",
       "3              NaN      NaN   \n",
       "4              NaN      NaN   \n",
       "\n",
       "                                      pdf_json_files  \\\n",
       "0  document_parses/pdf_json/d1aafb70c066a2068b027...   \n",
       "1  document_parses/pdf_json/6b0567729c2143a66d737...   \n",
       "2  document_parses/pdf_json/06ced00a5fc04215949aa...   \n",
       "3  document_parses/pdf_json/348055649b6b8cf2b9a37...   \n",
       "4  document_parses/pdf_json/5f48792a5fa08bed9f560...   \n",
       "\n",
       "                               pmc_json_files  \\\n",
       "0  document_parses/pmc_json/PMC35282.xml.json   \n",
       "1  document_parses/pmc_json/PMC59543.xml.json   \n",
       "2  document_parses/pmc_json/PMC59549.xml.json   \n",
       "3  document_parses/pmc_json/PMC59574.xml.json   \n",
       "4  document_parses/pmc_json/PMC59580.xml.json   \n",
       "\n",
       "                                                 url  s2_id  \n",
       "0  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3...    NaN  \n",
       "1  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...    NaN  \n",
       "2  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...    NaN  \n",
       "3  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...    NaN  \n",
       "4  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...    NaN  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cord.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# source\n",
    "df_sub = df_cord[df_cord.source_x.isin(df_cord.source_x.value_counts()[:30].index.tolist())]\n",
    "b = sns.countplot(y=\"source_x\", data=df_sub, order=df_sub['source_x'].value_counts().index)\n",
    "#b.axes.set_title(\"Title\",fontsize=50)\n",
    "b.set_xlabel(\"Count\",fontsize=15)\n",
    "b.set_ylabel(\"Source\",fontsize=15)\n",
    "b.tick_params(labelsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# license\n",
    "df_sub = df_cord[df_cord.license.isin(df_cord.license.value_counts()[:30].index.tolist())]\n",
    "b = sns.countplot(y=\"license\", data=df_sub, order=df_sub['license'].value_counts().index)\n",
    "#b.axes.set_title(\"Title\",fontsize=50)\n",
    "b.set_xlabel(\"Count\",fontsize=15)\n",
    "b.set_ylabel(\"License\",fontsize=15)\n",
    "b.tick_params(labelsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Full text availability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cord[\"has_full_text\"] = pd.notnull(df_cord.pmc_json_files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a3574f490>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# full text x source\n",
    "df_plot = df_cord.groupby(['has_full_text', 'source_x']).size().reset_index().pivot(columns='has_full_text', index='source_x', values=0)\n",
    "df_plot.plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a38396d90>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# full text x journal\n",
    "df_sub = df_cord[df_cord.journal.isin(df_cord.journal.value_counts()[:30].index.tolist())]\n",
    "df_plot = df_sub.groupby(['has_full_text', 'journal']).size().reset_index().pivot(columns='has_full_text', index='journal', values=0)\n",
    "df_plot.plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Overlap of CORD19 with Dimensions and WHO\n",
    "\n",
    "This is still problematically low.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# datasets\n",
    "\n",
    "dimensions_latest = \"datasets_input/Dimensions_01_07_2020.csv\"\n",
    "\n",
    "df_dimensions = pd.read_csv(dimensions_latest, sep=\";\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "dimensions_dois = df_dimensions[pd.notnull(df_dimensions[\"DOI\"])][\"DOI\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "cord_dois = df_cord[pd.notnull(df_cord[\"doi\"])][\"doi\"].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "127553"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cord_dois)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6603490936021948"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(cord_dois).intersection(set(dimensions_dois)))/len(set(dimensions_dois))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Abstracts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'In isolation, we are physically apart; in solidarity, we are together. The COVID-19 pandemic emphasizes our social responsibility to maintain physical distance from one another. In doing so, we solidify our collective strength.'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abstracts = df_meta[pd.notnull(df_meta[\"abstract\"])].abstract.tolist()\n",
    "abstracts[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Topic modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "import gensim, sklearn, spacy\n",
    "import pyLDAvis.gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: en_core_web_sm==2.0.0 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.0.0/en_core_web_sm-2.0.0.tar.gz#egg=en_core_web_sm==2.0.0 in /anaconda3/envs/covid/lib/python3.7/site-packages (2.0.0)\n",
      "\n",
      "\u001b[93m    Linking successful\u001b[0m\n",
      "    /anaconda3/envs/covid/lib/python3.7/site-packages/en_core_web_sm -->\n",
      "    /anaconda3/envs/covid/lib/python3.7/site-packages/spacy/data/en\n",
      "\n",
      "    You can now load the model via spacy.load('en')\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!python -m spacy download en"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "nlp = spacy.load('en')\n",
    "STOPWORDS = spacy.lang.en.stop_words.STOP_WORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<timed exec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/covid/lib/python3.7/site-packages/spacy/language.py\u001b[0m in \u001b[0;36mpipe\u001b[0;34m(self, texts, as_tuples, n_threads, batch_size, disable, cleanup, component_cfg, n_process)\u001b[0m\n\u001b[1;32m    817\u001b[0m         \u001b[0moriginal_strings_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    818\u001b[0m         \u001b[0mnr_seen\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 819\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mdoc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdocs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    820\u001b[0m             \u001b[0;32myield\u001b[0m \u001b[0mdoc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    821\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcleanup\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/covid/lib/python3.7/site-packages/spacy/language.py\u001b[0m in \u001b[0;36m_multiprocessing_pipe\u001b[0;34m(self, texts, pipes, n_process, batch_size)\u001b[0m\n\u001b[1;32m    870\u001b[0m         \u001b[0mdocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mDoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbyte_doc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbyte_doc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbyte_docs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    871\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 872\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_texts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    873\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mdoc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    874\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/covid/lib/python3.7/site-packages/spacy/language.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    868\u001b[0m         \u001b[0;31m# The received object is a batch of byte-encoded docs, so flatten them with chain.from_iterable.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    869\u001b[0m         \u001b[0mbyte_docs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_iterable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrecv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcycle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbytedocs_recv_ch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 870\u001b[0;31m         \u001b[0mdocs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mDoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_bytes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbyte_doc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mbyte_doc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbyte_docs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    871\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    872\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdoc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_texts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdocs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mdoc.pyx\u001b[0m in \u001b[0;36mspacy.tokens.doc.Doc.from_bytes\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mdoc.pyx\u001b[0m in \u001b[0;36mspacy.tokens.doc.Doc.from_array\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mmorphology.pyx\u001b[0m in \u001b[0;36mspacy.morphology.Morphology.assign_tag\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mmorphology.pyx\u001b[0m in \u001b[0;36mspacy.morphology.Morphology.assign_tag_id\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mmorphology.pyx\u001b[0m in \u001b[0;36mspacy.morphology.Morphology.lemmatize\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/anaconda3/envs/covid/lib/python3.7/site-packages/spacy/lemmatizer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, string, univ_pos, morphology)\u001b[0m\n\u001b[1;32m     32\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlookups\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlookups\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 34\u001b[0;31m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0muniv_pos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmorphology\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     35\u001b[0m         \"\"\"Lemmatize a string.\n\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "processed_docs = list()\n",
    "for doc in nlp.pipe(abstracts, n_process=5, batch_size=10):\n",
    "\n",
    "    # Process document using Spacy NLP pipeline.\n",
    "    #ents = doc.ents  # Named entities\n",
    "\n",
    "    # Keep only words (no numbers, no punctuation).\n",
    "    # Lemmatize tokens, remove punctuation and remove stopwords.\n",
    "    doc = [token.lemma_ for token in doc if token.is_alpha and not token.is_stop]\n",
    "\n",
    "    # Remove common words from a stopword list and keep only words of length 3 or more.\n",
    "    doc = [token for token in doc if token not in STOPWORDS and len(token) > 2]\n",
    "\n",
    "    # Add named entities, but only if they are a compound of more than one word.\n",
    "    #doc.extend([str(entity) for entity in ents if len(entity) > 1])\n",
    "                        \n",
    "    processed_docs.append(doc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "pickle.dump(processed_docs, open(\"datasets_output/processed_docs.pk\", \"wb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "processed_docs = pickle.load(open(\"datasets_output/processed_docs.pk\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = processed_docs\n",
    "del processed_docs\n",
    "\n",
    "# Add bigrams\n",
    "from gensim.models.phrases import Phrases\n",
    "# Add bigrams to docs (only ones that appear several times or more). A better approach would be to use a chi_sq test.\n",
    "bigram = Phrases(docs, min_count=50)\n",
    "for idx in range(len(docs)):\n",
    "    for token in bigram[docs[idx]]:\n",
    "        if '_' in token:\n",
    "            # Token is a bigram, add to document.\n",
    "            docs[idx].append(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove rare and common tokens.\n",
    "# Filter out words that occur too frequently or too rarely.\n",
    "max_freq = 0.5\n",
    "min_wordcount = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique tokens: 14389\n",
      "Number of docs: 39154 (39154)\n"
     ]
    }
   ],
   "source": [
    "# Create a dictionary representation of the documents, and filter out frequent and rare words.\n",
    "from gensim.corpora import Dictionary\n",
    "dictionary = Dictionary(docs)\n",
    "dictionary.filter_extremes(no_below=min_wordcount, no_above=max_freq)\n",
    "\n",
    "# Bag-of-words representation of the documents.\n",
    "corpus = [dictionary.doc2bow(doc) for doc in docs]\n",
    "#MmCorpus.serialize(\"models/corpus.mm\", corpus)\n",
    "\n",
    "print('Number of unique tokens: %d' % len(dictionary))\n",
    "print('Number of docs: %d (%d)' % (len(corpus),len(abstracts)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 24s, sys: 796 ms, total: 1min 24s\n",
      "Wall time: 1min 23s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "from gensim.models import LdaMulticore, LdaModel\n",
    "params = {'num_topics': 15,'passes': 3, 'random_state': seed}\n",
    "model = LdaModel(corpus=corpus, num_topics=params['num_topics'], id2word=dictionary, #workers=6,\n",
    "                passes=params['passes'], random_state=params['random_state'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.049*\"respiratory\" + 0.047*\"virus\" + 0.041*\"infection\" + 0.023*\"viral\" + 0.023*\"child\"'),\n",
       " (1,\n",
       "  '0.048*\"rna\" + 0.028*\"sequence\" + 0.024*\"virus\" + 0.024*\"gene\" + 0.015*\"genome\"'),\n",
       " (2,\n",
       "  '0.079*\"influenza\" + 0.023*\"influenza_virus\" + 0.022*\"case\" + 0.018*\"pandemic\" + 0.012*\"virus\"'),\n",
       " (3,\n",
       "  '0.025*\"group\" + 0.023*\"diarrhea\" + 0.022*\"pig\" + 0.021*\"day\" + 0.019*\"strain\"'),\n",
       " (4,\n",
       "  '0.016*\"rat\" + 0.013*\"increase\" + 0.012*\"group\" + 0.011*\"study\" + 0.011*\"effect\"'),\n",
       " (5,\n",
       "  '0.038*\"protein\" + 0.024*\"virus\" + 0.021*\"cell\" + 0.015*\"activity\" + 0.014*\"viral\"'),\n",
       " (6,\n",
       "  '0.056*\"cell\" + 0.025*\"infection\" + 0.022*\"mouse\" + 0.019*\"virus\" + 0.018*\"response\"'),\n",
       " (7,\n",
       "  '0.026*\"calf\" + 0.025*\"assay\" + 0.024*\"sample\" + 0.021*\"test\" + 0.021*\"pcr\"'),\n",
       " (8,\n",
       "  '0.030*\"sars\" + 0.030*\"cov\" + 0.029*\"antibody\" + 0.027*\"coronavirus\" + 0.024*\"protein\"'),\n",
       " (9,\n",
       "  '0.034*\"virus\" + 0.022*\"disease\" + 0.022*\"human\" + 0.017*\"animal\" + 0.013*\"specie\"'),\n",
       " (10,\n",
       "  '0.020*\"disease\" + 0.015*\"review\" + 0.014*\"vaccine\" + 0.012*\"development\" + 0.011*\"cat\"'),\n",
       " (11,\n",
       "  '0.015*\"infection\" + 0.013*\"control\" + 0.013*\"outbreak\" + 0.013*\"transmission\" + 0.011*\"sars\"'),\n",
       " (12,\n",
       "  '0.042*\"health\" + 0.018*\"public\" + 0.013*\"public_health\" + 0.010*\"care\" + 0.009*\"disease\"'),\n",
       " (13,\n",
       "  '0.032*\"model\" + 0.015*\"method\" + 0.015*\"-PRON-\" + 0.014*\"datum\" + 0.012*\"study\"'),\n",
       " (14,\n",
       "  '0.068*\"patient\" + 0.015*\"clinical\" + 0.014*\"case\" + 0.012*\"hospital\" + 0.011*\"study\"')]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.show_topics(num_words=5, num_topics=params['num_topics'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
       "\n",
       "\n",
       "<div id=\"ldavis_el266001126592610082000754445\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el266001126592610082000754445_data = {\"mdsDat\": {\"x\": [0.17480745294457353, 0.25890535918842567, -0.007638262179896828, 0.21514933068003556, 0.20436990404189695, 0.007382148844037598, -0.1257829070516989, -0.21082207491229074, 0.0331296060851704, -0.08107381946468352, -0.1521174966076142, -0.09326239136060555, 0.029935445600431227, -0.1757823361407252, -0.07719995966705663], \"y\": [0.057817194718886904, -0.026674449282178866, -0.18713361038957688, 0.013949931574608793, 0.09132483302356388, -0.043727706915576654, -0.2396893937715531, 0.0914378092087572, 0.03767209609844211, 0.08730533668482866, -0.08959500426393155, 0.18426277496102048, -0.20179681155716814, 0.04117758528288572, 0.18366941462699157], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [12.428565979003906, 10.989014625549316, 9.008088111877441, 7.6992058753967285, 6.742216110229492, 6.573009490966797, 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\"temperature\", \"temperature\", \"temperature\", \"temperature\", \"temperature\", \"temperature\", \"temperature\", \"temperature\", \"template\", \"template\", \"template\", \"template\", \"terminal\", \"terminal\", \"terminal\", \"terminal\", \"terminal\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"tgev\", \"tgev\", \"tgev\", \"theme\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapeutic\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"therapy\", \"thin\", \"thoracic\", \"tick\", \"tick\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"tissue\", \"titer\", \"titer\", \"titer\", \"titer\", \"titer\", \"titer\", \"tnf\", \"topological\", \"tourism\", \"tourist\", \"tract\", \"tract\", \"tract\", \"tract\", \"tract\", \"tract\", \"tract\", \"tract_infection\", \"training\", \"training\", \"training\", \"training\", \"training_program\", \"tran\", \"transcribe\", \"transcription\", \"transcription\", \"transcription\", \"transcription\", \"transcription\", \"transcription\", \"transmembrane\", \"transmembrane\", \"transmembrane\", \"transmembrane\", \"transmissible\", \"transmissible\", \"transmissible\", \"transmissible\", \"transmissible\", \"transmissible\", \"transmissible_gastroenteritis\", \"transmissible_gastroenteritis\", \"transmission\", \"transmission\", \"transmission\", \"transmission\", \"transmission\", \"transmission\", \"transmission\", \"transmission\", \"transplantation\", \"transplantation\", \"transplantation\", \"transplantation\", \"transplantation\", \"trauma\", \"trauma\", \"travel\", \"travel\", \"travel\", \"travel\", \"traveller\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treat\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"trna\", \"tumor\", \"tumor\", \"tumor\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"type\", \"ubiquitin\", \"una\", \"united\", \"united\", \"united\", \"united\", \"united\", \"university_hospital\", \"upper\", \"upper\", \"upper\", \"upper\", \"upper\", \"upper_respiratory\", \"upper_respiratory\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"use\", \"vaccinated\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccination\", \"vaccine\", \"vaccine\", \"vaccine\", \"vaccine\", \"vaccine\", \"vaccine\", \"vaccine\", \"vaccine\", \"ventilator\", \"vesicle\", \"vesicular\", \"vesicular_stomatitis\", \"villous\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"viral\", \"virion\", \"virion\", \"virion\", \"virome\", \"virtual\", \"virtual\", \"virulent\", \"virulent\", \"virulent\", \"virulent\", \"virulent\", \"virulent\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"virus\", \"visitor\", \"vitamin\", \"vlp\", \"vlp\", \"vsv\", \"wean\", \"wean\", \"wear\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"weight\", \"weight\", \"weight\", \"weight\", \"weight\", \"weight\", \"weight\", \"weight\", \"weight\", \"weight_gain\", \"west_nile\", \"west_nile\", \"west_nile\", \"wheeze\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"wildlife\", \"wind\", \"wind\", \"wnv\", \"wnv\", \"worker\", \"worker\", \"worker\", \"worker\", \"workforce\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"zoonosis\", \"zoonosis\", \"zoonotic\", \"zoonotic\", \"zoonotic\", \"zoonotic\", \"zoonotic\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [7, 6, 11, 2, 9, 10, 13, 15, 8, 5, 12, 1, 14, 3, 4]};\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_el266001126592610082000754445\", ldavis_el266001126592610082000754445_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_el266001126592610082000754445\", ldavis_el266001126592610082000754445_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_el266001126592610082000754445\", ldavis_el266001126592610082000754445_data);\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# plot topics (NOTE: the IDs here do not match those from the model)\n",
    "data =  pyLDAvis.gensim.prepare(model, corpus, dictionary)\n",
    "pyLDAvis.display(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# topics over time\n",
    "# the topics of ALL the documents of our corpus\n",
    "\n",
    "df_local = df_meta[pd.notnull(df_meta[\"abstract\"])]\n",
    "publication_years = df_local.publication_year.tolist()\n",
    "dois = df_local.doi.tolist()\n",
    "topics = np.zeros((len(docs),params['num_topics']))\n",
    "\n",
    "for n,doc_topics in enumerate(model.get_document_topics(corpus)):\n",
    "    for t in doc_topics:\n",
    "        topics[n][t[0]] = t[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.03333401, 0.03333402, 0.03333398, 0.03333421, 0.03333408,\n",
       "       0.03333404, 0.03333399, 0.03333408, 0.03333404, 0.03333401,\n",
       "       0.53332347, 0.03333399, 0.03333404, 0.03333413, 0.03333398])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topics[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 0.033333972),\n",
       " (1, 0.033333976),\n",
       " (2, 0.033333942),\n",
       " (3, 0.033334054),\n",
       " (4, 0.03333404),\n",
       " (5, 0.033333994),\n",
       " (6, 0.03333395),\n",
       " (7, 0.033334035),\n",
       " (8, 0.033333994),\n",
       " (9, 0.033333965),\n",
       " (10, 0.033334073),\n",
       " (11, 0.033333957),\n",
       " (12, 0.033334),\n",
       " (13, 0.53332406),\n",
       " (14, 0.03333394)]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_document_topics(corpus[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topics = pd.DataFrame(topics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_topics[\"year\"] = publication_years\n",
    "df_topics[\"doi\"] = dois"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "from_which_year = 2000\n",
    "\n",
    "grouped = df_topics.groupby('year')\n",
    "df_grouped = grouped.aggregate(np.mean)\n",
    "df_grouped = df_grouped[df_grouped.index >= from_which_year]\n",
    "#df_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(16, 12))\n",
    "plt.pcolor(df_grouped.to_numpy(), norm=None, cmap='Blues')\n",
    "plt.yticks(np.arange(df_grouped.to_numpy().shape[0]), df_grouped.index.values)\n",
    "plt.xticks(np.arange(df_grouped.to_numpy().shape[1])+0.5, [\"Topic #\"+str(n) for n in range(model.num_topics)], rotation = 90)\n",
    "plt.colorbar(cmap='Blues')  # plot colorbar\n",
    "plt.tight_layout()  # fixes margins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Language modelling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import Word2Vec\n",
    "import pickle\n",
    "\n",
    "processed_docs = pickle.load(open(\"datasets_output/processed_docs.pk\", \"rb\"))\n",
    "\n",
    "model = Word2Vec(processed_docs, size=256, window=10, min_count=2, workers=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('covs', 0.6251739263534546),\n",
       " ('cov', 0.5501048564910889),\n",
       " ('betacoronavirus', 0.5166511535644531),\n",
       " ('hcov', 0.48612651228904724),\n",
       " ('hcovs', 0.4802132546901703),\n",
       " ('coronaviruses', 0.48010629415512085),\n",
       " ('astrovirus', 0.4582931399345398),\n",
       " ('virus', 0.4369690418243408),\n",
       " ('scovs', 0.4323999881744385),\n",
       " ('alphacoronavirus', 0.4261910915374756)]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.wv.most_similar(\"coronavirus\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Keyword extraction\n",
    "\n",
    "Using [RAKE](https://csurfer.github.io/rake-nltk/_build/html/index.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rake_nltk import Rake\n",
    "\n",
    "# Uses stopwords for English from NLTK, and all puntuation characters by default\n",
    "r = Rake(min_length=2)\n",
    "\n",
    "# Extraction given the text\n",
    "r.extract_keywords_from_text(abstracts[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['serological andpathogenic microorganism test results',\n",
       " 'provide reasonable suggestions',\n",
       " '100 countries around',\n",
       " 'interstitial lung disease',\n",
       " 'disease onset characteristics',\n",
       " 'onset ild',\n",
       " 'lung injury',\n",
       " 'image characteristics',\n",
       " 'spread rapidly',\n",
       " 'repair caused']"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get keyword phrases ranked highest to lowest.\n",
    "r.get_ranked_phrases()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(25.0, 'serological andpathogenic microorganism test results'),\n",
       " (9.0, 'provide reasonable suggestions'),\n",
       " (9.0, '100 countries around'),\n",
       " (8.5, 'interstitial lung disease'),\n",
       " (8.0, 'disease onset characteristics'),\n",
       " (4.5, 'onset ild'),\n",
       " (4.5, 'lung injury'),\n",
       " (4.5, 'image characteristics'),\n",
       " (4.0, 'spread rapidly'),\n",
       " (4.0, 'repair caused')]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get keyword phrases ranked highest to lowest with scores.\n",
    "r.get_ranked_phrases_with_scores()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39154"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(processed_docs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = Rake(min_length=2, max_length=3)\n",
    "# Extraction given the list of strings where each string is a sentence.\n",
    "r.extract_keywords_from_sentences([\" \".join(d) for d in processed_docs])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(9.0, 'welcome adam vaughan'),\n",
       " (9.0, 'wash hand thoroughly'),\n",
       " (9.0, 'understand pandemic datum'),\n",
       " (9.0, 'trypsin cod trypsin'),\n",
       " (9.0, 'trichothecene mycotoxin deoxynivalenol'),\n",
       " (9.0, 'tolerance self rna'),\n",
       " (9.0, 'summarize presentation discussion'),\n",
       " (9.0, 'suffer cold addition'),\n",
       " (9.0, 'substitute mask alternative'),\n",
       " (9.0, 'strategy widely applicable'),\n",
       " (9.0, 'start codon suggest'),\n",
       " (9.0, 'speak italy day'),\n",
       " (9.0, 'set richard smyth'),\n",
       " (9.0, 'sensible way plan'),\n",
       " (9.0, 'scary hard stand'),\n",
       " (9.0, 'remain committed neurorehabilitation'),\n",
       " (9.0, 'rely quarantine isolation'),\n",
       " (9.0, 'pneumonia problem worldwide'),\n",
       " (9.0, 'new york dutton'),\n",
       " (9.0, 'need think containment'),\n",
       " (9.0, 'method general interest'),\n",
       " (9.0, 'measure cleavage angiotensin'),\n",
       " (9.0, 'knowledge sars high'),\n",
       " (9.0, 'join faculty worker'),\n",
       " (9.0, 'influence hbv transcription'),\n",
       " (9.0, 'individual different value'),\n",
       " (9.0, 'illustrate application pandemic'),\n",
       " (9.0, 'haq posthoc conclusion'),\n",
       " (9.0, 'febrile tachypneic hypoxic'),\n",
       " (9.0, 'explore hypothesis conserved'),\n",
       " (9.0, 'expect effort different'),\n",
       " (9.0, 'expand initial cohort'),\n",
       " (9.0, 'envoy ming emperor'),\n",
       " (9.0, 'endoplasmic reticulum glucosidas'),\n",
       " (9.0, 'discuss implication finding'),\n",
       " (9.0, 'describe pathology esophagus'),\n",
       " (9.0, 'describe management case'),\n",
       " (9.0, 'declare compete interest'),\n",
       " (9.0, 'decision retract correspondence'),\n",
       " (9.0, 'convert enzyme angiotensin'),\n",
       " (9.0, 'conflict interest reference'),\n",
       " (9.0, 'conclude derive vpl'),\n",
       " (9.0, 'complacent contingency planning'),\n",
       " (9.0, 'competitive inhibition value'),\n",
       " (9.0, 'circulate widely twitter'),\n",
       " (9.0, 'cadeia polimerase multiplex'),\n",
       " (9.0, 'bind viral replicase'),\n",
       " (9.0, 'approach problem straightforwardly'),\n",
       " (9.0, 'antiinflammatory agent steroid'),\n",
       " (9.0, 'analyze publication record')]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get keyword phrases ranked highest to lowest with scores.\n",
    "r.get_ranked_phrases_with_scores()[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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