SANA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e153753",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initial imports\n",
    "import sqlite3 as sql\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "002ed92d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Setup\n",
    "database = \"C:\\\\Users\\\\Nabiha Naqvie\\\\Documents\\\\SANA Project\\\\SANA.db\"\n",
    "connection = sql.connect(database)\n",
    "cursor = connection.cursor()\n",
    "cursor.execute(\"SELECT * FROM SANA\")\n",
    "results = cursor.fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3fd5c14f",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>Title of the paper</td>\n",
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       "      <td>Tags/ Sub-category</td>\n",
       "      <td>Language</td>\n",
       "      <td>External link</td>\n",
       "      <td>Book/paper</td>\n",
       "      <td>Abstract</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Methods and Perspectives in Islamic Sciences, ...</td>\n",
       "      <td>Ali Paya</td>\n",
       "      <td>Islamic Philosophy</td>\n",
       "      <td>Islamic Studies/ Methodology and Methods in Is...</td>\n",
       "      <td>English</td>\n",
       "      <td>https://www.islamic-college.ac.uk/about/academ...</td>\n",
       "      <td>Book</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ethics, Epistemology, and Religion: Further Th...</td>\n",
       "      <td>Ali Paya</td>\n",
       "      <td>Islamic Philosophy</td>\n",
       "      <td>Ethics/ Epistemology/Religion/Islam</td>\n",
       "      <td>English</td>\n",
       "      <td>https://www.routledge.com/search?kw=Ethics%2C+...</td>\n",
       "      <td>Book</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Analytic Philosophy: Problems and Perspectives...</td>\n",
       "      <td>Ali Paya</td>\n",
       "      <td>Islamic Philosophy</td>\n",
       "      <td>Analytical Philosophy</td>\n",
       "      <td>Persian</td>\n",
       "      <td>https://www.islamic-college.ac.uk/about/academ...</td>\n",
       "      <td>Book</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>On Religious Knowledge (Dar Bareh Ilm-e Dini),...</td>\n",
       "      <td>Ali Paya</td>\n",
       "      <td>Islamic Philosophy</td>\n",
       "      <td>Islamic Sciences/ Religious Knowledge</td>\n",
       "      <td>Persian</td>\n",
       "      <td>https://www.islamic-college.ac.uk/about/academ...</td>\n",
       "      <td>Book</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>The Origins and Rhetorical Evolution of the Te...</td>\n",
       "      <td>Shahzad Bashir</td>\n",
       "      <td>Anthropology of Shi’a Islam</td>\n",
       "      <td>historiography/Islamic history/qizilbash/Safav...</td>\n",
       "      <td>English</td>\n",
       "      <td>https://brill.com/view/journals/jesh/57/3/arti...</td>\n",
       "      <td>paper</td>\n",
       "      <td>This essay traces the use of the term Qizilb?s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>Bashir, Shahzad Shah Ismaʿil and the Qizilbash...</td>\n",
       "      <td>Shahzad Bashir</td>\n",
       "      <td>Anthropology of Shi’a Islam</td>\n",
       "      <td>historiography/Islamic history/qizilbash/Safav...</td>\n",
       "      <td>English</td>\n",
       "      <td>https://www.journals.uchicago.edu/doi/10.1086/...</td>\n",
       "      <td>paper</td>\n",
       "      <td>The first decade of the sixteeth century of th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>The Iranian Metaphysicals: Explorations in Sci...</td>\n",
       "      <td>Alireza Doostdar</td>\n",
       "      <td>Anthropology of Shi’a Islam</td>\n",
       "      <td>science/spirituality/mysticism/rationality/met...</td>\n",
       "      <td>English</td>\n",
       "      <td>https://www-jstor-org.proxygw.wrlc.org/stable/...</td>\n",
       "      <td>paper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>640</th>\n",
       "      <td>Varieties of Islamic Social Science KNOW: A Jo...</td>\n",
       "      <td>Alireza Doostdar</td>\n",
       "      <td>Anthropology of Shi’a Islam</td>\n",
       "      <td>social science/integration in Islam/Islamizati...</td>\n",
       "      <td>English</td>\n",
       "      <td>https://web-a-ebscohost-com.proxygw.wrlc.org/e...</td>\n",
       "      <td>paper</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>Empirical Spirits: Islam, Spiritism, and the V...</td>\n",
       "      <td>Alireza Doostdar</td>\n",
       "      <td>Anthropology of Shi’a Islam</td>\n",
       "      <td>Spiritism in Iran/science/religion/morality</td>\n",
       "      <td>English</td>\n",
       "      <td>https://www-proquest-com.proxygw.wrlc.org/docv...</td>\n",
       "      <td>paper</td>\n",
       "      <td>This article examines some aspects of the rece...</td>\n",
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       "<p>642 rows × 8 columns</p>\n",
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      "text/plain": [
       "                                                 Title            Author  \\\n",
       "0                                   Title of the paper            Author   \n",
       "1    Methods and Perspectives in Islamic Sciences, ...          Ali Paya   \n",
       "2    Ethics, Epistemology, and Religion: Further Th...          Ali Paya   \n",
       "3    Analytic Philosophy: Problems and Perspectives...          Ali Paya   \n",
       "4    On Religious Knowledge (Dar Bareh Ilm-e Dini),...          Ali Paya   \n",
       "..                                                 ...               ...   \n",
       "637  The Origins and Rhetorical Evolution of the Te...    Shahzad Bashir   \n",
       "638  Bashir, Shahzad Shah Ismaʿil and the Qizilbash...    Shahzad Bashir   \n",
       "639  The Iranian Metaphysicals: Explorations in Sci...  Alireza Doostdar   \n",
       "640  Varieties of Islamic Social Science KNOW: A Jo...  Alireza Doostdar   \n",
       "641  Empirical Spirits: Islam, Spiritism, and the V...  Alireza Doostdar   \n",
       "\n",
       "                        Category  \\\n",
       "0                       Category   \n",
       "1             Islamic Philosophy   \n",
       "2             Islamic Philosophy   \n",
       "3             Islamic Philosophy   \n",
       "4             Islamic Philosophy   \n",
       "..                           ...   \n",
       "637  Anthropology of Shi’a Islam   \n",
       "638  Anthropology of Shi’a Islam   \n",
       "639  Anthropology of Shi’a Islam   \n",
       "640  Anthropology of Shi’a Islam   \n",
       "641  Anthropology of Shi’a Islam   \n",
       "\n",
       "                                          Sub-Category  Language  \\\n",
       "0                                   Tags/ Sub-category  Language   \n",
       "1    Islamic Studies/ Methodology and Methods in Is...   English   \n",
       "2                  Ethics/ Epistemology/Religion/Islam   English   \n",
       "3                                Analytical Philosophy   Persian   \n",
       "4                Islamic Sciences/ Religious Knowledge   Persian   \n",
       "..                                                 ...       ...   \n",
       "637  historiography/Islamic history/qizilbash/Safav...   English   \n",
       "638  historiography/Islamic history/qizilbash/Safav...   English   \n",
       "639  science/spirituality/mysticism/rationality/met...   English   \n",
       "640  social science/integration in Islam/Islamizati...   English   \n",
       "641        Spiritism in Iran/science/religion/morality   English   \n",
       "\n",
       "                                        External Links  Book/Paper  \\\n",
       "0                                        External link  Book/paper   \n",
       "1    https://www.islamic-college.ac.uk/about/academ...        Book   \n",
       "2    https://www.routledge.com/search?kw=Ethics%2C+...        Book   \n",
       "3    https://www.islamic-college.ac.uk/about/academ...        Book   \n",
       "4    https://www.islamic-college.ac.uk/about/academ...        Book   \n",
       "..                                                 ...         ...   \n",
       "637  https://brill.com/view/journals/jesh/57/3/arti...       paper   \n",
       "638  https://www.journals.uchicago.edu/doi/10.1086/...       paper   \n",
       "639  https://www-jstor-org.proxygw.wrlc.org/stable/...       paper   \n",
       "640  https://web-a-ebscohost-com.proxygw.wrlc.org/e...       paper   \n",
       "641  https://www-proquest-com.proxygw.wrlc.org/docv...       paper   \n",
       "\n",
       "                                              Abstract  \n",
       "0                                             Abstract  \n",
       "1                                                 None  \n",
       "2                                                 None  \n",
       "3                                                 None  \n",
       "4                                                 None  \n",
       "..                                                 ...  \n",
       "637  This essay traces the use of the term Qizilb?s...  \n",
       "638  The first decade of the sixteeth century of th...  \n",
       "639                                               None  \n",
       "640                                               None  \n",
       "641  This article examines some aspects of the rece...  \n",
       "\n",
       "[642 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sana = pd.read_sql_query(\"Select * FROM SANA\", connection)\n",
    "sana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b16ea082",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop the unneccary columns \n",
    "sana = sana.drop([0])\n",
    "sana.drop(['Author', 'Language', 'Book/Paper', 'External Links'], axis=1, inplace = True)\n",
    "#Drop the rows with No abstract \n",
    "sana = sana.replace(to_replace='None', value=np.nan).dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c37209f4",
   "metadata": {},
   "source": [
    "# Pre-Processing the Text \n",
    "## Cleaning, Tokenizing and Lemmatizing \n",
    "#### Includes creating binary values for category "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2f39080",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to C:\\Users\\Nabiha\n",
      "[nltk_data]     Naqvie\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
      "[nltk_data]     C:\\Users\\Nabiha Naqvie\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package averaged_perceptron_tagger is already up-to-\n",
      "[nltk_data]       date!\n",
      "[nltk_data] Downloading package wordnet to C:\\Users\\Nabiha\n",
      "[nltk_data]     Naqvie\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#for text pre-processing\n",
    "import re, string\n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import SnowballStemmer\n",
    "from nltk.corpus import wordnet\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "nltk.download('punkt')\n",
    "nltk.download('averaged_perceptron_tagger')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ec7f25c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a function for pre-processing the important columns  \n",
    "def preprocess(text):\n",
    "    text = text.lower() #lowercase text\n",
    "    text = text.strip()  #remove whitespace \n",
    "    text = re.compile('[%s]' % re.escape(string.punctuation)).sub(' ', text) #Replace punctuation with space\n",
    "    text = re.sub('\\s+', ' ', text) #Remove extra space and tabs\n",
    "    text = re.sub(r'[^\\w\\s]', '', str(text).lower().strip())\n",
    "    text = re.sub(r'\\s+',' ',text) #\\s matches any whitespace, \\s+ matches multiple whitespace, \\S matches non-whitespace \n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9074d2fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Apply the function to each column \n",
    "sana['Sub-Category'] = sana['Sub-Category'].apply(preprocess)\n",
    "sana['Abstract'] = sana['Abstract'].apply(preprocess)\n",
    "sana['Title'] = sana['Title'].apply(preprocess)\n",
    "sana['Category'] = sana['Category'].apply(preprocess)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d557a7b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create function for stop words\n",
    "def stopword(string):\n",
    "    a= [i for i in string.split() if i not in stopwords.words('english')]\n",
    "    return ' '.join(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4d52e4d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Apply the function for stop words to each column \n",
    "sana['Category'] = sana['Category'].apply(stopword)\n",
    "sana['Title'] = sana['Title'].apply(stopword)\n",
    "sana['Sub-Category'] = sana['Sub-Category'].apply(stopword)\n",
    "sana['Abstract'] = sana['Abstract'].apply(stopword)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f4c1ff97",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a function to tokenize all columns \n",
    "sana[\"AbstractToken\"] = sana.apply(lambda row: word_tokenize(row[\"Abstract\"]), axis=1)\n",
    "sana[\"Sub-CategoryToken\"] = sana.apply(lambda row: word_tokenize(row[\"Sub-Category\"]), axis=1)\n",
    "sana[\"TitleToken\"] = sana.apply(lambda row: word_tokenize(row[\"Title\"]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c96762ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a function to lemmatize all columns \n",
    "WNL = WordNetLemmatizer()\n",
    "def lemmatize_text(text):\n",
    "    return [WNL.lemmatize(w) for w in text]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3a6cc15c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Apply the function for lemmatize to all columns \n",
    "sana[\"AbstractToken\"] = sana['AbstractToken'].apply(lemmatize_text)\n",
    "sana[\"AbstractL\"] = sana['AbstractToken'].apply(' '.join)\n",
    "sana[\"Sub-CategoryToken\"] = sana['Sub-CategoryToken'].apply(lemmatize_text)\n",
    "sana[\"Sub-CategoryL\"] = sana['Sub-CategoryToken'].apply(' '.join)\n",
    "sana[\"TitleToken\"] = sana['TitleToken'].apply(lemmatize_text)\n",
    "sana[\"TitleL\"] = sana['TitleToken'].apply(' '.join)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8eb70e78",
   "metadata": {},
   "outputs": [
    {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>islam modernity new millennium themes critical...</td>\n",
       "      <td>islamic philosophy</td>\n",
       "      <td>religious studies islamic studies philosophy r...</td>\n",
       "      <td>world becomes increasingly globalised islam fa...</td>\n",
       "      <td>[world, becomes, increasingly, globalised, isl...</td>\n",
       "      <td>[religious, study, islamic, study, philosophy,...</td>\n",
       "      <td>[islam, modernity, new, millennium, theme, cri...</td>\n",
       "      <td>world becomes increasingly globalised islam fa...</td>\n",
       "      <td>religious study islamic study philosophy relig...</td>\n",
       "      <td>islam modernity new millennium theme critical ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>analytic philosophy perspective critical ratio...</td>\n",
       "      <td>islamic philosophy</td>\n",
       "      <td>analytical philosophy critical rationalism</td>\n",
       "      <td>present paper attempt explore impact karl popp...</td>\n",
       "      <td>[present, paper, attempt, explore, impact, kar...</td>\n",
       "      <td>[analytical, philosophy, critical, rationalism]</td>\n",
       "      <td>[analytic, philosophy, perspective, critical, ...</td>\n",
       "      <td>present paper attempt explore impact karl popp...</td>\n",
       "      <td>analytical philosophy critical rationalism</td>\n",
       "      <td>analytic philosophy perspective critical ratio...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>misty land ideas light dialogue anthology comp...</td>\n",
       "      <td>islamic philosophy</td>\n",
       "      <td>islamic philosophy philosophy comparative</td>\n",
       "      <td>comparative philosophy like types philosophy s...</td>\n",
       "      <td>[comparative, philosophy, like, type, philosop...</td>\n",
       "      <td>[islamic, philosophy, philosophy, comparative]</td>\n",
       "      <td>[misty, land, idea, light, dialogue, anthology...</td>\n",
       "      <td>comparative philosophy like type philosophy so...</td>\n",
       "      <td>islamic philosophy philosophy comparative</td>\n",
       "      <td>misty land idea light dialogue anthology compa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>socio ethical consequences convergent technolo...</td>\n",
       "      <td>islamic philosophy</td>\n",
       "      <td>socio ethical consequences converging sciences...</td>\n",
       "      <td>critical assessment social changes impact fact...</td>\n",
       "      <td>[critical, assessment, social, change, impact,...</td>\n",
       "      <td>[socio, ethical, consequence, converging, scie...</td>\n",
       "      <td>[socio, ethical, consequence, convergent, tech...</td>\n",
       "      <td>critical assessment social change impact facto...</td>\n",
       "      <td>socio ethical consequence converging science t...</td>\n",
       "      <td>socio ethical consequence convergent technolog...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>shias britain earliest cases part 1 journal sh...</td>\n",
       "      <td>islamic philosophy</td>\n",
       "      <td>shi muslim counterflows colonialism east india...</td>\n",
       "      <td>shia muslims uk whether individually groups co...</td>\n",
       "      <td>[shia, muslim, uk, whether, individually, grou...</td>\n",
       "      <td>[shi, muslim, counterflows, colonialism, east,...</td>\n",
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       "      <td>shia muslim uk whether individually group comm...</td>\n",
       "      <td>shi muslim counterflows colonialism east india...</td>\n",
       "      <td>shia britain earliest case part 1 journal shia...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>bashir shahzad reading self persian prose poet...</td>\n",
       "      <td>anthropology shia islam</td>\n",
       "      <td>sufism persian poetry historiography 17th cent...</td>\n",
       "      <td>let us start thought experiment five hundred y...</td>\n",
       "      <td>[let, u, start, thought, experiment, five, hun...</td>\n",
       "      <td>[sufism, persian, poetry, historiography, 17th...</td>\n",
       "      <td>[bashir, shahzad, reading, self, persian, pros...</td>\n",
       "      <td>let u start thought experiment five hundred ye...</td>\n",
       "      <td>sufism persian poetry historiography 17th cent...</td>\n",
       "      <td>bashir shahzad reading self persian prose poet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>everlasting doubt uncertainty islamic represen...</td>\n",
       "      <td>anthropology shia islam</td>\n",
       "      <td>historiography islamic history persian arabic ...</td>\n",
       "      <td>utilizing treatments uncertainty regarding his...</td>\n",
       "      <td>[utilizing, treatment, uncertainty, regarding,...</td>\n",
       "      <td>[historiography, islamic, history, persian, ar...</td>\n",
       "      <td>[everlasting, doubt, uncertainty, islamic, rep...</td>\n",
       "      <td>utilizing treatment uncertainty regarding hist...</td>\n",
       "      <td>historiography islamic history persian arabic ...</td>\n",
       "      <td>everlasting doubt uncertainty islamic represen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>origins rhetorical evolution term qizilbāsh pe...</td>\n",
       "      <td>anthropology shia islam</td>\n",
       "      <td>historiography islamic history qizilbash safav...</td>\n",
       "      <td>essay traces use term qizilb sh select literat...</td>\n",
       "      <td>[essay, trace, use, term, qizilb, sh, select, ...</td>\n",
       "      <td>[historiography, islamic, history, qizilbash, ...</td>\n",
       "      <td>[origin, rhetorical, evolution, term, qizilbās...</td>\n",
       "      <td>essay trace use term qizilb sh select literatu...</td>\n",
       "      <td>historiography islamic history qizilbash safav...</td>\n",
       "      <td>origin rhetorical evolution term qizilbāsh per...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>bashir shahzad shah ismaʿil qizilbash cannibal...</td>\n",
       "      <td>anthropology shia islam</td>\n",
       "      <td>historiography islamic history qizilbash safav...</td>\n",
       "      <td>first decade sixteeth century common era saw t...</td>\n",
       "      <td>[first, decade, sixteeth, century, common, era...</td>\n",
       "      <td>[historiography, islamic, history, qizilbash, ...</td>\n",
       "      <td>[bashir, shahzad, shah, ismaʿil, qizilbash, ca...</td>\n",
       "      <td>first decade sixteeth century common era saw t...</td>\n",
       "      <td>historiography islamic history qizilbash safav...</td>\n",
       "      <td>bashir shahzad shah ismaʿil qizilbash cannibal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>empirical spirits islam spiritism virtues scie...</td>\n",
       "      <td>anthropology shia islam</td>\n",
       "      <td>spiritism iran science religion morality</td>\n",
       "      <td>article examines aspects reception french spir...</td>\n",
       "      <td>[article, examines, aspect, reception, french,...</td>\n",
       "      <td>[spiritism, iran, science, religion, morality]</td>\n",
       "      <td>[empirical, spirit, islam, spiritism, virtue, ...</td>\n",
       "      <td>article examines aspect reception french spiri...</td>\n",
       "      <td>spiritism iran science religion morality</td>\n",
       "      <td>empirical spirit islam spiritism virtue scienc...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Title  \\\n",
       "8    islam modernity new millennium themes critical...   \n",
       "12   analytic philosophy perspective critical ratio...   \n",
       "15   misty land ideas light dialogue anthology comp...   \n",
       "16   socio ethical consequences convergent technolo...   \n",
       "29   shias britain earliest cases part 1 journal sh...   \n",
       "..                                                 ...   \n",
       "635  bashir shahzad reading self persian prose poet...   \n",
       "636  everlasting doubt uncertainty islamic represen...   \n",
       "637  origins rhetorical evolution term qizilbāsh pe...   \n",
       "638  bashir shahzad shah ismaʿil qizilbash cannibal...   \n",
       "641  empirical spirits islam spiritism virtues scie...   \n",
       "\n",
       "                    Category  \\\n",
       "8         islamic philosophy   \n",
       "12        islamic philosophy   \n",
       "15        islamic philosophy   \n",
       "16        islamic philosophy   \n",
       "29        islamic philosophy   \n",
       "..                       ...   \n",
       "635  anthropology shia islam   \n",
       "636  anthropology shia islam   \n",
       "637  anthropology shia islam   \n",
       "638  anthropology shia islam   \n",
       "641  anthropology shia islam   \n",
       "\n",
       "                                          Sub-Category  \\\n",
       "8    religious studies islamic studies philosophy r...   \n",
       "12          analytical philosophy critical rationalism   \n",
       "15           islamic philosophy philosophy comparative   \n",
       "16   socio ethical consequences converging sciences...   \n",
       "29   shi muslim counterflows colonialism east india...   \n",
       "..                                                 ...   \n",
       "635  sufism persian poetry historiography 17th cent...   \n",
       "636  historiography islamic history persian arabic ...   \n",
       "637  historiography islamic history qizilbash safav...   \n",
       "638  historiography islamic history qizilbash safav...   \n",
       "641           spiritism iran science religion morality   \n",
       "\n",
       "                                              Abstract  \\\n",
       "8    world becomes increasingly globalised islam fa...   \n",
       "12   present paper attempt explore impact karl popp...   \n",
       "15   comparative philosophy like types philosophy s...   \n",
       "16   critical assessment social changes impact fact...   \n",
       "29   shia muslims uk whether individually groups co...   \n",
       "..                                                 ...   \n",
       "635  let us start thought experiment five hundred y...   \n",
       "636  utilizing treatments uncertainty regarding his...   \n",
       "637  essay traces use term qizilb sh select literat...   \n",
       "638  first decade sixteeth century common era saw t...   \n",
       "641  article examines aspects reception french spir...   \n",
       "\n",
       "                                         AbstractToken  \\\n",
       "8    [world, becomes, increasingly, globalised, isl...   \n",
       "12   [present, paper, attempt, explore, impact, kar...   \n",
       "15   [comparative, philosophy, like, type, philosop...   \n",
       "16   [critical, assessment, social, change, impact,...   \n",
       "29   [shia, muslim, uk, whether, individually, grou...   \n",
       "..                                                 ...   \n",
       "635  [let, u, start, thought, experiment, five, hun...   \n",
       "636  [utilizing, treatment, uncertainty, regarding,...   \n",
       "637  [essay, trace, use, term, qizilb, sh, select, ...   \n",
       "638  [first, decade, sixteeth, century, common, era...   \n",
       "641  [article, examines, aspect, reception, french,...   \n",
       "\n",
       "                                     Sub-CategoryToken  \\\n",
       "8    [religious, study, islamic, study, philosophy,...   \n",
       "12     [analytical, philosophy, critical, rationalism]   \n",
       "15      [islamic, philosophy, philosophy, comparative]   \n",
       "16   [socio, ethical, consequence, converging, scie...   \n",
       "29   [shi, muslim, counterflows, colonialism, east,...   \n",
       "..                                                 ...   \n",
       "635  [sufism, persian, poetry, historiography, 17th...   \n",
       "636  [historiography, islamic, history, persian, ar...   \n",
       "637  [historiography, islamic, history, qizilbash, ...   \n",
       "638  [historiography, islamic, history, qizilbash, ...   \n",
       "641     [spiritism, iran, science, religion, morality]   \n",
       "\n",
       "                                            TitleToken  \\\n",
       "8    [islam, modernity, new, millennium, theme, cri...   \n",
       "12   [analytic, philosophy, perspective, critical, ...   \n",
       "15   [misty, land, idea, light, dialogue, anthology...   \n",
       "16   [socio, ethical, consequence, convergent, tech...   \n",
       "29   [shia, britain, earliest, case, part, 1, journ...   \n",
       "..                                                 ...   \n",
       "635  [bashir, shahzad, reading, self, persian, pros...   \n",
       "636  [everlasting, doubt, uncertainty, islamic, rep...   \n",
       "637  [origin, rhetorical, evolution, term, qizilbās...   \n",
       "638  [bashir, shahzad, shah, ismaʿil, qizilbash, ca...   \n",
       "641  [empirical, spirit, islam, spiritism, virtue, ...   \n",
       "\n",
       "                                             AbstractL  \\\n",
       "8    world becomes increasingly globalised islam fa...   \n",
       "12   present paper attempt explore impact karl popp...   \n",
       "15   comparative philosophy like type philosophy so...   \n",
       "16   critical assessment social change impact facto...   \n",
       "29   shia muslim uk whether individually group comm...   \n",
       "..                                                 ...   \n",
       "635  let u start thought experiment five hundred ye...   \n",
       "636  utilizing treatment uncertainty regarding hist...   \n",
       "637  essay trace use term qizilb sh select literatu...   \n",
       "638  first decade sixteeth century common era saw t...   \n",
       "641  article examines aspect reception french spiri...   \n",
       "\n",
       "                                         Sub-CategoryL  \\\n",
       "8    religious study islamic study philosophy relig...   \n",
       "12          analytical philosophy critical rationalism   \n",
       "15           islamic philosophy philosophy comparative   \n",
       "16   socio ethical consequence converging science t...   \n",
       "29   shi muslim counterflows colonialism east india...   \n",
       "..                                                 ...   \n",
       "635  sufism persian poetry historiography 17th cent...   \n",
       "636  historiography islamic history persian arabic ...   \n",
       "637  historiography islamic history qizilbash safav...   \n",
       "638  historiography islamic history qizilbash safav...   \n",
       "641           spiritism iran science religion morality   \n",
       "\n",
       "                                                TitleL  \n",
       "8    islam modernity new millennium theme critical ...  \n",
       "12   analytic philosophy perspective critical ratio...  \n",
       "15   misty land idea light dialogue anthology compa...  \n",
       "16   socio ethical consequence convergent technolog...  \n",
       "29   shia britain earliest case part 1 journal shia...  \n",
       "..                                                 ...  \n",
       "635  bashir shahzad reading self persian prose poet...  \n",
       "636  everlasting doubt uncertainty islamic represen...  \n",
       "637  origin rhetorical evolution term qizilbāsh per...  \n",
       "638  bashir shahzad shah ismaʿil qizilbash cannibal...  \n",
       "641  empirical spirit islam spiritism virtue scienc...  \n",
       "\n",
       "[198 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#look at all the results \n",
    "sana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4a8ceeac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Abstract</th>\n",
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       "      <th>TitleL</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>islam modernity new millennium themes critical...</td>\n",
       "      <td>islamic philosophy</td>\n",
       "      <td>religious studies islamic studies philosophy r...</td>\n",
       "      <td>world becomes increasingly globalised islam fa...</td>\n",
       "      <td>world becomes increasingly globalised islam fa...</td>\n",
       "      <td>religious study islamic study philosophy relig...</td>\n",
       "      <td>islam modernity new millennium theme critical ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>analytic philosophy perspective critical ratio...</td>\n",
       "      <td>islamic philosophy</td>\n",
       "      <td>analytical philosophy critical rationalism</td>\n",
       "      <td>present paper attempt explore impact karl popp...</td>\n",
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       "      <td>analytical philosophy critical rationalism</td>\n",
       "      <td>analytic philosophy perspective critical ratio...</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>misty land ideas light dialogue anthology comp...</td>\n",
       "      <td>islamic philosophy</td>\n",
       "      <td>islamic philosophy philosophy comparative</td>\n",
       "      <td>comparative philosophy like types philosophy s...</td>\n",
       "      <td>comparative philosophy like type philosophy so...</td>\n",
       "      <td>islamic philosophy philosophy comparative</td>\n",
       "      <td>misty land idea light dialogue anthology compa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>socio ethical consequences convergent technolo...</td>\n",
       "      <td>islamic philosophy</td>\n",
       "      <td>socio ethical consequences converging sciences...</td>\n",
       "      <td>critical assessment social changes impact fact...</td>\n",
       "      <td>critical assessment social change impact facto...</td>\n",
       "      <td>socio ethical consequence converging science t...</td>\n",
       "      <td>socio ethical consequence convergent technolog...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>shias britain earliest cases part 1 journal sh...</td>\n",
       "      <td>islamic philosophy</td>\n",
       "      <td>shi muslim counterflows colonialism east india...</td>\n",
       "      <td>shia muslims uk whether individually groups co...</td>\n",
       "      <td>shia muslim uk whether individually group comm...</td>\n",
       "      <td>shi muslim counterflows colonialism east india...</td>\n",
       "      <td>shia britain earliest case part 1 journal shia...</td>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>635</th>\n",
       "      <td>bashir shahzad reading self persian prose poet...</td>\n",
       "      <td>anthropology shia islam</td>\n",
       "      <td>sufism persian poetry historiography 17th cent...</td>\n",
       "      <td>let us start thought experiment five hundred y...</td>\n",
       "      <td>let u start thought experiment five hundred ye...</td>\n",
       "      <td>sufism persian poetry historiography 17th cent...</td>\n",
       "      <td>bashir shahzad reading self persian prose poet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>636</th>\n",
       "      <td>everlasting doubt uncertainty islamic represen...</td>\n",
       "      <td>anthropology shia islam</td>\n",
       "      <td>historiography islamic history persian arabic ...</td>\n",
       "      <td>utilizing treatments uncertainty regarding his...</td>\n",
       "      <td>utilizing treatment uncertainty regarding hist...</td>\n",
       "      <td>historiography islamic history persian arabic ...</td>\n",
       "      <td>everlasting doubt uncertainty islamic represen...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>origins rhetorical evolution term qizilbāsh pe...</td>\n",
       "      <td>anthropology shia islam</td>\n",
       "      <td>historiography islamic history qizilbash safav...</td>\n",
       "      <td>essay traces use term qizilb sh select literat...</td>\n",
       "      <td>essay trace use term qizilb sh select literatu...</td>\n",
       "      <td>historiography islamic history qizilbash safav...</td>\n",
       "      <td>origin rhetorical evolution term qizilbāsh per...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>bashir shahzad shah ismaʿil qizilbash cannibal...</td>\n",
       "      <td>anthropology shia islam</td>\n",
       "      <td>historiography islamic history qizilbash safav...</td>\n",
       "      <td>first decade sixteeth century common era saw t...</td>\n",
       "      <td>first decade sixteeth century common era saw t...</td>\n",
       "      <td>historiography islamic history qizilbash safav...</td>\n",
       "      <td>bashir shahzad shah ismaʿil qizilbash cannibal...</td>\n",
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       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>empirical spirits islam spiritism virtues scie...</td>\n",
       "      <td>anthropology shia islam</td>\n",
       "      <td>spiritism iran science religion morality</td>\n",
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       "      <td>article examines aspect reception french spiri...</td>\n",
       "      <td>spiritism iran science religion morality</td>\n",
       "      <td>empirical spirit islam spiritism virtue scienc...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Title  \\\n",
       "8    islam modernity new millennium themes critical...   \n",
       "12   analytic philosophy perspective critical ratio...   \n",
       "15   misty land ideas light dialogue anthology comp...   \n",
       "16   socio ethical consequences convergent technolo...   \n",
       "29   shias britain earliest cases part 1 journal sh...   \n",
       "..                                                 ...   \n",
       "635  bashir shahzad reading self persian prose poet...   \n",
       "636  everlasting doubt uncertainty islamic represen...   \n",
       "637  origins rhetorical evolution term qizilbāsh pe...   \n",
       "638  bashir shahzad shah ismaʿil qizilbash cannibal...   \n",
       "641  empirical spirits islam spiritism virtues scie...   \n",
       "\n",
       "                    Category  \\\n",
       "8         islamic philosophy   \n",
       "12        islamic philosophy   \n",
       "15        islamic philosophy   \n",
       "16        islamic philosophy   \n",
       "29        islamic philosophy   \n",
       "..                       ...   \n",
       "635  anthropology shia islam   \n",
       "636  anthropology shia islam   \n",
       "637  anthropology shia islam   \n",
       "638  anthropology shia islam   \n",
       "641  anthropology shia islam   \n",
       "\n",
       "                                          Sub-Category  \\\n",
       "8    religious studies islamic studies philosophy r...   \n",
       "12          analytical philosophy critical rationalism   \n",
       "15           islamic philosophy philosophy comparative   \n",
       "16   socio ethical consequences converging sciences...   \n",
       "29   shi muslim counterflows colonialism east india...   \n",
       "..                                                 ...   \n",
       "635  sufism persian poetry historiography 17th cent...   \n",
       "636  historiography islamic history persian arabic ...   \n",
       "637  historiography islamic history qizilbash safav...   \n",
       "638  historiography islamic history qizilbash safav...   \n",
       "641           spiritism iran science religion morality   \n",
       "\n",
       "                                              Abstract  \\\n",
       "8    world becomes increasingly globalised islam fa...   \n",
       "12   present paper attempt explore impact karl popp...   \n",
       "15   comparative philosophy like types philosophy s...   \n",
       "16   critical assessment social changes impact fact...   \n",
       "29   shia muslims uk whether individually groups co...   \n",
       "..                                                 ...   \n",
       "635  let us start thought experiment five hundred y...   \n",
       "636  utilizing treatments uncertainty regarding his...   \n",
       "637  essay traces use term qizilb sh select literat...   \n",
       "638  first decade sixteeth century common era saw t...   \n",
       "641  article examines aspects reception french spir...   \n",
       "\n",
       "                                             AbstractL  \\\n",
       "8    world becomes increasingly globalised islam fa...   \n",
       "12   present paper attempt explore impact karl popp...   \n",
       "15   comparative philosophy like type philosophy so...   \n",
       "16   critical assessment social change impact facto...   \n",
       "29   shia muslim uk whether individually group comm...   \n",
       "..                                                 ...   \n",
       "635  let u start thought experiment five hundred ye...   \n",
       "636  utilizing treatment uncertainty regarding hist...   \n",
       "637  essay trace use term qizilb sh select literatu...   \n",
       "638  first decade sixteeth century common era saw t...   \n",
       "641  article examines aspect reception french spiri...   \n",
       "\n",
       "                                         Sub-CategoryL  \\\n",
       "8    religious study islamic study philosophy relig...   \n",
       "12          analytical philosophy critical rationalism   \n",
       "15           islamic philosophy philosophy comparative   \n",
       "16   socio ethical consequence converging science t...   \n",
       "29   shi muslim counterflows colonialism east india...   \n",
       "..                                                 ...   \n",
       "635  sufism persian poetry historiography 17th cent...   \n",
       "636  historiography islamic history persian arabic ...   \n",
       "637  historiography islamic history qizilbash safav...   \n",
       "638  historiography islamic history qizilbash safav...   \n",
       "641           spiritism iran science religion morality   \n",
       "\n",
       "                                                TitleL  \n",
       "8    islam modernity new millennium theme critical ...  \n",
       "12   analytic philosophy perspective critical ratio...  \n",
       "15   misty land idea light dialogue anthology compa...  \n",
       "16   socio ethical consequence convergent technolog...  \n",
       "29   shia britain earliest case part 1 journal shia...  \n",
       "..                                                 ...  \n",
       "635  bashir shahzad reading self persian prose poet...  \n",
       "636  everlasting doubt uncertainty islamic represen...  \n",
       "637  origin rhetorical evolution term qizilbāsh per...  \n",
       "638  bashir shahzad shah ismaʿil qizilbash cannibal...  \n",
       "641  empirical spirit islam spiritism virtue scienc...  \n",
       "\n",
       "[198 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop the unneccasary columns\n",
    "sana.drop(['AbstractToken', 'Sub-CategoryToken', 'TitleToken'], axis=1, inplace = True)\n",
    "sana"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "22ca0c35",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reset the indexes \n",
    "sana.reset_index(inplace = True)\n",
    "sana.drop(['index'], axis=1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6de30185",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Abstract</th>\n",
       "      <th>AbstractL</th>\n",
       "      <th>Sub-CategoryL</th>\n",
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       "      <td>islam modernity new millennium themes critical...</td>\n",
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       "      <td>sufism persian poetry historiography 17th cent...</td>\n",
       "      <td>bashir shahzad reading self persian prose poet...</td>\n",
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       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>everlasting doubt uncertainty islamic represen...</td>\n",
       "      <td>0</td>\n",
       "      <td>historiography islamic history persian arabic ...</td>\n",
       "      <td>utilizing treatments uncertainty regarding his...</td>\n",
       "      <td>utilizing treatment uncertainty regarding hist...</td>\n",
       "      <td>historiography islamic history persian arabic ...</td>\n",
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       "      <th>195</th>\n",
       "      <td>origins rhetorical evolution term qizilbāsh pe...</td>\n",
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       "      <td>historiography islamic history qizilbash safav...</td>\n",
       "      <td>essay traces use term qizilb sh select literat...</td>\n",
       "      <td>essay trace use term qizilb sh select literatu...</td>\n",
       "      <td>historiography islamic history qizilbash safav...</td>\n",
       "      <td>origin rhetorical evolution term qizilbāsh per...</td>\n",
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       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>bashir shahzad shah ismaʿil qizilbash cannibal...</td>\n",
       "      <td>0</td>\n",
       "      <td>historiography islamic history qizilbash safav...</td>\n",
       "      <td>first decade sixteeth century common era saw t...</td>\n",
       "      <td>first decade sixteeth century common era saw t...</td>\n",
       "      <td>historiography islamic history qizilbash safav...</td>\n",
       "      <td>bashir shahzad shah ismaʿil qizilbash cannibal...</td>\n",
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       "      <td>article examines aspect reception french spiri...</td>\n",
       "      <td>spiritism iran science religion morality</td>\n",
       "      <td>empirical spirit islam spiritism virtue scienc...</td>\n",
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       "</table>\n",
       "<p>198 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Title  Category  \\\n",
       "0    islam modernity new millennium themes critical...         1   \n",
       "1    analytic philosophy perspective critical ratio...         1   \n",
       "2    misty land ideas light dialogue anthology comp...         1   \n",
       "3    socio ethical consequences convergent technolo...         1   \n",
       "4    shias britain earliest cases part 1 journal sh...         1   \n",
       "..                                                 ...       ...   \n",
       "193  bashir shahzad reading self persian prose poet...         0   \n",
       "194  everlasting doubt uncertainty islamic represen...         0   \n",
       "195  origins rhetorical evolution term qizilbāsh pe...         0   \n",
       "196  bashir shahzad shah ismaʿil qizilbash cannibal...         0   \n",
       "197  empirical spirits islam spiritism virtues scie...         0   \n",
       "\n",
       "                                          Sub-Category  \\\n",
       "0    religious studies islamic studies philosophy r...   \n",
       "1           analytical philosophy critical rationalism   \n",
       "2            islamic philosophy philosophy comparative   \n",
       "3    socio ethical consequences converging sciences...   \n",
       "4    shi muslim counterflows colonialism east india...   \n",
       "..                                                 ...   \n",
       "193  sufism persian poetry historiography 17th cent...   \n",
       "194  historiography islamic history persian arabic ...   \n",
       "195  historiography islamic history qizilbash safav...   \n",
       "196  historiography islamic history qizilbash safav...   \n",
       "197           spiritism iran science religion morality   \n",
       "\n",
       "                                              Abstract  \\\n",
       "0    world becomes increasingly globalised islam fa...   \n",
       "1    present paper attempt explore impact karl popp...   \n",
       "2    comparative philosophy like types philosophy s...   \n",
       "3    critical assessment social changes impact fact...   \n",
       "4    shia muslims uk whether individually groups co...   \n",
       "..                                                 ...   \n",
       "193  let us start thought experiment five hundred y...   \n",
       "194  utilizing treatments uncertainty regarding his...   \n",
       "195  essay traces use term qizilb sh select literat...   \n",
       "196  first decade sixteeth century common era saw t...   \n",
       "197  article examines aspects reception french spir...   \n",
       "\n",
       "                                             AbstractL  \\\n",
       "0    world becomes increasingly globalised islam fa...   \n",
       "1    present paper attempt explore impact karl popp...   \n",
       "2    comparative philosophy like type philosophy so...   \n",
       "3    critical assessment social change impact facto...   \n",
       "4    shia muslim uk whether individually group comm...   \n",
       "..                                                 ...   \n",
       "193  let u start thought experiment five hundred ye...   \n",
       "194  utilizing treatment uncertainty regarding hist...   \n",
       "195  essay trace use term qizilb sh select literatu...   \n",
       "196  first decade sixteeth century common era saw t...   \n",
       "197  article examines aspect reception french spiri...   \n",
       "\n",
       "                                         Sub-CategoryL  \\\n",
       "0    religious study islamic study philosophy relig...   \n",
       "1           analytical philosophy critical rationalism   \n",
       "2            islamic philosophy philosophy comparative   \n",
       "3    socio ethical consequence converging science t...   \n",
       "4    shi muslim counterflows colonialism east india...   \n",
       "..                                                 ...   \n",
       "193  sufism persian poetry historiography 17th cent...   \n",
       "194  historiography islamic history persian arabic ...   \n",
       "195  historiography islamic history qizilbash safav...   \n",
       "196  historiography islamic history qizilbash safav...   \n",
       "197           spiritism iran science religion morality   \n",
       "\n",
       "                                                TitleL  \n",
       "0    islam modernity new millennium theme critical ...  \n",
       "1    analytic philosophy perspective critical ratio...  \n",
       "2    misty land idea light dialogue anthology compa...  \n",
       "3    socio ethical consequence convergent technolog...  \n",
       "4    shia britain earliest case part 1 journal shia...  \n",
       "..                                                 ...  \n",
       "193  bashir shahzad reading self persian prose poet...  \n",
       "194  everlasting doubt uncertainty islamic represen...  \n",
       "195  origin rhetorical evolution term qizilbāsh per...  \n",
       "196  bashir shahzad shah ismaʿil qizilbash cannibal...  \n",
       "197  empirical spirit islam spiritism virtue scienc...  \n",
       "\n",
       "[198 rows x 7 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create numerical column for category \n",
    "sana['Category'].value_counts()\n",
    "category = {'anthropology shia islam': 0, 'islamic philosophy': 1}\n",
    "sana['Category'] = [category[item] for item in sana['Category']]\n",
    "sana"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cea7484",
   "metadata": {},
   "source": [
    "# Vectorizing Data \n",
    "#### Importing for ML "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "af0017c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for model-building\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import classification_report, f1_score, accuracy_score, confusion_matrix\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "\n",
    "# bag of words\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2184878f",
   "metadata": {},
   "source": [
    "###### CountVectorize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "84f02753",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(198, 4974)\n",
      "['000', '100', '1000', '1010', '1021', '1088', '10th', '1111', '1164', '1171', '11th', '1210', '1274', '12th', '12thcenturyce', '1334', '135', '1350', '1370', '1382', '1389', '1390', '1392', '1394', '1399', '13th', '14', '1405', '1406', '1409', '1413', '1414', '1418', '1423', '1426', '1432', '1433', '1447', '1449', '1454', '1460s', '1464', '1469', '1488', '1490', '14901540ce', '14th', '150', '1501', '1502', '15121520', '15181687', '15201566', '1524', '1534', '157', '1587', '1592', '15th', '16', '1626', '1629', '163', '1642', '16th', '1700', '175', '18', '1835', '1867', '1882', '1899', '19', '1903', '1909', '1940s', '1950s', '1955das', '1960s', '1970s', '1975', '1976', '1978', '1979', '19791989', '1980s', '1982', '1989', '19892005', '1990s', '1991', '1992', '1993', '1995', '1996', '1997', '1998', '1from', '1the', '1two', '20', '200', '2003', '2005', '20052013', '2006', '2007', '2009', '2010', '2011', '2016', '2017', '2018', '2025', '20th', '21', '22', '25', '25b', '28', '298', '2nd', '302', '32', '333', '334', '335', '336', '341', '401', '411', '481', '4th', '505', '5th', '606', '680', '6th', '751', '771', '784', '79', '791', '792', '796', '7th', '80', '801', '808', '858', '88', '8th', '90', '909', '911', '915', '918926', '926974', '940', '945', '946', '947', '948', '953', '9th', 'ab', 'abadi', 'abandon', 'abar', 'abbas', 'abbasid', 'abd', 'abegescheidenheit', 'abi', 'abidance', 'ability', 'able', 'abomination', 'abrahamic', 'abridged', 'absence', 'absorption', 'abu', 'abundantly', 'aca', 'academia', 'academic', 'academy', 'accept', 'acceptance', 'access', 'accessing', 'acclaimed', 'accommodate', 'accommodation', 'accompanied', 'accomplished', 'accordance', 'according', 'accordingly', 'account', 'accountability', 'accountable', 'acculturated', 'accurate', 'accurately', 'accused', 'accusing', 'achieve', 'achieved', 'achievement', 'achieving', 'acknowledge', 'acknowledged', 'acknowledging', 'acquire', 'acquired', 'acquires', 'acquisition', 'across', 'act', 'acted', 'action', 'activated', 'active', 'actively', 'activism', 'activist', 'activity', 'actor', 'actual', 'actually', 'acute', 'acutely', 'ad', 'adabized', 'adam', 'adamic', 'adaptation', 'add', 'added', 'addition', 'additional', 'address', 'addressed', 'addressing', 'adequate', 'adhered', 'administration', 'admitted', 'adopt', 'adopted', 'adopting', 'adoption', 'adoration', 'advance', 'advanced', 'advancement', 'advancing', 'advent', 'adventurer', 'adversary', 'advertises', 'advocate', 'aesthetic', 'af', 'afarn', 'afavid', 'affair', 'affect', 'affected', 'affiliation', 'affirmed', 'affirms', 'afghani', 'afghanistan', 'afghanjihadagainst', 'afield', 'africa', 'aftermath', 'afterward', 'aga', 'agamben', 'age', 'agency', 'agent', 'aggression', 'aggressive', 'ago', 'agreement', 'ah', 'ahd', 'ahkam', 'ahl', 'ahmad', 'ahmads', 'ahmar', 'aid', 'aim', 'aimed', 'aiva', 'akhl', 'al', 'alam', 'alarming', 'albania', 'albeit', 'alchemy', 'ald', 'alevilik', 'alevis', 'alevism', 'alexandre', 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'antique', 'antiquity', 'anw', 'anything', 'apart', 'aperture', 'apostasy', 'apparatus', 'apparent', 'apparently', 'appeal', 'appealed', 'appear', 'appeared', 'appearing', 'appears', 'append', 'applicable', 'application', 'applied', 'applying', 'appoint', 'appointee', 'appraised', 'appreciated', 'appreciation', 'approach', 'approaching', 'appropriate', 'appropriated', 'appropriation', 'approximate', 'april', 'apt', 'aqq', 'aqquyunlu', 'ar', 'ara', 'arab', 'arabi', 'arabia', 'arabian', 'arabic', 'arabism', 'arabist', 'arabo', 'araf', 'arba', 'arbitrator', 'arcane', 'archetypical', 'architectural', 'architecture', 'archival', 'ardabil', 'area', 'arena', 'arguably', 'argue', 'argued', 'argues', 'arguing', 'argument', 'aristotelian', 'aristotle', 'armed', 'army', 'around', 'arram', 'arrange', 'array', 'arrival', 'arriving', 'art', 'article', 'articulate', 'articulated', 'artifact', 'artificial', 'asad', 'ascendancy', 'ascribed', 'ashar', 'ashura', 'asia', 'asian', 'aside', 'ask', 'asked', 'askhatt', 'asking', 'asks', 'aspect', 'aspiration', 'asr', 'asraf', 'ass', 'assembly', 'assert', 'assessed', 'assessing', 'assessment', 'assist', 'assistance', 'asso', 'associate', 'associated', 'association', 'assorted', 'assume', 'assumed', 'assumption', 'astar', 'astatbir', 'astral', 'astrological', 'astrology', 'astrologywith', 'astronomical', 'ata', 'ath', 'athist', 'attached', 'attachment', 'attained', 'attaining', 'attar', 'attempt', 'attendant', 'attention', 'attestation', 'attitude', 'attracted', 'attraction', 'attribute', 'attributed', 'audience', 'augmented', 'august', 'author', 'authored', 'authoritarianism', 'authoritative', 'authority', 'authorship', 'ava', 'avatar', 'aver', 'aversion', 'averting', 'avoid', 'avoided', 'avoiding', 'await', 'aware', 'away', 'awe', 'awziyyas', 'axis', 'ay', 'ayatollah', 'ayatullah', 'aydar', 'ayr', 'ayriyya', 'az', 'aza', 'ba', 'baby', 'back', 'background', 'backward', 'bacon', 'bad', 'bada', 'badakhshan', 'badakhshanis', 'bah', 'bahrain', 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'translated', 'translation', 'transmission', 'transmogrification', 'transnational', 'transnationally', 'transregionally', 'trap', 'travel', 'traveler', 'travelled', 'traveller', 'treat', 'treated', 'treatise', 'treatment', 'trend', 'triad', 'trial', 'tried', 'triggered', 'trinity', 'tripartite', 'triply', 'triumph', 'trope', 'true', 'truly', 'trust', 'truth', 'try', 'trying', 'tumultuous', 'tune', 'tunisia', 'turka', 'turkas', 'turkey', 'turkish', 'turmoil', 'turn', 'turned', 'turning', 'twelfth', 'twelve', 'twelver', 'twelverization', 'twentieth', 'twenty', 'twin', 'two', 'type', 'typical', 'typically', 'tyrant', 'uals', 'ubiquitous', 'udis', 'uk', 'ulam', 'ulama', 'ulema', 'ultimate', 'ultimately', 'ulugh', 'umbrella', 'umm', 'umma', 'un', 'unanimously', 'unauthentic', 'uncatalogued', 'uncertainly', 'uncertainty', 'uncover', 'uncritical', 'und', 'underdeveloped', 'underdevelopment', 'undergoes', 'underlying', 'underscore', 'understand', 'understanding', 'understood', 'undertake', 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'whether', 'whilst', 'whole', 'whose', 'wide', 'widely', 'wider', 'widespread', 'wield', 'wild', 'willingness', 'window', 'wing', 'wisdom', 'wish', 'wit', 'witchcraft', 'within', 'without', 'witnessed', 'woman', 'womb', 'wonder', 'word', 'work', 'worked', 'working', 'world', 'worldly', 'worldview', 'worldviews', 'worldwide', 'worry', 'worsened', 'worship', 'worshipful', 'worshipped', 'would', 'writer', 'writing', 'written', 'wrote', 'x00a0', 'xosrow', 'xvii', 'ya', 'yamun', 'yaqub', 'yaz', 'yazd', 'ye', 'year', 'yemen', 'yet', 'yetzirah', 'yi', 'yielded', 'yielding', 'young', 'younger', 'ysunghur', 'za', 'zahir', 'zahra', 'zahras', 'zana', 'zaydis', 'zaynab', 'zbek', 'zeal', 'zero', 'ziya', 'zo', 'zul']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda 2\\envs\\SANA\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#Vectorize the Abstract \n",
    "#The results for counts.shape give a tuple that indicate 198 abstract that have 4974 unique words \n",
    "count_vect = CountVectorizer()\n",
    "Xa_counts = count_vect.fit_transform(sana['AbstractL'])\n",
    "print(Xa_counts.shape)\n",
    "print(count_vect.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2f83974d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<198x4974 sparse matrix of type '<class 'numpy.int64'>'\n",
       "\twith 13496 stored elements in Compressed Sparse Row format>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xa_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0884090a",
   "metadata": {},
   "outputs": [
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       "\n",
       "[198 rows x 4974 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Putting the result above into the dataframe \n",
    "Xa_counts_df = pd.DataFrame(Xa_counts.toarray())\n",
    "Xa_counts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7c7b006c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda 2\\envs\\SANA\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
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      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The results here show the same table as above except here it has columns names \n",
    "Xa_counts_df.columns = count_vect.get_feature_names()\n",
    "Xa_counts_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3676859d",
   "metadata": {},
   "source": [
    "##### N-Grams for TFIDF "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3434bbd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  (0, 15473)\t1\n",
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      "  (0, 6283)\t1\n",
      "  :\t:\n",
      "  (197, 2948)\t1\n",
      "  (197, 10834)\t1\n",
      "  (197, 12350)\t1\n",
      "  (197, 14918)\t1\n",
      "  (197, 3793)\t1\n",
      "  (197, 8848)\t1\n",
      "  (197, 13476)\t1\n",
      "  (197, 1004)\t1\n",
      "  (197, 1389)\t1\n",
      "  (197, 7414)\t1\n",
      "  (197, 10674)\t1\n",
      "  (197, 3963)\t1\n",
      "  (197, 704)\t1\n",
      "  (197, 686)\t1\n",
      "  (197, 14739)\t1\n",
      "  (197, 12294)\t1\n",
      "  (197, 10575)\t1\n",
      "  (197, 8735)\t1\n",
      "  (197, 8769)\t1\n",
      "  (197, 13620)\t1\n",
      "  (197, 11388)\t1\n",
      "  (197, 12481)\t1\n",
      "  (197, 255)\t1\n",
      "  (197, 8032)\t1\n",
      "  (197, 2973)\t1\n"
     ]
    }
   ],
   "source": [
    "#Looking for \n",
    "ngram_vect = CountVectorizer(ngram_range = (3,3))\n",
    "X_counts = ngram_vect.fit_transform(sana['AbstractL'])\n",
    "print(X_counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "5f381aa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "D:\\Anaconda 2\\envs\\SANA\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 15678 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     000 participant bahrain  100 000 participant  1000 1592 ce  1010 maw il  \\\n",
       "0                          0                    0             0            0   \n",
       "1                          0                    0             0            0   \n",
       "2                          0                    0             0            0   \n",
       "3                          0                    0             0            0   \n",
       "4                          0                    0             0            0   \n",
       "..                       ...                  ...           ...          ...   \n",
       "193                        0                    0             0            0   \n",
       "194                        0                    0             0            0   \n",
       "195                        0                    0             0            0   \n",
       "196                        0                    0             0            0   \n",
       "197                        0                    0             0            0   \n",
       "\n",
       "     1021 powerful ruler  1088 introducing ir  1088 renowned ism  \\\n",
       "0                      0                    0                  0   \n",
       "1                      0                    0                  0   \n",
       "2                      0                    0                  0   \n",
       "3                      0                    0                  0   \n",
       "4                      0                    0                  0   \n",
       "..                   ...                  ...                ...   \n",
       "193                    0                    0                  0   \n",
       "194                    0                    0                  0   \n",
       "195                    0                    0                  0   \n",
       "196                    0                    0                  0   \n",
       "197                    0                    0                  0   \n",
       "\n",
       "     10th century draw  10th century eventually  10th century missionary  ...  \\\n",
       "0                    0                        0                        0  ...   \n",
       "1                    0                        0                        0  ...   \n",
       "2                    0                        0                        0  ...   \n",
       "3                    0                        0                        0  ...   \n",
       "4                    0                        0                        0  ...   \n",
       "..                 ...                      ...                      ...  ...   \n",
       "193                  0                        0                        0  ...   \n",
       "194                  0                        0                        0  ...   \n",
       "195                  0                        0                        0  ...   \n",
       "196                  0                        0                        0  ...   \n",
       "197                  0                        0                        0  ...   \n",
       "\n",
       "     zaynab though sayyida  zaynab twelver shiis  zaynab well analyze  \\\n",
       "0                        0                     0                    0   \n",
       "1                        0                     0                    0   \n",
       "2                        0                     0                    0   \n",
       "3                        0                     0                    0   \n",
       "4                        0                     0                    0   \n",
       "..                     ...                   ...                  ...   \n",
       "193                      0                     0                    0   \n",
       "194                      0                     0                    0   \n",
       "195                      0                     0                    0   \n",
       "196                      0                     0                    0   \n",
       "197                      0                     0                    0   \n",
       "\n",
       "     zbek activity twelver  zbek khan golden  zeal individ uals  \\\n",
       "0                        0                 0                  0   \n",
       "1                        0                 0                  0   \n",
       "2                        0                 0                  0   \n",
       "3                        0                 0                  0   \n",
       "4                        0                 0                  0   \n",
       "..                     ...               ...                ...   \n",
       "193                      0                 0                  0   \n",
       "194                      0                 0                  0   \n",
       "195                      0                 0                  0   \n",
       "196                      0                 0                  0   \n",
       "197                      0                 0                  0   \n",
       "\n",
       "     zero sum game  ziya battle standard  zo state exception  zul qarneyn work  \n",
       "0                0                     0                   0                 0  \n",
       "1                0                     0                   0                 0  \n",
       "2                0                     0                   0                 0  \n",
       "3                0                     0                   0                 0  \n",
       "4                0                     0                   0                 0  \n",
       "..             ...                   ...                 ...               ...  \n",
       "193              0                     0                   0                 0  \n",
       "194              0                     0                   0                 0  \n",
       "195              0                     0                   0                 0  \n",
       "196              0                     0                   0                 0  \n",
       "197              0                     0                   0                 0  \n",
       "\n",
       "[198 rows x 15678 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_counts_df = pd.DataFrame(X_counts.toarray())\n",
    "X_counts_df.columns = ngram_vect.get_feature_names()\n",
    "X_counts_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3d14eb7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(198, 4974)\n",
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'translated', 'translation', 'transmission', 'transmogrification', 'transnational', 'transnationally', 'transregionally', 'trap', 'travel', 'traveler', 'travelled', 'traveller', 'treat', 'treated', 'treatise', 'treatment', 'trend', 'triad', 'trial', 'tried', 'triggered', 'trinity', 'tripartite', 'triply', 'triumph', 'trope', 'true', 'truly', 'trust', 'truth', 'try', 'trying', 'tumultuous', 'tune', 'tunisia', 'turka', 'turkas', 'turkey', 'turkish', 'turmoil', 'turn', 'turned', 'turning', 'twelfth', 'twelve', 'twelver', 'twelverization', 'twentieth', 'twenty', 'twin', 'two', 'type', 'typical', 'typically', 'tyrant', 'uals', 'ubiquitous', 'udis', 'uk', 'ulam', 'ulama', 'ulema', 'ultimate', 'ultimately', 'ulugh', 'umbrella', 'umm', 'umma', 'un', 'unanimously', 'unauthentic', 'uncatalogued', 'uncertainly', 'uncertainty', 'uncover', 'uncritical', 'und', 'underdeveloped', 'underdevelopment', 'undergoes', 'underlying', 'underscore', 'understand', 'understanding', 'understood', 'undertake', 'undertaken', 'undertaking', 'undesirable', 'undesired', 'unequivocal', 'uneven', 'unexamined', 'unforgettable', 'unilateral', 'uninteresting', 'union', 'unique', 'uniquely', 'united', 'unity', 'universal', 'universalism', 'universalist', 'universality', 'universe', 'university', 'unknown', 'unprecedented', 'unpredictable', 'unpublished', 'unrelated', 'unsatisfactory', 'unsettled', 'unsettling', 'untapped', 'untenable', 'unteren', 'unwanted', 'unwavering', 'unwished', 'upheld', 'upon', 'uprising', 'ups', 'upshot', 'upsurge', 'ur', 'urafa', 'urban', 'urbanism', 'urdu', 'ures', 'urge', 'urgency', 'urgent', 'us', 'usa', 'usage', 'usayn', 'use', 'used', 'useful', 'user', 'using', 'ustad', 'usual', 'usually', 'usurpation', 'utilised', 'utilize', 'utilizes', 'utilizing', 'utmost', 'utopian', 'uzun', 'va', 'vacuum', 'vai', 'vaired', 'valence', 'valency', 'vali', 'valid', 'validated', 'validity', 'valorized', 'valuable', 'value', 'valued', 'vanished', 'variant', 'variation', 'varied', 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'whether', 'whilst', 'whole', 'whose', 'wide', 'widely', 'wider', 'widespread', 'wield', 'wild', 'willingness', 'window', 'wing', 'wisdom', 'wish', 'wit', 'witchcraft', 'within', 'without', 'witnessed', 'woman', 'womb', 'wonder', 'word', 'work', 'worked', 'working', 'world', 'worldly', 'worldview', 'worldviews', 'worldwide', 'worry', 'worsened', 'worship', 'worshipful', 'worshipped', 'would', 'writer', 'writing', 'written', 'wrote', 'x00a0', 'xosrow', 'xvii', 'ya', 'yamun', 'yaqub', 'yaz', 'yazd', 'ye', 'year', 'yemen', 'yet', 'yetzirah', 'yi', 'yielded', 'yielding', 'young', 'younger', 'ysunghur', 'za', 'zahir', 'zahra', 'zahras', 'zana', 'zaydis', 'zaynab', 'zbek', 'zeal', 'zero', 'ziya', 'zo', 'zul']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda 2\\envs\\SANA\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#Applying TFIDF Vectorizer\n",
    "tfidf_vect = TfidfVectorizer()\n",
    "X_tfidf = tfidf_vect.fit_transform(sana['AbstractL'])\n",
    "print(X_tfidf.shape)\n",
    "print(tfidf_vect.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fa264dff",
   "metadata": {},
   "outputs": [
    {
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       "193  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...     0.0   \n",
       "194  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...     0.0   \n",
       "195  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...     0.0   \n",
       "196  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...     0.0   \n",
       "197  0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...     0.0   \n",
       "\n",
       "     zana  zaydis  zaynab  zbek  zeal  zero  ziya   zo  zul  \n",
       "0     0.0     0.0     0.0   0.0   0.0   0.0   0.0  0.0  0.0  \n",
       "1     0.0     0.0     0.0   0.0   0.0   0.0   0.0  0.0  0.0  \n",
       "2     0.0     0.0     0.0   0.0   0.0   0.0   0.0  0.0  0.0  \n",
       "3     0.0     0.0     0.0   0.0   0.0   0.0   0.0  0.0  0.0  \n",
       "4     0.0     0.0     0.0   0.0   0.0   0.0   0.0  0.0  0.0  \n",
       "..    ...     ...     ...   ...   ...   ...   ...  ...  ...  \n",
       "193   0.0     0.0     0.0   0.0   0.0   0.0   0.0  0.0  0.0  \n",
       "194   0.0     0.0     0.0   0.0   0.0   0.0   0.0  0.0  0.0  \n",
       "195   0.0     0.0     0.0   0.0   0.0   0.0   0.0  0.0  0.0  \n",
       "196   0.0     0.0     0.0   0.0   0.0   0.0   0.0  0.0  0.0  \n",
       "197   0.0     0.0     0.0   0.0   0.0   0.0   0.0  0.0  0.0  \n",
       "\n",
       "[198 rows x 4974 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_tfidf_df = pd.DataFrame(X_tfidf.toarray())\n",
    "X_tfidf_df.columns = tfidf_vect.get_feature_names()\n",
    "X_tfidf_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3db171a3",
   "metadata": {},
   "source": [
    "### Feature Engineering: Feature Creation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "499fc2a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      649\n",
       "1      423\n",
       "2      524\n",
       "3      782\n",
       "4      881\n",
       "      ... \n",
       "193    694\n",
       "194    629\n",
       "195    374\n",
       "196    304\n",
       "197    560\n",
       "Name: AbstractL_len, Length: 198, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sanaC = sana[['TitleL','AbstractL']]\n",
    "sana['AbstractL_len'] = sana['AbstractL'].apply(lambda x : len(x) - x.count(\" \"))\n",
    "sana['AbstractL_len']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "93b13343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       89\n",
       "1      145\n",
       "2      196\n",
       "3      113\n",
       "4      157\n",
       "      ... \n",
       "193    104\n",
       "194    119\n",
       "195    139\n",
       "196    119\n",
       "197     84\n",
       "Name: TitleL_len, Length: 198, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sana['TitleL_len'] = sana['TitleL'].apply(lambda x : len(x) - x.count(\" \"))\n",
    "sana['TitleL_len']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14341918",
   "metadata": {},
   "source": [
    "#### Evaluate the created features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "44e204d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot \n",
    "import numpy as np\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "216b54ff",
   "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": [
    "bins = np.linspace(0,200,40)\n",
    "pyplot.hist(sana[sana['Category']=='1']['AbstractL_len'], bins, alpha = 0.5, label = '1')\n",
    "pyplot.hist(sana[sana['Category']=='0']['AbstractL_len'], bins, alpha = 0.5, label = '0')\n",
    "pyplot.legend(loc='upper left')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "eef942c9",
   "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": [
    "bins = np.linspace (0,700,40)\n",
    "pyplot.hist(sana['AbstractL_len'],bins)\n",
    "pyplot.title(\"Abstract Length Distribution\")\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "38e7109e",
   "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": [
    "bins = np.linspace (0,650,40)\n",
    "pyplot.hist(sana['TitleL_len'],bins)\n",
    "pyplot.title(\"Title Length Distribution\")\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7457a0b8",
   "metadata": {},
   "source": [
    "### Machine Learning Classifiers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b88950da",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import KFold, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "1722d3da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.   , 0.425, 1.   , 1.   , 1.   ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create the vectore form \n",
    "tdfidf_vect = TfidfVectorizer()\n",
    "X_tfidf = tfidf_vect.fit_transform(sana['AbstractL'])\n",
    "\n",
    "X_features = pd.concat([sana['AbstractL_len'], pd.DataFrame(X_tfidf.toarray())], axis=1)\n",
    "X_features.head()\n",
    "\n",
    "#Apply the RDF Classifier \n",
    "rf = RandomForestClassifier(n_jobs=-1)\n",
    "k_fold = KFold(n_splits=5)\n",
    "\n",
    "cross_val_score(rf,X_features, sana['Category'], cv=k_fold, scoring='accuracy', n_jobs=-1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "c04cfb92",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_fscore_support as score\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "445f4121",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda 2\\envs\\SANA\\lib\\site-packages\\sklearn\\utils\\validation.py:1679: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  FutureWarning,\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_features, sana['Category'], test_size=0.2)\n",
    "rf = RandomForestClassifier(n_estimators=50, max_depth=20, n_jobs=-1)\n",
    "rf_model = rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e0249091",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.023796336136017546, 4008),\n",
       " (0.016041445706263324, 1120),\n",
       " (0.014246313775468563, 3354),\n",
       " (0.013157679876070565, 3259),\n",
       " (0.012265096191958815, 4099),\n",
       " (0.01203104596700971, 4757),\n",
       " (0.010746099477402888, 2422),\n",
       " (0.0101163110449086, 455),\n",
       " (0.009355272915003432, 3357),\n",
       " (0.008845630639430087, 791)]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(zip(rf_model.feature_importances_,X_train.columns), reverse= True)[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8d817afe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda 2\\envs\\SANA\\lib\\site-packages\\sklearn\\utils\\validation.py:1679: FutureWarning: Feature names only support names that are all strings. Got feature names with dtypes: ['int', 'str']. An error will be raised in 1.2.\n",
      "  FutureWarning,\n"
     ]
    }
   ],
   "source": [
    "y_pred = rf_model.predict(X_test)\n",
    "precision,recall,fscore,support=score(y_test,y_pred,pos_label = 1, average='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "94fc7e68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision:1.0/Recall:0.182/Accuracy:0.775\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "print('Precision:{}/Recall:{}/Accuracy:{}'.format(round(precision,3), round(recall,3), round((y_pred==y_test).sum() / len(y_pred),3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "596617db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "#Implement TF-IDF for binary model\n",
    "tfidf = TfidfVectorizer(max_features=1000, ngram_range=(1,7), analyzer='char')\n",
    "X = tfidf.fit_transform(sana['AbstractL'])\n",
    "Y = sana['Category']\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=1) #Split into 80/20 train and test sets\n",
    "\n",
    "#Run binary logistic regression\n",
    "LR = linear_model.LogisticRegression(solver='lbfgs',max_iter=1000)\n",
    "LR.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7a053c68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF-IDF Abstract\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Binary Logistic Model Score for TF-IDF: 55.0 % \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda 2\\envs\\SANA\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1308: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yellowbrick.target import ClassBalance\n",
    "import matplotlib.pyplot as plt\n",
    "#Class Balance Visualization\n",
    "CB = ClassBalance()\n",
    "CB.fit(Y_train, Y_test)\n",
    "print('TF-IDF Abstract\\n')\n",
    "plt.show()\n",
    "from yellowbrick.classifier import ClassificationReport\n",
    "#Classification Report and overall score for Logistics Regression\n",
    "LRScore = round((LR.score(X_test, Y_test))*100,2)\n",
    "print('Binary Logistic Model Score for TF-IDF:',LRScore,'%','\\n')\n",
    "LRC = ClassificationReport(LR, support=False)\n",
    "LRC.fit(X_train, Y_train)      \n",
    "LRC.score(X_test, Y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b69852db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(max_iter=1000)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "#Implement TF-IDF for binary model\n",
    "tfidf = TfidfVectorizer(max_features=1000, ngram_range=(1,7), analyzer='char')\n",
    "X = tfidf.fit_transform(sana['TitleL'])\n",
    "Y = sana['Category']\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=1) #Split into 80/20 train and test sets\n",
    "\n",
    "#Run binary logistic regression\n",
    "LR = linear_model.LogisticRegression(solver='lbfgs',max_iter=1000)\n",
    "LR.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fcb165bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TF-IDF Title\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Title Logistic Model Score for TF-IDF: 67.5 % \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from yellowbrick.target import ClassBalance\n",
    "import matplotlib.pyplot as plt\n",
    "#Class Balance Visualization\n",
    "CB = ClassBalance()\n",
    "CB.fit(Y_train, Y_test)\n",
    "print('TF-IDF Title\\n')\n",
    "plt.show()\n",
    "from yellowbrick.classifier import ClassificationReport\n",
    "#Classification Report and overall score for Logistics Regression\n",
    "LRScore = round((LR.score(X_test, Y_test))*100,2)\n",
    "print('Title Logistic Model Score for TF-IDF:',LRScore,'%','\\n')\n",
    "LRC = ClassificationReport(LR, support=False)\n",
    "LRC.fit(X_train, Y_train)      \n",
    "LRC.score(X_test, Y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e119690",
   "metadata": {},
   "source": [
    "#### Doc2Vec Approach "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "2ae68320",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a function to tokenize all columns \n",
    "sana[\"AbstractToken\"] = sana.apply(lambda row: word_tokenize(row[\"Abstract\"]), axis=1)\n",
    "sana[\"TitleToken\"] = sana.apply(lambda row: word_tokenize(row[\"Title\"]), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "80685d5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      [world, becomes, increasingly, globalised, isl...\n",
       "1      [present, paper, attempt, explore, impact, kar...\n",
       "2      [comparative, philosophy, like, types, philoso...\n",
       "3      [critical, assessment, social, changes, impact...\n",
       "4      [shia, muslims, uk, whether, individually, gro...\n",
       "                             ...                        \n",
       "193    [let, us, start, thought, experiment, five, hu...\n",
       "194    [utilizing, treatments, uncertainty, regarding...\n",
       "195    [essay, traces, use, term, qizilb, sh, select,...\n",
       "196    [first, decade, sixteeth, century, common, era...\n",
       "197    [article, examines, aspects, reception, french...\n",
       "Name: AbstractToken, Length: 198, dtype: object"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sananew = sana['AbstractToken'].transpose()\n",
    "sananew"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "c8f7850b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      1\n",
       "2      1\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "193    0\n",
       "194    0\n",
       "195    0\n",
       "196    0\n",
       "197    0\n",
       "Name: Category, Length: 198, dtype: int64"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sananew1=sana['Category'].transpose()\n",
    "sananew1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "27bb4c3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sana0=sana.loc[sana['Category']==0]\n",
    "sana1=sana.loc[sana['Category']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "90cbf230",
   "metadata": {},
   "outputs": [],
   "source": [
    "tagged_train = [gensim.models.doc2vec.TaggedDocument(v, [i])\n",
    "                     for i, v in enumerate(sananew)]\n",
    "tagged_test = [gensim.models.doc2vec.TaggedDocument(v, [i])\n",
    "                    for i, v in enumerate(sananew)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "309d4172",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc = gensim.models.Doc2Vec(tagged_train, vector_size = 100, window = 5, min_count = 2 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "734ee58b",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "eval() arg 1 must be a string, bytes or code object",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\NABIHA~1\\AppData\\Local\\Temp/ipykernel_2680/4204237505.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain_vectors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdoc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfer_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwords\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtagged_train\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mtest_vectors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdoc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfer_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwords\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtagged_test\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\NABIHA~1\\AppData\\Local\\Temp/ipykernel_2680/4204237505.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mtrain_vectors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdoc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfer_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwords\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtagged_train\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mtest_vectors\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdoc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minfer_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwords\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mtagged_test\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: eval() arg 1 must be a string, bytes or code object"
     ]
    }
   ],
   "source": [
    "train_vectors = [doc.infer_vector(eval(v.words)) for v in tagged_train]\n",
    "test_vectors = [doc.infer_vector(eval(v.words)) for v in tagged_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "10c25f6d",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'words'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\NABIHA~1\\AppData\\Local\\Temp/ipykernel_2680/1134764864.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;31m#Build BOW Doc2Vec model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[0mDBOWModel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mDoc2Vec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvector_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 22\u001b[1;33m \u001b[0mDBOWModel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild_vocab\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mAllData\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;31m#Iterate over data to train model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda 2\\envs\\SANA\\lib\\site-packages\\gensim\\models\\doc2vec.py\u001b[0m in \u001b[0;36mbuild_vocab\u001b[1;34m(self, corpus_iterable, corpus_file, update, progress_per, keep_raw_vocab, trim_rule, **kwargs)\u001b[0m\n\u001b[0;32m    878\u001b[0m         total_words, corpus_count = self.scan_vocab(\n\u001b[0;32m    879\u001b[0m             \u001b[0mcorpus_iterable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcorpus_iterable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcorpus_file\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcorpus_file\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m             \u001b[0mprogress_per\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mprogress_per\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrim_rule\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrim_rule\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    881\u001b[0m         )\n\u001b[0;32m    882\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcorpus_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcorpus_count\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda 2\\envs\\SANA\\lib\\site-packages\\gensim\\models\\doc2vec.py\u001b[0m in \u001b[0;36mscan_vocab\u001b[1;34m(self, corpus_iterable, corpus_file, progress_per, trim_rule)\u001b[0m\n\u001b[0;32m   1046\u001b[0m             \u001b[0mcorpus_iterable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTaggedLineDocument\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcorpus_file\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1047\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1048\u001b[1;33m         \u001b[0mtotal_words\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcorpus_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_scan_vocab\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcorpus_iterable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprogress_per\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrim_rule\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1049\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1050\u001b[0m         logger.info(\n",
      "\u001b[1;32mD:\\Anaconda 2\\envs\\SANA\\lib\\site-packages\\gensim\\models\\doc2vec.py\u001b[0m in \u001b[0;36m_scan_vocab\u001b[1;34m(self, corpus_iterable, progress_per, trim_rule)\u001b[0m\n\u001b[0;32m    950\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mdocument_no\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdocument\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcorpus_iterable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    951\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mchecked_string_types\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 952\u001b[1;33m                 \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdocument\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwords\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    953\u001b[0m                     logger.warning(\n\u001b[0;32m    954\u001b[0m                         \u001b[1;34m\"Each 'words' should be a list of words (usually unicode strings). \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'str' object has no attribute 'words'"
     ]
    }
   ],
   "source": [
    "from gensim.models.doc2vec import TaggedDocument\n",
    "from gensim.models import Doc2Vec\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "X = sana['AbstractL']\n",
    "Y = sana['Category']\n",
    "#Function to label reviews train or test\n",
    "def label_reviews(review, label_type):\n",
    "    labeled = []\n",
    "    for i,v in enumerate(review):\n",
    "        label = label_type + '_' + str(i)\n",
    "        labeled.append(TaggedDocument(v, [label]))\n",
    "    return labeled\n",
    "\n",
    "#Label training and test sets using function\n",
    "X_train = label_reviews(X_train, 'Train')\n",
    "X_test = label_reviews(X_test,'Test')\n",
    "AllData = X_train + X_test\n",
    "\n",
    "#Build BOW Doc2Vec model\n",
    "DBOWModel = Doc2Vec(dm=0, vector_size=100)\n",
    "DBOWModel.build_vocab(str([x for x in AllData]))\n",
    "\n",
    "#Iterate over data to train model\n",
    "for epoch in range(100):\n",
    "    DBOWModel.train(utils.shuffle([x for x in AllData]), total_examples=len(AllData), epochs=1)\n",
    "    DBOWModel.alpha -= 0.002\n",
    "    DBOWModel.min_alpha = DBOWModel.alpha\n",
    "    \n",
    "#Create function to vectorize all reviews\n",
    "def get_vectors(model, corpus_size, vectors_size, vectors_type):\n",
    "    vectors = np.zeros((corpus_size, vectors_size))\n",
    "    for i in range(0, corpus_size):\n",
    "        prefix = vectors_type + '_' + str(i)\n",
    "        vectors[i] = model.docvecs[prefix]\n",
    "    return vectors\n",
    "\n",
    "#Vectorize training and testing data\n",
    "train_vectors_dbow = get_vectors(DBOWModel, len(X_train), 300, 'Train')\n",
    "test_vectors_dbow = get_vectors(DBOWModel, len(X_test), 300, 'Test') \n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "rf_model = rf.fit(train_vectors_dbow, y_train)\n",
    "y_pred=rf_model.predict(test_vectors_dbow)\n",
    "precision=precision_score(y_test,y_pred)\n",
    "recall = recall_score(y_test,y_pred)\n",
    "print('Precision: {} / Recall: {} / Accuracy: {}'.format(\n",
    "    round(precision, 3), round(recall, 3), round((y_pred==y_test['label']).sum()/len(y_pred), 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54ccbfda",
   "metadata": {},
   "source": [
    "#### Recurrent Neural Networks "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "e9b96c16",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.text import Tokenizer \n",
    "from keras.preprocessing.sequence import pad_sequences "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "c8e9fe4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Fit the tokenize \n",
    "tokenizer = Tokenizer()\n",
    "tokenizer.fit_on_texts(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c5cc8bf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train and Test split \n",
    "X_train_seq = tokenizer.texts_to_sequences(X_train)\n",
    "X_test_seq = tokenizer.texts_to_sequences(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b1bba5d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_seq_padded = pad_sequences(X_train_seq, 200)\n",
    "X_test_seq_padded = pad_sequences(X_test_seq, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "713c4855",
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras.backend as K\n",
    "from keras.layers import Dense, Embedding, LSTM\n",
    "from keras.models import Sequential\n",
    "\n",
    "def recall_m(y_true, y_pred):\n",
    "        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n",
    "        recall = true_positives / (possible_positives + K.epsilon())\n",
    "        return recall\n",
    "\n",
    "def precision_m(y_true, y_pred):\n",
    "        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n",
    "        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n",
    "        precision = true_positives / (predicted_positives + K.epsilon())\n",
    "        return precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "066882f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, None, 100)         469100    \n",
      "                                                                 \n",
      " lstm (LSTM)                 (None, 100)               80400     \n",
      "                                                                 \n",
      " dense (Dense)               (None, 100)               10100     \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 101       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 559,701\n",
      "Trainable params: 559,701\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#Creating the model\n",
    "#dropout helps with overfitting \n",
    "model = Sequential()\n",
    "model.add(Embedding(len(tokenizer.index_word)+1, 100))\n",
    "model.add(LSTM(100,dropout=0, recurrent_dropout=0))\n",
    "model.add(Dense(100, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "1e96bf86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy', precision_m, recall_m])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7ec828e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "2/2 [==============================] - 0s 201ms/step - loss: 3.3937e-04 - accuracy: 1.0000 - precision_m: 1.0000 - recall_m: 1.0000 - val_loss: 0.5542 - val_accuracy: 0.8600 - val_precision_m: 0.9167 - val_recall_m: 0.6471\n",
      "Epoch 2/10\n",
      "2/2 [==============================] - 0s 187ms/step - loss: 2.1170e-04 - accuracy: 1.0000 - precision_m: 1.0000 - recall_m: 1.0000 - val_loss: 0.6568 - val_accuracy: 0.8600 - val_precision_m: 0.9167 - val_recall_m: 0.6471\n",
      "Epoch 3/10\n",
      "2/2 [==============================] - 0s 243ms/step - loss: 1.4872e-04 - accuracy: 1.0000 - precision_m: 1.0000 - recall_m: 1.0000 - val_loss: 0.7160 - val_accuracy: 0.8600 - val_precision_m: 0.9167 - val_recall_m: 0.6471\n",
      "Epoch 4/10\n",
      "2/2 [==============================] - 0s 206ms/step - loss: 1.1286e-04 - accuracy: 1.0000 - precision_m: 1.0000 - recall_m: 1.0000 - val_loss: 0.7539 - val_accuracy: 0.8600 - val_precision_m: 0.9167 - val_recall_m: 0.6471\n",
      "Epoch 5/10\n",
      "2/2 [==============================] - 1s 206ms/step - loss: 9.1277e-05 - accuracy: 1.0000 - precision_m: 1.0000 - recall_m: 1.0000 - val_loss: 0.7852 - val_accuracy: 0.8800 - val_precision_m: 1.0000 - val_recall_m: 0.6471\n",
      "Epoch 6/10\n",
      "2/2 [==============================] - 0s 196ms/step - loss: 7.7355e-05 - accuracy: 1.0000 - precision_m: 1.0000 - recall_m: 1.0000 - val_loss: 0.8202 - val_accuracy: 0.8600 - val_precision_m: 1.0000 - val_recall_m: 0.5882\n",
      "Epoch 7/10\n",
      "2/2 [==============================] - 0s 194ms/step - loss: 6.7829e-05 - accuracy: 1.0000 - precision_m: 1.0000 - recall_m: 1.0000 - val_loss: 0.8545 - val_accuracy: 0.8400 - val_precision_m: 1.0000 - val_recall_m: 0.5294\n",
      "Epoch 8/10\n",
      "2/2 [==============================] - 0s 187ms/step - loss: 6.1404e-05 - accuracy: 1.0000 - precision_m: 1.0000 - recall_m: 1.0000 - val_loss: 0.8855 - val_accuracy: 0.8400 - val_precision_m: 1.0000 - val_recall_m: 0.5294\n",
      "Epoch 9/10\n",
      "2/2 [==============================] - 0s 190ms/step - loss: 5.6763e-05 - accuracy: 1.0000 - precision_m: 1.0000 - recall_m: 1.0000 - val_loss: 0.9106 - val_accuracy: 0.8400 - val_precision_m: 1.0000 - val_recall_m: 0.5294\n",
      "Epoch 10/10\n",
      "2/2 [==============================] - 0s 205ms/step - loss: 0.0564 - accuracy: 0.9932 - precision_m: 0.9667 - recall_m: 1.0000 - val_loss: 0.9171 - val_accuracy: 0.8400 - val_precision_m: 1.0000 - val_recall_m: 0.5294\n"
     ]
    }
   ],
   "source": [
    "# Fit the RNN model\n",
    "history = model.fit(X_train_seq_padded, y_train, \n",
    "                    batch_size=100, epochs=10,\n",
    "                    validation_data=(X_test_seq_padded, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5a7c3429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the evaluation metrics by each epoch for the model to see if we are over or underfitting\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "for i in ['accuracy', 'precision_m', 'recall_m']:\n",
    "    acc = history.history[i]\n",
    "    val_acc = history.history['val_{}'.format(i)]\n",
    "    epochs = range(1, len(acc) + 1)\n",
    "\n",
    "    plt.figure()\n",
    "    plt.plot(epochs, acc, label='Training Accuracy')\n",
    "    plt.plot(epochs, val_acc, label='Validation Accuracy')\n",
    "    plt.title('Results for {}'.format(i))\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "327aa70b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}