Topic_model_IQ_lab.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Topic model IQ lab.ipynb",
      "provenance": [],
      "machine_shape": "hm",
      "mount_file_id": "1dplabnpryB9XP8PxSF3YEDyNMAjfAjn0",
      "authorship_tag": "ABX9TyPjncB7C8wceWnqPbiC+7gH",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jonasdreyoe/IQLab/blob/master/Topic_model_IQ_lab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l-GlNm90NvGF",
        "colab_type": "text"
      },
      "source": [
        "# Creating dataframe"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IP8wxzo_rgGp",
        "colab_type": "text"
      },
      "source": [
        "## Loading data into one frame"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4ZcOIyAUOIng",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FB6bKY2kO2oy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_excel('/content/STEP_8_Analysis.xlsx')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zp0nQsnLhe_b",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Is9MqC5MPHxC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hN89NutWQceI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install PyPDF2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eDHBZhLGVBZE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install pdfminer.six"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d2vpf2wsQlGR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from PyPDF2 import PdfFileReader\n",
        "import os\n",
        "\n",
        "# Get all PDF filenames from a folder 'pdfpath'.\n",
        "pdfpath = '/content/Publications2/'\n",
        "# Generate list pdfFiles containing all pdf file paths\n",
        "pdfFiles = []\n",
        "# Generate list pdfNames containing all pdf names\n",
        "pdfNames = []\n",
        "for filename in os.listdir(pdfpath):\n",
        "  if filename.endswith('.pdf'):\n",
        "    filepath = pdfpath + filename\n",
        "    pdfFiles.append(filepath)\n",
        "    pdfFiles.sort(key=str.lower)\n",
        "    pdfNames.append(filename)\n",
        "    pdfNames.sort(key=str.lower)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YaqwT3BaQqAU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(len(pdfFiles))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vUKJi3PpS3h2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# initialize list of lists \n",
        "pdfdata = [] \n",
        "  \n",
        "# Create the pandas DataFrame \n",
        "df_pdf = pd.DataFrame(pdfdata, columns = ['Filename', 'Page', 'Text']) \n",
        "  \n",
        "# print dataframe. \n",
        "df_pdf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rx6eDjqBUVOj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Extract pdf texts to dataframe: df_pdf\n",
        "from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\n",
        "from pdfminer.pdfpage import PDFPage\n",
        "from pdfminer.converter import XMLConverter, HTMLConverter, TextConverter\n",
        "from pdfminer.layout import LAParams\n",
        "from pdfminer.pdfdocument import PDFDocument\n",
        "from pdfminer.pdfparser import PDFParser\n",
        "import io\n",
        "import os\n",
        "\n",
        "numberofpdfs = len(pdfFiles)\n",
        "for a in range(numberofpdfs):\n",
        "  filename = pdfNames[a]\n",
        "  \n",
        "  pdf_document = pdfFiles[a]\n",
        "  with open(pdf_document, \"rb\") as filehandle:\n",
        "    pdf = PdfFileReader(filehandle)\n",
        "    info = pdf.getDocumentInfo()\n",
        "    pages = pdf.getNumPages()\n",
        "\n",
        "\n",
        "    fp = open(pdf_document, 'rb')\n",
        "    rsrcmgr = PDFResourceManager()\n",
        "    retstr = io.StringIO()\n",
        "    print(type(retstr))\n",
        "    codec = 'utf-8'\n",
        "    laparams = LAParams()\n",
        "    device = TextConverter(rsrcmgr, retstr, codec=codec, laparams=laparams)\n",
        "    interpreter = PDFPageInterpreter(rsrcmgr, device)\n",
        "    \n",
        "    page_no = 0\n",
        "    for pageNumber, page in enumerate(PDFPage.get_pages(fp)):\n",
        "        if pageNumber == page_no:\n",
        "            interpreter.process_page(page)\n",
        "\n",
        "            data = retstr.getvalue()\n",
        "            df_pdf.loc[len(df_pdf)] = [filename, page_no+1, str(data)]\n",
        "\n",
        "            data = ''\n",
        "            retstr.truncate(0)\n",
        "            retstr.seek(0)\n",
        "\n",
        "        page_no += 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHvqFV_8Zu13",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_pdf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vi1DMnHjZ6st",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_pdf['cleantext'] = df_pdf['Text'].str.replace(r'\\s+|\\\\n', ' ', regex=True)\n",
        "df_pdf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V5sF5eFvblrT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_pdf_test = df_pdf\n",
        "df_pdf_ft = df_pdf_test.groupby('Filename')['cleantext'].apply(lambda x: \"%s\" % ' '.join(x))\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fhDXRtaQclsE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_pdf_ft = pd.DataFrame(df_pdf_ft)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a2oV1W-veerR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_pdf_ft = df_pdf_ft.reset_index()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uPA9QUqetpL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_pdf_ft.columns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AgD2owyWc64E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(df_pdf_ft['cleantext'][0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cqqY8-4ZeYVR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import re"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xxjAUokogNuB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_pdf_ft_1 = df_pdf_ft"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kdkk8UXUdyF4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for a in range(len(df_pdf_ft_1)):\n",
        "  df_pdf_ft_1['Filename'][a] = re.sub(r'.pdf', '', df_pdf_ft_1['Filename'][a])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MrALnBeBfhly",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_pdf_ft_1['Filename'].str.replace(r'.pdf', '')\n",
        "df_pdf_ft_1.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L1rf40SRhsUz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_pdf_ft['IQLab ID'] = df_pdf_ft['Filename']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DC1yF8uWhCcF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_merged = df.merge(df_pdf_ft, how='left', on='IQLab ID')\n",
        "df_merged.head()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T68zJWbGh_q8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_merged.info()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D4GCqsjYiP4z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.to_csv('/content/IQlab_fulltexts.tsv', sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dTX8CqaDracD",
        "colab_type": "text"
      },
      "source": [
        "## Cleaning texts"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ajn2YYBGrloq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv('/content/IQlab_fulltexts.tsv', sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7nNp-Edvr4SX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.pop('Unnamed: 0')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DsDaiOjtrs8C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tach4khWj-YD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.cleantext = df.cleantext.replace(r'\\n',' ', regex=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uRCroqFBtEtp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import re"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "apYSi8Me7TPu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.cleantext = df.cleantext.str.replace(r'\\b\\-\\s\\b', '')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EuIt-6MOmGFJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.cleantext = df.cleantext.str.replace(r'(\\(cid\\:[0-9]+\\))', '')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z3Wyd5rfw43z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.cleantext = df.cleantext.str.replace(r'\\s\\s+', ' ')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OU4sg39yx77W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.cleantext = df.cleantext.str.strip()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Axs7slyZxM0P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_ft = df.dropna(subset=['cleantext'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wC5pccAExQWL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pd.options.display.max_rows = 999\n",
        "df_ft.cleantext"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_zsrW6R2Ser",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pd.options.display.max_rows = 10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGsx6KBLzXYn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4bd8eafe-4205-447a-d079-83ca357bd385"
      },
      "source": [
        "df.cleantext[104]"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "''"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3Wu58HN0BCX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7b9f37ab-549c-429b-e156-274d1162680c"
      },
      "source": [
        "df.loc[104, 'IQLab ID']"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'A11571'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zAbJzyoE0Cck",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "6ab9468d-abc5-448b-f9f2-bf85f8a85a3b"
      },
      "source": [
        "df.cleantext[370]"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "'!\"## $%&#’ %& ()! !%&( )! %& % !% $%*+ ,#%(-./ ! \" #$ \" % # &&’ ( $) && 0./,%1/2 !. %,/1’# +#.1,0#. /!# +#*#’)3 #&/ %&+ %33’1%/)& )4 % /\")5/#, -’/3’# 1!)1# +%(5 &)./1 &./,- #&/ /) %..#.. !(! .1!))’ ./-+#&/.6 -&+#,./%&+&( )4 &),(%&1 1!# ./,7 8-%’/%/*# %&%’7.. !# +#*#’)3 #&/ )4 /!# +%(&)./1 &./,- #&/ \"%. 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E !% ; !# #44#1/ )4 )+=#+ ’%0),%/),7 &./,-1/)& )& ./-+#&/.6 %1!#*# #&/ & 1!# ./,7 3,%1/1%’. #.#%,1! ,#3),/ &.//-/# )4 +-1%/)& &(%3),# %,,.)& E ,#%(-./ $ D)+#’’&( & .1#&1# ’#..)&.2 ,# /!#,# 0#//#, \"%7. /) ’#%,& \"/! )+#’.O 1!))’ 1#&1# %&+ D%/!# %/1. %.’% E ,#%(-./ $ ; $%(&).&( .#1)&+%,7 ./-+#&/. .1)&1#3/)&. )4 3!)/).7&/!#.. %&+ ,#.3,%/)& & 3’%&/. -.&( % /\")5/#, -’/3’# 1!)1# &./,- #&/ )-,&%’ )4 C)’)(1%’ +-1%/)& )*%9 $ < )&1#3/ %33&(2 /))’ 4), 3,)*&( .1#&1# /#%1!&( %&+ ’#%,&&( & ,#%(-./ $ $-/ E ,%.#, C +. 3,)*&( /#%1!&( %&+ ’#%,&&( & .1#&1# %&+ %/!# %/1. 33 #\" R),92 #%1!#,. )’’#(# ,#.. )*19 E -..0%- ; -&), !(! .1!))’ 3-3’.6 -&+#,./%&+&( )4 /!# 3%,/5 1-’%/# &%/-,# )4 %//#,2 & &/#,*#\" ./-+7 1#&1# +-1%/)& < ; +) E C%,,)\" : $#*#’)3 #&/ %&+ %33’1%/)& )4 % /\")5/#, +%(&)./1 /#./ #%.-,&( 1)’’#(# 0)’)(7 ./-+#&/.6 -&+#,./%&+&( )4 +44-.)& %&+ ). ).. %4/#, % 1)-,.# )4 &./,-1/)& )-,&%’ )4 #.#%,1! & 1#&1# #%1!&( :< %’ #, $ >3’),&( /!# ’&9 0#/\"##& ./-+#&/.6 .1#&/=1 %&+ &)&.1#&/=1 1)&1#3/)&. 1#&1# +-1%/)& <<: #/#,.)& E ,#%(-./ $ ,%+#5 ./-+#&/.6 .1)&1#3/)&. )4 1)*%’#&/ 0)&+&( %&+ ./,-1/-,# )-,&%’ )4 !# 1%’ +-1%/)& << :< #/#,.)& ,#%(-./ $ E %,&#// $#*#’)3 #&/ %&+ %33’1%/)& )4 % +%(&)./1 &./,- #&/ /) #*%’-%/# ,%+#5 %&+ 5 ./-+#&/.6 1)&1#3/. )4 1)*%’#&/ 0)&+&( %&+ ./,-1/-,# 4)’’)\"&( % 1)-,.# )4 &./,-1/)& )-,&%’ )4 #.#%,1! & 1#&1# #%1!&( < $D G$ &/,1! 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C E D-&07 )&1#3/ %33&( %&+ .1)&1#3/)&.2 ./-+7 )4 !(!5 .1!))’ ./-+#&/.6 -&+#,./%&+&(. )4 %1+. %&+ 0%.#. &/#,&%/)&%’ )-,&%’ )4 1#&1# +-1%/)& 4), ?&+)\". #’#%.# M) 3-/#, )4/\"%,#N !1%()2 %0#, +#%. %0)-/ )&.%/)& #&#,(72 +%(&)./1 &./,- #&/ 1!))’ 1#&1# #*#\" ; %& $ $#*#’)3 #&/ %&+ %33’1%/)& )4 % +%(&)./1 &./,- #&/ /) #*%’-%/# .#1)&+%,7 ./-+#&/.6 1)&1#3/)&. )4 8-%’/%/*# %&%’7.. &3-0’.!#+ !$ /!#.. -,/& &*#,./7 )4 #1!&)’)(7 %& $ E )! $..)’*# ), ,#%1/O -./,%’%& 1#&1# #%1!#,.6 )-,&%’ : < %& $ E ,#%(-./ $ *%’-%/&( ./-+#&/.6 -&+#,./%&+&( )4 1!# 1%’ 0)&+&( 1!))’ 1#&1# #*#\" ;: %.9#, E ,#70#,( : %1&( /!# . %/1!#. & /!# 1’%..,)) & .0),&# E ,#70#,( +. #%,&&( & .1#&1#2 !# 3’1%/)&. )4 1!’+,#&6. .1#&1# 33 << -19’%&+ )&+)&2 #&# %&& ,#%(-./ $ !# +#*#’)3 #&/ %&+ -.# )4 +%(&)./1 &./,- #&/. /) #*%’-%/# ./-+#&/.6 .1)&1#3/)&. & .1#&1# &/#,&%/)&%’ )-,&%’ )4 1#&1# +-1%/)& :< ,#%(-./ $ : $%(&)./1 %..#.. #&/ )4 ./-+#&/.6 .1#&1# 9&)\"’#+(# & ’7&& D E $-/ +. #%,&&( .1#&1# & /!# .1!))’.2 #.#%,1! ,#4), &( 3,%1/1# 33 ; < D%!\"%! 2 ,’0%- .) D 44#1/. )4 +44#,#&/ &./,-1/)&%’ #/!)+. )& &/#,3,#/%/)& .9’’. & 1!# 1%’ 8-%’/%/*# %&%’7.. &3-0’.!#+ D+ /!#.. %/)&%’ &*#,./7 )4 &(%3),# 7.)& ,#%(-./ $ E C-1%/ C !# 1) 3’#>/7 )4 /#%1!&( %&+ ’#%,&&( 1!# 1%’ #8-’0,- )-,&%’ )4 !# 1%’ +-1%/)& ;< :::: &*#,./7 )4 % 0,+(# )1%’ >% &%/)&. 7&+1%/# : !# ./,72 #3),/ )& /!# )*# 0#, #>% &%/)&. % 0,+(#2 -/!), &*#,./7 )4 % 0,+(# )1%’ >% &%/)&. 7&+1%/# !# ./,72 #3),/ )& /!# )*# 0#, ; #>% &%/)&. % 0,+(#2 -/!), %& $,#’ +# ). ? E #,’))3 &/,)+-1&( +7&% 1 #8-’0,- %. %& #>3’%&%/),7 )+#’ )-,&%’ )4 !# 1%’ +-1%/)& ;< :::< )’#/ D)+#’’&( %&+ 1)%1!&( )4 ,#’#*%&/ #/%1)(&/*# ./,%/#(#. 4), #&!%&1&( -&*#,./7 ./-+#&/.6 ’#%,&&( #%,&&( %&+ &./,-1/)& < ).9% ? E #99&#& ? +#&/=1%/)& %&+ %&%’7.. )4 ./-+#&/ 1)&1#3/)&. -.#+ /) .)’*# 1!# 1%’ #8-’0,- 3,)0’# . )-,&%’ )4 #.#%,1! & 1#&1# #%1!&( ; < ;< ?))’&)-(! C E ’’.)3 : ,%1/1%’ \"),9 & .1#&1# % 0,+(#2 % 0,+(# &*#,./7 ,#..'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCb1hOUJ02ln",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f996a53b-5607-44ba-ea11-b91dd70cadc7"
      },
      "source": [
        "df.loc[370, 'IQLab ID']"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'A46119'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "ttQ50Uzy0KeN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "outputId": "3749168b-70af-4594-e783-af9e71584607"
      },
      "source": [
        "df_ft = df.drop([104, 370])\n",
        "df_ft = df_ft.dropna(subset=['cleantext'])\n",
        "df_ft"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>IQLab ID</th>\n",
              "      <th>ERIC/ISI ID</th>\n",
              "      <th>Title</th>\n",
              "      <th>Journal</th>\n",
              "      <th>Abstract</th>\n",
              "      <th>Link</th>\n",
              "      <th>Responsible</th>\n",
              "      <th>Pub. downloaded?</th>\n",
              "      <th>Unnamed: 8</th>\n",
              "      <th>Unnamed: 9</th>\n",
              "      <th>cleantext</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A21167</td>\n",
              "      <td>RN10237</td>\n",
              "      <td>A Collaborative; Investigative Recombinant Dna...</td>\n",
              "      <td>Biochemistry And Molecular Biology Education</td>\n",
              "      <td>A recombinant DNA technology course was design...</td>\n",
              "      <td>https://eric.ed.gov/?q=title%3a%22A+Collaborat...</td>\n",
              "      <td>Frederik</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>© 2002 by The International Union of Biochemis...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A20656</td>\n",
              "      <td>RN7375</td>\n",
              "      <td>A Laboratory Preparation Of Aspartame Analogs ...</td>\n",
              "      <td>Journal Of Chemical Education</td>\n",
              "      <td>This laboratory experiment provides a unique o...</td>\n",
              "      <td>https://eric.ed.gov/?q=title%3a%22A+Laboratory...</td>\n",
              "      <td>Frederik</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>In the Laboratory A Laboratory Preparation of ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A20227</td>\n",
              "      <td>RN1445</td>\n",
              "      <td>A Practical Guide To Developing Virtual And Au...</td>\n",
              "      <td>Biochemistry And Molecular Biology Education</td>\n",
              "      <td>Although virtual and augmented reality (VR and...</td>\n",
              "      <td>https://eric.ed.gov/?q=title%3a%22A+Practical+...</td>\n",
              "      <td>Frederik</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Article A Practical Guide to Developing Virtua...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A19082</td>\n",
              "      <td>RN2164</td>\n",
              "      <td>An Application Of Context- And Problem-Based L...</td>\n",
              "      <td>Research In Science Education</td>\n",
              "      <td>This study aims to investigate the applicabili...</td>\n",
              "      <td>https://eric.ed.gov/?q=title%3a%22An+Applicati...</td>\n",
              "      <td>Frederik</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Res Sci Educ (2018) 48:663–689 DOI 10.1007/s11...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>A18706</td>\n",
              "      <td>RN3208</td>\n",
              "      <td>An Inorganic Chemistry Laboratory Course As Re...</td>\n",
              "      <td>Journal Of Chemical Education</td>\n",
              "      <td>A research-based inorganic chemistry laborator...</td>\n",
              "      <td>https://eric.ed.gov/?q=title%3a%22An+Inorganic...</td>\n",
              "      <td>Frederik</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Cite This: J. Chem. Educ. 2018, 95, 1520−1525 ...</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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>458</th>\n",
              "      <td>A36859</td>\n",
              "      <td>ISI:000328668300029</td>\n",
              "      <td>Letter Writing As A Service-Learning Project: ...</td>\n",
              "      <td>Journal Of Chemical Education</td>\n",
              "      <td>Letter writing as an alternative to a traditio...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maja</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Communication pubs.acs.org/jchemeduc Letter Wr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>459</th>\n",
              "      <td>A39747</td>\n",
              "      <td>ISI:000441354400002</td>\n",
              "      <td>Implementation Of Innovative Chemistry Learnin...</td>\n",
              "      <td>Journal Of Baltic Science Education</td>\n",
              "      <td>This research aimed to provide an innovative c...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maja</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>IMPLEMENTATION OF INNOVATIVE CHEMISTRY LEARNIN...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>460</th>\n",
              "      <td>A43904</td>\n",
              "      <td>ISI:000377037300002</td>\n",
              "      <td>Empirical Evaluation Of A Virtual Laboratory A...</td>\n",
              "      <td>Annals Of Medicine And Surgery</td>\n",
              "      <td>Background: Personalised instruction is increa...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maja</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Annals of Medicine and Surgery 8 (2016) 6e13 C...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>461</th>\n",
              "      <td>A46126</td>\n",
              "      <td>ISI:000334106800007</td>\n",
              "      <td>Development And Application Of 7E Learning Mod...</td>\n",
              "      <td>Journal Of Baltic Science Education</td>\n",
              "      <td>The purposes of this study are to develop a co...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maja</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>DeVeLoPMent AnD APPLICAtIon oF 7e LeARnInG MoD...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>463</th>\n",
              "      <td>A54271</td>\n",
              "      <td>ISI:000266914900014</td>\n",
              "      <td>A Multiweek; Problem-Based Laboratory Project ...</td>\n",
              "      <td>Journal Of Chemical Education</td>\n",
              "      <td>We describe a set of laboratory exercises cent...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Maja</td>\n",
              "      <td>Yes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>In the Laboratory A Multiweek, Problem-Based L...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>431 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    IQLab ID  ...                                          cleantext\n",
              "0     A21167  ...  © 2002 by The International Union of Biochemis...\n",
              "1     A20656  ...  In the Laboratory A Laboratory Preparation of ...\n",
              "2     A20227  ...  Article A Practical Guide to Developing Virtua...\n",
              "3     A19082  ...  Res Sci Educ (2018) 48:663–689 DOI 10.1007/s11...\n",
              "4     A18706  ...  Cite This: J. Chem. Educ. 2018, 95, 1520−1525 ...\n",
              "..       ...  ...                                                ...\n",
              "458   A36859  ...  Communication pubs.acs.org/jchemeduc Letter Wr...\n",
              "459   A39747  ...  IMPLEMENTATION OF INNOVATIVE CHEMISTRY LEARNIN...\n",
              "460   A43904  ...  Annals of Medicine and Surgery 8 (2016) 6e13 C...\n",
              "461   A46126  ...  DeVeLoPMent AnD APPLICAtIon oF 7e LeARnInG MoD...\n",
              "463   A54271  ...  In the Laboratory A Multiweek, Problem-Based L...\n",
              "\n",
              "[431 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Q7Hs7EH1RaR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_ft.to_csv('IQlab_clean_fulltexts.tsv', sep='\\t')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ok-h4_b8apj4",
        "colab_type": "text"
      },
      "source": [
        "# Topic model (gensim)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QjNtxuB7al_p",
        "colab_type": "text"
      },
      "source": [
        "## Installing and importing packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lSClljjD4pXo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install --upgrade gensim"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UXUdDxPO4opD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os       #importing os to set environment variable\n",
        "def install_java():\n",
        "  !apt-get install -y openjdk-8-jdk-headless -qq > /dev/null      #install openjdk\n",
        "  os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"     #set environment variable\n",
        "  !java -version       #check java version\n",
        "install_java()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDvi9dvh4sCx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!wget http://mallet.cs.umass.edu/dist/mallet-2.0.8.zip\n",
        "!unzip mallet-2.0.8.zip"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bGQFqQaMbTUO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# https://pypi.python.org/pypi/pydot\n",
        "!apt-get -qq install -y graphviz && pip install -q pydot\n",
        "import pydot"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSTTaVZdaLGM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import nltk; nltk.download('stopwords')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NuRxJGIDaPPF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!python3 -m spacy download en"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6n4SjCWDacCq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install pyLDAvis"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jihqJuwIaT1M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import re\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from pprint import pprint\n",
        "\n",
        "# Gensim\n",
        "import gensim\n",
        "import gensim.corpora as corpora\n",
        "from gensim.utils import simple_preprocess\n",
        "from gensim.models import CoherenceModel\n",
        "\n",
        "# spacy for lemmatization\n",
        "import spacy\n",
        "\n",
        "# Plotting tools\n",
        "import pyLDAvis\n",
        "import pyLDAvis.gensim  # don't skip this\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# Enable logging for gensim - optional\n",
        "import logging\n",
        "logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.ERROR)\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\",category=DeprecationWarning)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mFghQP01ajBu",
        "colab_type": "text"
      },
      "source": [
        "## Preparing stop words"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PIsWAghtaix_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# NLTK Stop words\n",
        "from nltk.corpus import stopwords\n",
        "stop_words = stopwords.words('english')\n",
        "\n",
        "# Extra stopwords, insert as list into extrastop\n",
        "#extrastop = []\n",
        "#stop_words.extend(extrastop)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k3XHf4HJaZzF",
        "colab_type": "code",
        "outputId": "287e29bd-53bf-4eca-d92d-e5ea9635974e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        }
      },
      "source": [
        "#Import dataset\n",
        "df = pd.read_csv('/content/IQlab_clean_fulltexts.tsv', sep='\\t')\n",
        "df.info()"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 431 entries, 0 to 430\n",
            "Data columns (total 12 columns):\n",
            "Unnamed: 0          431 non-null int64\n",
            "IQLab ID            431 non-null object\n",
            "ERIC/ISI ID         431 non-null object\n",
            "Title               431 non-null object\n",
            "Journal             431 non-null object\n",
            "Abstract            431 non-null object\n",
            "Link                323 non-null object\n",
            "Responsible         431 non-null object\n",
            "Pub. downloaded?    431 non-null object\n",
            "Unnamed: 8          24 non-null object\n",
            "Unnamed: 9          10 non-null object\n",
            "cleantext           431 non-null object\n",
            "dtypes: int64(1), object(11)\n",
            "memory usage: 40.5+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ne3bNP6QpWZE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.pop('Unnamed: 0')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IixKp9LLbPf6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert to list\n",
        "data = df.cleantext.values.tolist()\n",
        "\n",
        "# Remove new line characters\n",
        "data = [re.sub('\\s+', ' ', sent) for sent in data]\n",
        "\n",
        "# Remove distracting single quotes\n",
        "data = [re.sub(\"\\'\", \"\", sent) for sent in data]\n",
        "\n",
        "pprint(data[:1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GIPHjiqfcpjP",
        "colab_type": "code",
        "outputId": "33fe4930-bd7d-4f88-d44d-462c922c65fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "def sent_to_words(sentences):\n",
        "    for sentence in sentences:\n",
        "        yield(gensim.utils.simple_preprocess(str(sentence), deacc=True))  # deacc=True removes punctuations\n",
        "\n",
        "data_words = list(sent_to_words(data))\n",
        "\n",
        "print(data_words[:1])"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[['by', 'the', 'international', 'union', 'of', 'biochemistry', 'and', 'molecular', 'biology', 'printed', 'in', 'biochemistry', 'and', 'molecular', 'biology', 'education', 'vol', 'no', 'pp', 'articles', 'collaborative', 'investigative', 'recombinant', 'dna', 'technology', 'course', 'with', 'laboratory', 'received', 'for', 'publication', 'june', 'and', 'in', 'revised', 'form', 'july', 'leo', 'pezzementi', 'and', 'joy', 'johnson', 'from', 'the', 'division', 'of', 'science', 'and', 'mathematics', 'birmingham', 'southern', 'college', 'birmingham', 'alabama', 'and', 'the', 'center', 'for', 'educational', 'accountability', 'school', 'of', 'education', 'university', 'of', 'alabama', 'at', 'birmingham', 'birmingham', 'alabama', 'recombinant', 'dna', 'technology', 'course', 'was', 'designed', 'to', 'promote', 'contextual', 'collaborative', 'inquiry', 'based', 'learning', 'of', 'science', 'where', 'students', 'learn', 'from', 'one', 'another', 'and', 'have', 'sense', 'of', 'ownership', 'of', 'their', 'education', 'the', 'class', 'stressed', 'group', 'presentations', 'and', 'critical', 'reading', 'and', 'discussion', 'of', 'scientific', 'articles', 'the', 'laboratory', 'consisted', 'of', 'two', 'research', 'projects', 'random', 'cdna', 'cloning', 'and', 'in', 'vitro', 'expression', 'and', 'site', 'directed', 'mutagenesis', 'of', 'cholinesterase', 'students', 'prepared', 'poster', 'and', 'wrote', 'scientific', 'paper', 'peer', 'reviewed', 'by', 'scientists', 'at', 'nearby', 'university', 'multiple', 'methods', 'were', 'used', 'to', 'evaluate', 'the', 'course', 'evaluation', 'data', 'were', 'consistent', 'and', 'positive', 'students', 'learned', 'to', 'read', 'present', 'and', 'discuss', 'scientific', 'articles', 'in', 'depth', 'work', 'collaboratively', 'in', 'small', 'teams', 'and', 'perform', 'genetic', 'engineering', 'experiments', 'the', 'students', 'also', 'outperformed', 'all', 'biology', 'majors', 'on', 'the', 'molecular', 'biology', 'genetics', 'subtest', 'of', 'the', 'major', 'field', 'achievement', 'test', 'the', 'course', 'has', 'an', 'extensive', 'website', 'at', 'http', 'panther', 'bsc', 'edu', 'lepezzem', 'bi', 'bi', 'html', 'keywords', 'recombinant', 'dna', 'collaborative', 'learning', 'investigative', 'laboratories', 'cdna', 'cloning', 'in', 'vitro', 'mutagenesis', 'course', 'in', 'recombinant', 'dna', 'technology', 'biology', 'was', 'designed', 'to', 'give', 'students', 'better', 'understanding', 'of', 'both', 'the', 'content', 'and', 'process', 'of', 'science', 'and', 'to', 'promote', 'contextual', 'collaborative', 'cooperative', 'inquiry', 'based', 'learning', 'of', 'science', 'an', 'effective', 'form', 'of', 'learning', 'where', 'students', 'teach', 'one', 'another', 'and', 'have', 'sense', 'of', 'ownership', 'of', 'their', 'education', 'the', 'goals', 'of', 'the', 'recombinant', 'dna', 'technology', 'course', 'were', 'to', 'teach', 'students', 'molecular', 'biology', 'in', 'the', 'context', 'of', 'the', 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'toxicol', 'the', 'boyer', 'commission', 'on', 'educating', 'undergraduates', 'in', 'the', 're', 'naples', 'cc', 'sunysb', 'edu', 'pres', 'boyer', 'nsf', 'search', 'university', 'suny', 'at', 'stony', 'brook', 'stony', 'brook', 'ny', 'martin', 'mulla', 'willis', 'cammers', 'goodwin', 'derivatives', 'of', 'benzyl', 'bromide', 'minimalist', 'models', 'for', 'face', 'to', 'face', 'center', 'to', 'edge', 'stacking', 'in', 'water', 'org', 'chem']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P184aiW4dAM7",
        "colab_type": "code",
        "outputId": "8b882aac-b6fe-42d8-943d-4f05ad4b67e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Build the bigram and trigram models\n",
        "bigram = gensim.models.Phrases(data_words, min_count=5, threshold=100) # higher threshold fewer phrases.\n",
        "trigram = gensim.models.Phrases(bigram[data_words], threshold=100)  \n",
        "\n",
        "# Faster way to get a sentence clubbed as a trigram/bigram\n",
        "bigram_mod = gensim.models.phrases.Phraser(bigram)\n",
        "trigram_mod = gensim.models.phrases.Phraser(trigram)\n",
        "\n",
        "# See trigram example\n",
        "print(trigram_mod[bigram_mod[data_words[0]]])"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['by', 'the', 'international_union', 'of', 'biochemistry', 'and', 'molecular_biology', 'printed', 'in', 'biochemistry', 'and', 'molecular_biology', 'education', 'vol_no_pp', 'articles', 'collaborative', 'investigative', 'recombinant_dna_technology', 'course', 'with', 'laboratory', 'received', 'for', 'publication', 'june', 'and', 'in', 'revised', 'form', 'july', 'leo', 'pezzementi', 'and', 'joy', 'johnson', 'from', 'the', 'division', 'of', 'science', 'and', 'mathematics', 'birmingham', 'southern', 'college', 'birmingham', 'alabama', 'and', 'the', 'center', 'for', 'educational', 'accountability', 'school', 'of', 'education', 'university', 'of', 'alabama', 'at', 'birmingham', 'birmingham', 'alabama', 'recombinant_dna_technology', 'course', 'was', 'designed', 'to', 'promote', 'contextual', 'collaborative', 'inquiry', 'based', 'learning', 'of', 'science', 'where', 'students', 'learn', 'from', 'one', 'another', 'and', 'have', 'sense', 'of', 'ownership', 'of', 'their', 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'face', 'to', 'face', 'center', 'to', 'edge', 'stacking', 'in', 'water', 'org', 'chem']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aaPc_LzTdOAb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define functions for stopwords, bigrams, trigrams and lemmatization\n",
        "def remove_stopwords(texts):\n",
        "    return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]\n",
        "\n",
        "def make_bigrams(texts):\n",
        "    return [bigram_mod[doc] for doc in texts]\n",
        "\n",
        "def make_trigrams(texts):\n",
        "    return [trigram_mod[bigram_mod[doc]] for doc in texts]\n",
        "\n",
        "def lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n",
        "    \"\"\"https://spacy.io/api/annotation\"\"\"\n",
        "    texts_out = []\n",
        "    for sent in texts:\n",
        "        doc = nlp(\" \".join(sent)) \n",
        "        texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n",
        "    return texts_out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Qv-IJxTdZCb",
        "colab_type": "code",
        "outputId": "45c736ea-1947-4fb5-ac11-5e7e01fd2868",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Remove Stop Words\n",
        "data_words_nostops = remove_stopwords(data_words)\n",
        "\n",
        "# Form Bigrams\n",
        "data_words_bigrams = make_bigrams(data_words_nostops)\n",
        "\n",
        "# Initialize spacy 'en' model, keeping only tagger component (for efficiency)\n",
        "# python3 -m spacy download en\n",
        "nlp = spacy.load('en', disable=['parser', 'ner'])\n",
        "\n",
        "# Do lemmatization keeping only noun, adj, vb, adv\n",
        "data_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n",
        "\n",
        "# process text using custom filters\n",
        "#data_stemmed = preprocess_string(data_words, custom_filters)\n",
        "\n",
        "print(data_lemmatized[:1])"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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'martin', 'mulla', 'willis', 'cammer', 'goodwin', 'derivative', 'benzyl', 'minimalist', 'model', 'face', 'face', 'center', 'edge', 'stack', 'water', 'org', 'chem']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CCmW26PadxQz",
        "colab_type": "code",
        "outputId": "9a9935b6-ba7c-4adc-e63d-a027d4894e32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "# Create Dictionary\n",
        "id2word = corpora.Dictionary(data_lemmatized)\n",
        "\n",
        "# Create Corpus\n",
        "texts = data_lemmatized\n",
        "\n",
        "# Term Document Frequency\n",
        "corpus = [id2word.doc2bow(text) for text in texts]\n",
        "\n",
        "# View\n",
        "print(corpus[:1])"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[(0, 2), (1, 1), (2, 1), (3, 1), (4, 3), (5, 1), (6, 4), (7, 1), (8, 2), (9, 1), (10, 1), (11, 4), (12, 1), (13, 1), (14, 2), (15, 5), (16, 1), (17, 6), (18, 1), (19, 1), (20, 4), (21, 1), (22, 1), (23, 4), (24, 3), (25, 1), (26, 3), (27, 1), (28, 1), (29, 1), (30, 1), (31, 2), (32, 2), (33, 1), (34, 1), (35, 2), (36, 1), (37, 3), (38, 2), (39, 1), (40, 1), (41, 4), (42, 3), (43, 1), (44, 21), (45, 1), (46, 1), (47, 2), (48, 1), (49, 1), (50, 2), (51, 1), (52, 1), (53, 1), (54, 2), (55, 1), (56, 2), (57, 1), (58, 1), (59, 1), (60, 14), (61, 2), (62, 2), (63, 1), (64, 2), (65, 1), (66, 1), (67, 1), (68, 2), (69, 2), (70, 1), (71, 1), (72, 2), (73, 1), (74, 20), (75, 2), (76, 7), (77, 1), (78, 1), (79, 2), (80, 1), (81, 1), (82, 1), (83, 1), (84, 2), (85, 1), (86, 3), (87, 1), (88, 1), (89, 1), (90, 1), (91, 1), (92, 1), (93, 6), (94, 1), (95, 5), (96, 1), (97, 5), (98, 1), (99, 3), (100, 3), (101, 2), (102, 1), (103, 15), (104, 1), (105, 4), (106, 5), (107, 1), (108, 13), 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(508, 1), (509, 2), (510, 1), (511, 2), (512, 1), (513, 1), (514, 2), (515, 11), (516, 1), (517, 2), (518, 1), (519, 2), (520, 1), (521, 1), (522, 1), (523, 2), (524, 1), (525, 2), (526, 3), (527, 7), (528, 1), (529, 3), (530, 1), (531, 2), (532, 1), (533, 1), (534, 1), (535, 1), (536, 1), (537, 1), (538, 3), (539, 1), (540, 3), (541, 1), (542, 1), (543, 1), (544, 1), (545, 1), (546, 1), (547, 1), (548, 1), (549, 3), (550, 1), (551, 4), (552, 1), (553, 6), (554, 1), (555, 3), (556, 1), (557, 2), (558, 1), (559, 1), (560, 1), (561, 3), (562, 8), (563, 3), (564, 1), (565, 1), (566, 1), (567, 1), (568, 5), (569, 2), (570, 1), (571, 1), (572, 1), (573, 1), (574, 1), (575, 17), (576, 2), (577, 2), (578, 1), (579, 7), (580, 1), (581, 4), (582, 2), (583, 1), (584, 1), (585, 1), (586, 1), (587, 1), (588, 5), (589, 2), (590, 2), (591, 2), (592, 1), (593, 1), (594, 1), (595, 1), (596, 9), (597, 1), (598, 2), (599, 3), (600, 1), (601, 7), (602, 1), (603, 1), (604, 2), (605, 1), (606, 4), (607, 2), (608, 3), (609, 1), (610, 1), (611, 3), (612, 1), (613, 5), (614, 20), (615, 1), (616, 1), (617, 1), (618, 2), (619, 1), (620, 1), (621, 10), (622, 2), (623, 1), (624, 3), (625, 3), (626, 1), (627, 1), (628, 1), (629, 1), (630, 2), (631, 2), (632, 1), (633, 2), (634, 1), (635, 2), (636, 1), (637, 4), (638, 1), (639, 1), (640, 3), (641, 21), (642, 13), (643, 4), (644, 1), (645, 1), (646, 1), (647, 1), (648, 4), (649, 1), (650, 2), (651, 2), (652, 2), (653, 1), (654, 1), (655, 5), (656, 1), (657, 2), (658, 1), (659, 1), (660, 1), (661, 2), (662, 1), (663, 1), (664, 2), (665, 4), (666, 2), (667, 1), (668, 2), (669, 1), (670, 1), (671, 6), (672, 1), (673, 1), (674, 1), (675, 1), (676, 2), (677, 1), (678, 1), (679, 1), (680, 1), (681, 1), (682, 1), (683, 1), (684, 2), (685, 1), (686, 1), (687, 1), (688, 7), (689, 1), (690, 1), (691, 2), (692, 2), (693, 1), (694, 1), (695, 1), (696, 1), (697, 3), (698, 61), (699, 2), (700, 1), (701, 1), (702, 1), (703, 2), (704, 1), (705, 2), (706, 1), (707, 1), (708, 2), (709, 2), (710, 1), (711, 1), (712, 2), (713, 1), (714, 1), (715, 1), (716, 1), (717, 2), (718, 2), (719, 2), (720, 1), (721, 1), (722, 1), (723, 1), (724, 3), (725, 4), (726, 1), (727, 1), (728, 2), (729, 6), (730, 1), (731, 1), (732, 2), (733, 3), (734, 10), (735, 2), (736, 5), (737, 1), (738, 2), (739, 1), (740, 1), (741, 1), (742, 1), (743, 1), (744, 1), (745, 1), (746, 1), (747, 2), (748, 5), (749, 1), (750, 1), (751, 6), (752, 1), (753, 1), (754, 1), (755, 4), (756, 1), (757, 1), (758, 1), (759, 2), (760, 1), (761, 1), (762, 5), (763, 3), (764, 2), (765, 9), (766, 1), (767, 2), (768, 12), (769, 1), (770, 1), (771, 1), (772, 1), (773, 1), (774, 2), (775, 1), (776, 4), (777, 1), (778, 1), (779, 3), (780, 3), (781, 1), (782, 3), (783, 1), (784, 2), (785, 3), (786, 1), (787, 1), (788, 10), (789, 1), (790, 3), (791, 1), (792, 5), (793, 8), (794, 1)]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_WpnZxvPd7Uc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Human readable format of corpus (term-frequency)\n",
        "[[(id2word[id], freq) for id, freq in cp] for cp in corpus[:100]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c66Pdms4xJK0",
        "colab_type": "text"
      },
      "source": [
        "## Topic model based on mallet"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCVTYTJh5Rii",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "os.environ['MALLET_HOME'] = '/content/mallet-2.0.8'\n",
        "mallet_path = '/content/mallet-2.0.8/bin/mallet'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mapd39WwxLTM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ldamallet = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics=10, id2word=id2word)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gvg_vrluxWXv",
        "colab_type": "code",
        "outputId": "ea57cf72-a2ac-4711-d29f-447fd92b8132",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Show Topics\n",
        "pprint(ldamallet.show_topics(formatted=False))\n",
        "\n",
        "# Compute Coherence Score\n",
        "coherence_model_ldamallet = CoherenceModel(model=ldamallet, texts=data_lemmatized, dictionary=id2word, coherence='c_v')\n",
        "coherence_ldamallet = coherence_model_ldamallet.get_coherence()\n",
        "print('\\nCoherence Score: ', coherence_ldamallet)"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[(0,\n",
            "  [('laboratory', 0.05714200026991498),\n",
            "   ('student', 0.05485272139276128),\n",
            "   ('chemistry', 0.029605674212623025),\n",
            "   ('experiment', 0.02092340912612527),\n",
            "   ('lab', 0.01894403262972164),\n",
            "   ('question', 0.009112129679151067),\n",
            "   ('chem_educ', 0.00812743985644522),\n",
            "   ('group', 0.008102447728965376),\n",
            "   ('datum', 0.008032469772021813),\n",
            "   ('video', 0.007872520156150812)]),\n",
            " (1,\n",
            "  [('research', 0.019306427294084647),\n",
            "   ('student', 0.018962796732003226),\n",
            "   ('experience', 0.016704653038325325),\n",
            "   ('learn', 0.01561064553455591),\n",
            "   ('science', 0.011557207475717942),\n",
            "   ('study', 0.010926049300466355),\n",
            "   ('participant', 0.00791752866510046),\n",
            "   ('interview', 0.007608962446088573),\n",
            "   ('environment', 0.007272344752621059),\n",
            "   ('work', 0.0071320873803429295)]),\n",
            " (2,\n",
            "  [('student', 0.032899368609264124),\n",
            "   ('chemistry', 0.025082416047382244),\n",
            "   ('science', 0.023841984690171538),\n",
            "   ('laboratory', 0.018248868525451194),\n",
            "   ('group', 0.017181650555959098),\n",
            "   ('learn', 0.014091747220204504),\n",
            "   ('test', 0.013762083030675533),\n",
            "   ('education', 0.013270380510700118),\n",
            "   ('skill', 0.012594289545733921),\n",
            "   ('school', 0.012164049840755434)]),\n",
            " (3,\n",
            "  [('student', 0.03548423872769766),\n",
            "   ('skill', 0.02232371886222425),\n",
            "   ('practical', 0.0203998745938551),\n",
            "   ('work', 0.015469132987516388),\n",
            "   ('laboratory', 0.014742347375021376),\n",
            "   ('year', 0.014272074331642251),\n",
            "   ('university', 0.007845009405460868),\n",
            "   ('education', 0.007688251724334493),\n",
            "   ('question', 0.007567120788918657),\n",
            "   ('assessment', 0.007467365900929146)]),\n",
            " (4,\n",
            "  [('student', 0.04048995390766253),\n",
            "   ('concept', 0.014515266495199776),\n",
            "   ('chemistry', 0.01424363577716095),\n",
            "   ('activity', 0.013292928264025058),\n",
            "   ('knowledge', 0.01201965927321806),\n",
            "   ('test', 0.011603724736221108),\n",
            "   ('model', 0.010907671021246615),\n",
            "   ('understanding', 0.009812659689152597),\n",
            "   ('question', 0.009761728929520317),\n",
            "   ('learn', 0.008267759980306773)]),\n",
            " (5,\n",
            "  [('student', 0.054869592448440925),\n",
            "   ('research', 0.019013107860242472),\n",
            "   ('project', 0.013907529190712656),\n",
            "   ('lab', 0.012766742719098107),\n",
            "   ('report', 0.010345367512190758),\n",
            "   ('design', 0.00885787142665414),\n",
            "   ('undergraduate', 0.008455240907260771),\n",
            "   ('chemistry', 0.007521361785890037),\n",
            "   ('semester', 0.007057218270478236),\n",
            "   ('write', 0.0069621527311770235)]),\n",
            " (6,\n",
            "  [('student', 0.040713790713790714),\n",
            "   ('science', 0.021254331254331255),\n",
            "   ('level', 0.010852390852390852),\n",
            "   ('question', 0.010748440748440748),\n",
            "   ('change', 0.009369369369369369),\n",
            "   ('inquiry', 0.008884268884268885),\n",
            "   ('understanding', 0.006992376992376992),\n",
            "   ('concept', 0.0068814968814968815),\n",
            "   ('learn', 0.006798336798336798),\n",
            "   ('process', 0.006798336798336798)]),\n",
            " (7,\n",
            "  [('student', 0.01743041364573711),\n",
            "   ('learn', 0.017243792514626433),\n",
            "   ('virtual', 0.014780393583965512),\n",
            "   ('laboratory', 0.012839533820414485),\n",
            "   ('simulation', 0.011365226884640148),\n",
            "   ('computer', 0.010301486437309297),\n",
            "   ('learning', 0.00962031930875533),\n",
            "   ('design', 0.008808517388423892),\n",
            "   ('technology', 0.007371534678871689),\n",
            "   ('education', 0.006904981851094999)]),\n",
            " (8,\n",
            "  [('student', 0.036157632418041666),\n",
            "   ('laboratory', 0.016768308413047293),\n",
            "   ('biochemistry', 0.011412768679895572),\n",
            "   ('protein', 0.011134156786263403),\n",
            "   ('datum', 0.007522521128068601),\n",
            "   ('experiment', 0.006893064627640363),\n",
            "   ('cell', 0.0067898750374062265),\n",
            "   ('result', 0.006335840840376023),\n",
            "   ('experimental', 0.006253289168188713),\n",
            "   ('biology', 0.0061397806189311625)]),\n",
            " (9,\n",
            "  [('experiment', 0.01191375975198883),\n",
            "   ('chemical', 0.010350879440437607),\n",
            "   ('solution', 0.007878454029541),\n",
            "   ('green', 0.006623025582557231),\n",
            "   ('sample', 0.00644367866155955),\n",
            "   ('laboratory', 0.005803153943710688),\n",
            "   ('rate', 0.0052010607089327575),\n",
            "   ('compound', 0.005188250214575781),\n",
            "   ('safety', 0.005175439720218803),\n",
            "   ('water', 0.005111387248433917)])]\n",
            "\n",
            "Coherence Score:  0.36492997327141324\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h24SHxjPxn5g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n",
        "    \"\"\"\n",
        "    Compute c_v coherence for various number of topics\n",
        "\n",
        "    Parameters:\n",
        "    ----------\n",
        "    dictionary : Gensim dictionary\n",
        "    corpus : Gensim corpus\n",
        "    texts : List of input texts\n",
        "    limit : Max num of topics\n",
        "\n",
        "    Returns:\n",
        "    -------\n",
        "    model_list : List of LDA topic models\n",
        "    coherence_values : Coherence values corresponding to the LDA model with respective number of topics\n",
        "    \"\"\"\n",
        "    coherence_values = []\n",
        "    model_list = []\n",
        "    for num_topics in range(start, limit, step):\n",
        "        model = gensim.models.wrappers.LdaMallet(mallet_path, corpus=corpus, num_topics=num_topics, id2word=id2word)\n",
        "        model_list.append(model)\n",
        "        coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')\n",
        "        coherence_values.append(coherencemodel.get_coherence())\n",
        "\n",
        "    return model_list, coherence_values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPSmd0CoubDs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mallet_lda_model = gensim.models.wrappers.ldamallet.malletmodel2ldamodel(ldamallet)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BSZw_xx5ubmM",
        "colab_type": "code",
        "outputId": "a997dc54-2cc8-44e3-cea7-d6b0df20b803",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Visualize the topics\n",
        "pyLDAvis.enable_notebook()\n",
        "vis = pyLDAvis.gensim.prepare(mallet_lda_model, corpus, id2word)\n",
        "vis"
      ],
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pyLDAvis/_prepare.py:223: RuntimeWarning: divide by zero encountered in log\n",
            "  kernel = (topic_given_term * np.log((topic_given_term.T / topic_proportion).T))\n",
            "/usr/local/lib/python3.6/dist-packages/pyLDAvis/_prepare.py:240: RuntimeWarning: divide by zero encountered in log\n",
            "  log_lift = np.log(topic_term_dists / term_proportion)\n",
            "/usr/local/lib/python3.6/dist-packages/pyLDAvis/_prepare.py:241: RuntimeWarning: divide by zero encountered in log\n",
            "  log_ttd = np.log(topic_term_dists)\n",
            "/usr/local/lib/python3.6/dist-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
            "of pandas will change to not sort by default.\n",
            "\n",
            "To accept the future behavior, pass 'sort=False'.\n",
            "\n",
            "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
            "\n",
            "  return pd.concat([default_term_info] + list(topic_dfs))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el1391398407240625209687107296\"></div>\n",
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\"learn\", \"learn\", \"learning\", \"learning\", \"learning\", \"learning\", \"learning\", \"learning\", \"learning\", \"learning\", \"learning\", \"lecture\", \"lecture\", \"lecture\", \"lecture\", \"lecture\", \"lecture\", \"lengthy\", \"leonard\", \"lesson\", \"lesson\", \"lesson\", \"lesson\", \"lesson\", \"level\", \"level\", \"level\", \"level\", \"level\", \"level\", \"level\", \"level\", \"level\", \"lieff\", \"lignin\", \"lignin_degradation\", \"literacy\", \"literacy\", \"literacy\", \"literacy\", \"literature\", \"literature\", \"literature\", \"literature\", \"literature\", \"literature\", \"literature\", \"literature\", \"literature\", \"liversedge\", \"longitudinal\", \"longitudinal\", \"lopatto\", \"lopatto\", \"lse\", \"ltm\", \"lyall_patti\", \"macaulay\", \"macroscopic_microscopic\", \"major\", \"major\", \"major\", \"major\", \"major\", \"major\", \"make\", \"make\", \"make\", \"make\", \"make\", \"make\", \"make\", \"make\", \"make\", \"make\", \"malaysia\", \"male_female\", \"male_female\", \"male_female\", \"management\", \"management\", \"management\", \"management\", \"management\", \"mannitol\", \"manual\", \"manual\", \"manual\", \"manual\", \"marcus\", \"mark\", \"mark\", \"mark\", \"mass\", \"mass\", \"mass\", \"mass_transfer\", \"mathematica\", \"mawn\", \"mchem\", \"mcmullin\", \"medical\", \"medical\", \"medical\", \"medicinal\", \"meissner\", \"mennin\", \"mental_model\", \"mental_model\", \"menu\", \"metacognition\", \"metacognitive\", \"metacognitive\", \"metacognitive\", \"method\", \"method\", \"method\", \"method\", \"method\", \"method\", \"method\", \"method\", \"method\", \"method\", \"methoxyphenyl\", \"mib\", \"min\", \"min\", \"min\", \"min\", \"min\", \"mineral\", \"mini_project\", \"mirror_image\", \"misconception\", \"misconception\", \"misguided\", \"mixed_method\", \"ml\", \"ml\", \"ml\", \"ml\", \"ml\", \"mlli\", \"mmvcl\", \"model\", \"model\", \"model\", \"model\", \"model\", \"model\", \"model\", \"model\", \"model_re\\ufb01nement\", \"molecular\", \"molecular\", \"molecular\", \"molecular\", \"molecular_biology\", \"molecular_orbital\", \"molecule\", \"molecule\", \"molecule\", \"molecule\", \"monash\", \"monash_university\", \"motivation\", \"motivation\", \"motivation\", \"multimedia\", \"multimedia\", \"multimodal\", \"mungarulire\", \"mutan\", \"mutant\", \"mutation\", \"nakhleh_krajcik\", \"nakiboglu\", \"nanoscience\", \"natural_product\", \"nce\", \"necatibey\", \"neisser\", \"neuro\", \"neuron\", \"neutral_disagree\", \"newmark\", \"niemin\", \"nigeria\", \"niskanen\", \"nmr\", \"nmr\", \"noise\", \"noise\", \"nonconcurrent\", \"nos\", \"nos\", \"nsf\", \"ntho\", \"nuclide\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"number\", \"numerical_ability\", \"objective\", \"objective\", \"objective\", \"objective\", \"objective\", \"objective\", \"objective\", \"objective\", \"observation\", \"observation\", \"observation\", \"observation\", \"observation\", \"october_downloade\", \"oer\", \"omar\", \"online\", \"online\", \"online\", \"online\", \"online\", \"online\", \"online\", \"open_book\", \"openinquiry\", \"optar\", \"oral_microbiology\", \"oral_presentation\", \"orally\", \"organise\", \"oscillation\", \"osguthorpe\", \"osmosis\", \"oxidizer\", \"o\\ufb00ered\", \"parent\", \"parnoma\", \"participant\", \"participant\", \"participant\", \"participant\", \"participant\", \"participant\", \"particulate\", \"particulate\", \"pathogenic\", \"pathology\", \"payton\", \"pbl\", \"pbl\", \"pchk\", \"pck\", \"pcr\", \"peak\", \"peer_reviewed\", \"penny\", \"peptide\", \"peptide\", \"perceive\", \"perceive\", \"perceive\", \"perceive\", \"perceive\", \"perceive\", \"perceptible\", \"perception\", \"perception\", \"perception\", \"perception\", \"perception\", \"perception\", \"perform\", \"perform\", \"perform\", \"perform\", \"perform\", \"perform\", \"perform\", \"perform\", \"periodontal\", \"periodontic\", \"persistence\", \"perspective\", \"perspective\", \"perspective\", \"perspective\", \"perspective\", \"perspective\", \"perspective\", \"petri_dish\", \"peuke\", \"pharm\", \"pharmacy\", \"pharmacy\", \"phenomenon\", \"phenomenon\", \"phenomenon\", \"phenomenon\", \"phenophthalein\", \"photosynthesis\", \"physical\", \"physical\", \"physical\", \"physical\", \"physical\", \"physical\", \"picciano\", \"pip\", \"pipe\", \"pipet\", \"pipet\", \"plab\", \"plagiarism\", \"plane\", \"plant\", \"plant\", \"plant\", \"plate\", \"plate\", \"platform\", \"platform\", \"pln\", \"podcast\", \"podcast_treatment\", \"pogil\", \"polarity\", \"pontificia\", \"poster\", \"poster_presentation\", \"postgraduate\", \"posting\", \"postsurvey\", \"postsurvey\", \"posttest\", \"posttest\", \"pozo\", \"practical\", \"practical\", \"practical\", \"practice\", \"practice\", \"practice\", \"practice\", \"practice\", \"practice\", \"practice\", \"practice\", \"pre\", \"pre\", \"pre\", \"pre\", \"pre\", \"predict_observe\", \"prelab\", \"prelaboratory\", \"presentation\", \"presentation\", \"presentation\", \"presentation\", \"pressure\", \"pressure\", \"pressure\", \"pressure\", \"primer\", \"principle\", \"principle\", \"principle\", \"principle\", \"principle\", \"principle\", \"principle\", \"principle\", \"principle\", \"printer\", \"prior\", \"prior\", \"prior\", \"prior\", \"prior\", \"prior\", \"prior\", \"prior\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"problem\", \"procedural_guidance\", \"procedure\", \"procedure\", \"procedure\", \"procedure\", \"procedure\", \"procedure\", \"procedure\", \"process\", \"process\", \"process\", \"process\", \"process\", \"process\", \"process\", \"process\", \"procter\", \"proctored\", \"product\", \"product\", \"product\", \"product\", \"product\", \"product\", \"product\", \"profile_template\", \"prognosis\", \"program\", \"program\", \"program\", \"program\", \"program\", \"program\", \"program\", \"programme\", \"programme\", \"programme\", \"programme\", \"project\", \"project\", \"project\", \"project\", \"proportional_reasoning\", \"proposal\", \"proposal\", \"prospective_teacher\", \"protein\", \"provenance\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"provide\", \"psl\", \"psychol_meas\", \"publicacione\", \"puri\\ufb01cation\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"question\", \"quiz\", \"quiz\", \"quiz\", \"radio\", \"raffinose\", \"raise\", \"raise\", \"raise\", \"raise\", \"raise\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rate\", \"rattan\", \"rau\", \"reaction\", \"reaction\", \"reaction\", \"reaction\", \"reaction\", \"real\", \"real\", \"real\", \"real\", \"real\", \"real\", \"rebecca\", \"recognise\", \"record\", \"record\", \"record\", \"record\", \"recordkeepe\", \"reextract\", \"referee\", \"refined_model\", \"release\", \"release\", \"release\", \"remote\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"report\", \"representation\", \"representation\", \"representation\", \"require\", \"require\", \"require\", \"require\", \"require\", \"require\", \"require\", \"require\", \"require\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"research\", \"resident\", \"resident\", \"response\", \"response\", \"response\", \"response\", \"response\", \"response\", \"resubmit\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"result\", \"retrospectively\", \"reu\", \"reus\", \"re\\ufb01ned_model\", \"rna\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"role\", \"rudd\", \"safety\", \"safety\", \"safety\", \"safety\", \"safety\", \"salivaris\", \"salt_bridge\", \"sample\", \"sample\", \"sample\", \"sample\", \"sample\", \"sample\", \"sample\", \"sample\", \"sandi_urena\", \"sangra\", \"sangster\", \"scale\", \"scale\", \"scale\", \"scale\", \"scale\", \"scale\", \"scale\", \"scatterplot\", \"schematic\", \"schlosser\", \"school\", \"school\", \"school\", \"school\", \"school\", \"school\", \"science\", \"science\", \"science\", \"science\", \"science\", \"science\", \"science\", \"science\", \"sciencemag\", \"scientific\", \"scientific\", \"scientific\", \"scientific\", \"scientific\", \"scienti\\ufb01c\", \"scienti\\ufb01c\", \"scienti\\ufb01c\", \"scienti\\ufb01c\", \"scienti\\ufb01cally\", \"scol\", \"score\", \"score\", \"score\", \"score\", \"score\", \"score\", \"score\", \"seb\", \"secondary_metabolite\", \"section\", \"section\", \"section\", \"section\", \"section\", \"secundaria\", \"seery\", \"self_efficacy\", \"self_efficacy\", \"self_ef\\ufb01cacy\", \"self_ef\\ufb01cacy\", \"self_regulation\", \"self_regulatory\", \"semantic_density\", \"semester\", \"semester\", \"semester\", \"semester\", \"seminario\", \"sender\", \"separatory_funnel\", \"september_downloade\", \"sequence\", \"sequence\", \"sequence\", \"sequence\", \"sequence\", \"sequence\", \"sequence\", \"series\", \"series\", \"series\", \"series\", \"series\", \"series\", \"session\", \"session\", \"session\", \"session\", \"session\", \"shalhoub\", \"shibley\", \"shidler\", \"show\", \"show\", \"show\", \"show\", \"show\", \"show\", \"show\", \"show\", \"show\", \"show\", \"signed_rank\", \"significant_figure\", \"signi\\ufb01cant_di\\ufb00erence\", \"simonson\", \"simulate\", \"simulate\", \"simulation\", \"simulator\", \"sinex\", \"skeptical\", \"skill\", \"skill\", \"skill\", \"skill\", \"skill\", \"sloanconsortium\", \"social\", \"social\", \"social\", \"social\", \"social\", \"social_psychology\", \"soft\", \"software\", \"software\", \"software\", \"software\", \"software\", \"solubility_equilibrium\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solvent\", \"solvent\", \"solvent\", \"soton\", \"sovegjarto\", \"sparrow\", \"spatial\", \"spatial\", \"spatial\", \"spatial_ability\", \"speci\\ufb01c\", \"speci\\ufb01c\", \"speci\\ufb01c\", \"spectra\", \"spectra\", \"spectrometer\", \"spectrometer\", \"spice\", \"spry\", \"spst\", \"staff\", \"staff\", \"stain\", \"statement\", \"statement\", \"statement\", \"statement\", \"statement\", \"statement\", \"statement\", \"statistically_signi\\ufb01cant\", \"statistically_signi\\ufb01cant\", \"sta\\ufb00\", \"sta\\ufb00\", \"sta\\ufb00\", \"steel_wool\", \"stem\", \"stem\", \"stem\", \"stereochemistry\", \"stote\", \"strain\", \"strain\", \"structural_isomer\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"studio\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"subscale\", \"subscale\", \"subscale\", \"subscribe\", \"substrate\", \"subtest\", \"subtheme\", \"suf\\ufb01ciency\", \"sugar\", \"sugar\", \"suljak\", \"summarise\", \"supragingival\", \"surgical\", \"survey\", \"survey\", \"survey\", \"survey\", \"sustainability\", \"sustainability\", \"suzuki\", \"swh\", \"swinglehurst\", \"sydney\", \"symbolic\", \"symbolic\", \"symmetry\", \"symmetry\", \"synthesis\", \"synthesis\", \"synthesis\", \"synthesis\", \"syringe\", \"syringe\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"ta\", \"tablet\", \"tablet\", \"tablet\", \"tas\", \"tas\", \"task\", \"task\", \"task\", \"task\", \"task\", \"task\", \"task\", \"tbl\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teacher\", \"teacher\", \"teacher\", \"teacher\", \"teacher\", \"teacher\", \"teacher_candidate\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"team\", \"team\", \"team\", \"teammate\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"tedious\", \"temperature\", \"temperature\", \"temperature\", \"temperature_gradient\", \"template\", \"template\", \"template\", \"template_condition\", \"tesis\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"theme\", \"theme\", \"theme\", \"theme\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"thermodynamic\", \"thermodynamic_principle\", \"thing\", \"thing\", \"thing\", \"thing\", \"thompson_river\", \"tick\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"timonen\", \"titles_template\", \"tll\", \"tnf\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"trade\", \"traditional\", \"traditional\", \"traditional\", \"traditional\", \"traditional\", \"traditional\", \"transfer_credit\", \"transferable\", \"transferable\", \"trisodium\", \"tru\", \"truol\", \"tube\", \"tube\", \"tube\", \"turkey\", \"tweezer\", \"ubiquitin_ligase\", \"ude\", \"ugs\", \"uk\", \"uk\", \"uk\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"undertake\", \"undertake\", \"unequivocally\", \"unger\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"unschedule\", \"unscheduled_presentation\", \"ure\", \"urlacher\", \"urm\", \"usability\", \"user\", \"usma\", \"valuation\", \"vapour_pressure\", \"varma_nelson\", \"vashaw\", \"verage\", \"video\", \"video\", \"videodiscs\", \"vilche\", \"virtual\", \"virtual_reality\", \"visual\", \"visual\", \"visual\", \"visual\", \"visual_media\", \"visual_representation\", \"visualization\", \"visualization\", \"waldo\", \"walker_sampson\", \"warwick\", \"watch\", \"watch\", \"water\", \"water\", \"water\", \"wavelength\", \"web\", \"web\", \"web\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week_contraste\", \"welch\", \"whatknow\", \"wigber\", \"wiki\", \"wikipedia\", \"willoughby\", \"wisman\", \"wissenserwerb\", \"wit\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"write\", \"write\", \"write\", \"write\", \"write\", \"writer\", \"writing\", \"writing\", \"wuhan_university\", \"xxxi\", \"xyw\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yeast\", \"zhen\", \"\\ufb01nal\", \"\\ufb01nal\", \"\\ufb01nal\", \"\\ufb01nal_examination\", \"\\ufb01nancial\", \"\\ufb01rst\", \"\\ufb01rst\", \"\\ufb01rst\", \"\\ufb01rst\", \"\\ufb01rst\", \"\\ufb01rst\", \"\\ufb01ve\", \"\\ufb01ve\", \"\\ufb01ve\", \"\\ufb01ve\", \"\\ufb01ve\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 6, 3, 7, 2, 4, 5, 8, 9, 10]};\n",
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            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "0     -0.002546  0.069163       1        1  14.464798\n",
              "5      0.070456  0.125768       2        1  12.925090\n",
              "2      0.080966 -0.050744       3        1  12.734551\n",
              "6      0.042186 -0.127472       4        1  10.498145\n",
              "1      0.145194 -0.079476       5        1  10.383740\n",
              "3      0.087150  0.083735       6        1  10.171961\n",
              "4      0.046568 -0.144348       7        1   8.486196\n",
              "7     -0.014974  0.029816       8        1   7.750783\n",
              "8     -0.098142  0.176248       9        1   6.974822\n",
              "9     -0.356859 -0.082691      10        1   5.609914, topic_info=     Category          Freq        Term         Total  loglift  logprob\n",
              "405   Default  22550.000000  laboratory  22550.000000  30.0000  30.0000\n",
              "106   Default  15685.000000   chemistry  15685.000000  29.0000  29.0000\n",
              "641   Default  11689.000000     science  11689.000000  28.0000  28.0000\n",
              "669   Default   7526.000000       skill   7526.000000  27.0000  27.0000\n",
              "1127  Default   3540.000000   practical   3540.000000  26.0000  26.0000\n",
              "...       ...           ...         ...           ...      ...      ...\n",
              "1450  Topic10    282.970885     control   2680.369718   0.6323  -5.7084\n",
              "208   Topic10    263.304955   determine   1718.107942   1.0050  -5.7804\n",
              "621   Topic10    286.248540      result   6753.302437  -0.2803  -5.6969\n",
              "527   Topic10    255.657093     perform   2384.716360   0.6476  -5.8099\n",
              "440   Topic10    255.657093      method   3506.026803   0.2622  -5.8099\n",
              "\n",
              "[1081 rows x 6 columns], token_table=       Topic      Freq           Term\n",
              "term                                 \n",
              "5602       9  0.296123     absorbance\n",
              "5602      10  0.707159     absorbance\n",
              "5603      10  1.004151     absorption\n",
              "19948      3  0.924436     academical\n",
              "14087      6  1.000971  accreditation\n",
              "...      ...       ...            ...\n",
              "3440       2  0.717535            ﬁve\n",
              "3440       3  0.088713            ﬁve\n",
              "3440       4  0.125243            ﬁve\n",
              "3440       5  0.023483            ﬁve\n",
              "3440       9  0.049575            ﬁve\n",
              "\n",
              "[2140 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[1, 6, 3, 7, 2, 4, 5, 8, 9, 10])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eHsaWIwtx7nH",
        "colab_type": "text"
      },
      "source": [
        "## Optimizing number of topics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9F2jRy14xq1m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Can take a long time to run.\n",
        "model_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_lemmatized, start=2, limit=40, step=4)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gtyktrv_xxjw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "0b2299ba-417e-47fd-fa69-8022cb049823"
      },
      "source": [
        "# Show graph\n",
        "limit=40; start=2; step=4;\n",
        "x = range(start, limit, step)\n",
        "plt.plot(x, coherence_values)\n",
        "plt.xlabel(\"Num Topics\")\n",
        "plt.ylabel(\"Coherence score\")\n",
        "plt.legend((\"coherence_values\"), loc='best')\n",
        "plt.show()"
      ],
      "execution_count": 155,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AZ4DnnW0jgQ+A4araFugF/HUMZhM0q7bv58EZy+jRpCZ39G3hdhxjjAv8Gc21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\ngTDGGJMvKxDGGGPy9f9rND04K8LxGAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dtM2cup7x2bs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "6f8e5e4e-131f-4df4-c2ed-4ff9b1128f9f"
      },
      "source": [
        "# Print the coherence scores\n",
        "for m, cv in zip(x, coherence_values):\n",
        "    print(\"Num Topics =\", m, \" has Coherence Value of\", round(cv, 4))"
      ],
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Num Topics = 2  has Coherence Value of 0.3272\n",
            "Num Topics = 6  has Coherence Value of 0.3467\n",
            "Num Topics = 10  has Coherence Value of 0.3596\n",
            "Num Topics = 14  has Coherence Value of 0.3732\n",
            "Num Topics = 18  has Coherence Value of 0.3639\n",
            "Num Topics = 22  has Coherence Value of 0.3801\n",
            "Num Topics = 26  has Coherence Value of 0.3834\n",
            "Num Topics = 30  has Coherence Value of 0.3704\n",
            "Num Topics = 34  has Coherence Value of 0.3775\n",
            "Num Topics = 38  has Coherence Value of 0.3878\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aUw9LnDgx6pm",
        "colab_type": "code",
        "outputId": "fae62bdd-5f00-40aa-a859-8b8d956dbc8e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Select the model and print the topics\n",
        "optimal_model = model_list[6]\n",
        "model_topics = optimal_model.show_topics(formatted=False)\n",
        "pprint(optimal_model.print_topics(num_words=10))"
      ],
      "execution_count": 157,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[(25,\n",
            "  '0.057*\"model\" + 0.038*\"student\" + 0.028*\"interaction\" + 0.021*\"level\" + '\n",
            "  '0.017*\"datum\" + 0.015*\"group\" + 0.014*\"research\" + 0.012*\"laboratory\" + '\n",
            "  '0.012*\"study\" + 0.009*\"treatment\"'),\n",
            " (8,\n",
            "  '0.032*\"student\" + 0.016*\"protein\" + 0.015*\"biochemistry\" + '\n",
            "  '0.014*\"laboratory\" + 0.009*\"molecular_biology\" + 0.009*\"cell\" + '\n",
            "  '0.008*\"datum\" + 0.008*\"enzyme\" + 0.008*\"fig\" + 0.007*\"gene\"'),\n",
            " (6,\n",
            "  '0.072*\"student\" + 0.027*\"lab\" + 0.014*\"report\" + 0.012*\"chemistry\" + '\n",
            "  '0.012*\"ﬁrst\" + 0.010*\"project\" + 0.009*\"article\" + 0.009*\"question\" + '\n",
            "  '0.008*\"semester\" + 0.008*\"chemical\"'),\n",
            " (19,\n",
            "  '0.050*\"student\" + 0.017*\"change\" + 0.014*\"water\" + 0.012*\"temperature\" + '\n",
            "  '0.012*\"level\" + 0.011*\"concept\" + 0.009*\"response\" + 0.009*\"gas\" + '\n",
            "  '0.009*\"idea\" + 0.009*\"chemical\"'),\n",
            " (12,\n",
            "  '0.024*\"lab\" + 0.021*\"learn\" + 0.019*\"student\" + 0.016*\"experience\" + '\n",
            "  '0.014*\"work\" + 0.013*\"group\" + 0.012*\"interview\" + 0.011*\"knowledge\" + '\n",
            "  '0.010*\"participant\" + 0.010*\"teaching\"'),\n",
            " (16,\n",
            "  '0.048*\"chemistry\" + 0.029*\"science\" + 0.029*\"attitude\" + 0.027*\"school\" + '\n",
            "  '0.024*\"student\" + 0.024*\"laboratory\" + 0.022*\"item\" + 0.016*\"scale\" + '\n",
            "  '0.016*\"education\" + 0.013*\"factor\"'),\n",
            " (9,\n",
            "  '0.029*\"student\" + 0.027*\"approach\" + 0.027*\"learn\" + 0.023*\"problem\" + '\n",
            "  '0.018*\"learning\" + 0.018*\"pbl\" + 0.015*\"strategy\" + 0.014*\"case\" + '\n",
            "  '0.013*\"base\" + 0.010*\"study\"'),\n",
            " (18,\n",
            "  '0.033*\"student\" + 0.027*\"science\" + 0.024*\"write\" + 0.017*\"report\" + '\n",
            "  '0.016*\"argument\" + 0.016*\"evidence\" + 0.014*\"investigation\" + 0.013*\"claim\" '\n",
            "  '+ 0.012*\"scientiﬁc\" + 0.010*\"learn\"'),\n",
            " (10,\n",
            "  '0.037*\"video\" + 0.014*\"tool\" + 0.011*\"learn\" + 0.008*\"online\" + '\n",
            "  '0.007*\"access\" + 0.007*\"notebook\" + 0.007*\"image\" + 0.007*\"technology\" + '\n",
            "  '0.007*\"figure\" + 0.007*\"content\"'),\n",
            " (21,\n",
            "  '0.043*\"student\" + 0.036*\"laboratory\" + 0.035*\"chemistry\" + '\n",
            "  '0.017*\"experience\" + 0.015*\"research\" + 0.015*\"learn\" + 0.014*\"chem_educ\" + '\n",
            "  '0.012*\"study\" + 0.012*\"response\" + 0.009*\"cognitive\"'),\n",
            " (0,\n",
            "  '0.035*\"student\" + 0.026*\"activity\" + 0.016*\"learn\" + 0.014*\"knowledge\" + '\n",
            "  '0.012*\"understanding\" + 0.011*\"representation\" + 0.010*\"prior\" + '\n",
            "  '0.010*\"visual\" + 0.009*\"chemistry\" + 0.009*\"instructional\"'),\n",
            " (13,\n",
            "  '0.077*\"experiment\" + 0.064*\"laboratory\" + 0.047*\"student\" + '\n",
            "  '0.019*\"chemistry\" + 0.016*\"inquiry\" + 0.016*\"experimental\" + '\n",
            "  '0.015*\"research\" + 0.014*\"base\" + 0.012*\"activity\" + 0.011*\"procedure\"'),\n",
            " (1,\n",
            "  '0.024*\"laboratory\" + 0.023*\"safety\" + 0.017*\"class\" + 0.014*\"student\" + '\n",
            "  '0.011*\"chemical\" + 0.010*\"practice\" + 0.010*\"medical\" + 0.010*\"base\" + '\n",
            "  '0.007*\"drug\" + 0.006*\"pharmacy\"'),\n",
            " (24,\n",
            "  '0.052*\"student\" + 0.035*\"laboratory\" + 0.029*\"practical\" + 0.022*\"work\" + '\n",
            "  '0.017*\"year\" + 0.014*\"study\" + 0.011*\"exercise\" + 0.010*\"session\" + '\n",
            "  '0.009*\"time\" + 0.009*\"question\"'),\n",
            " (3,\n",
            "  '0.046*\"student\" + 0.030*\"concept\" + 0.020*\"chemistry\" + 0.014*\"understand\" '\n",
            "  '+ 0.013*\"understanding\" + 0.010*\"solution\" + 0.010*\"reaction\" + '\n",
            "  '0.010*\"study\" + 0.010*\"acid\" + 0.009*\"conceptual\"'),\n",
            " (15,\n",
            "  '0.018*\"motivation\" + 0.018*\"research\" + 0.016*\"student\" + '\n",
            "  '0.015*\"environment\" + 0.014*\"science\" + 0.012*\"achievement\" + 0.011*\"goal\" '\n",
            "  '+ 0.010*\"model\" + 0.009*\"study\" + 0.008*\"high\"'),\n",
            " (7,\n",
            "  '0.020*\"chemistry\" + 0.019*\"green\" + 0.015*\"sample\" + 0.014*\"environmental\" '\n",
            "  '+ 0.013*\"group\" + 0.010*\"education\" + 0.008*\"food\" + 0.008*\"study\" + '\n",
            "  '0.007*\"test\" + 0.007*\"student\"'),\n",
            " (20,\n",
            "  '0.064*\"student\" + 0.050*\"laboratory\" + 0.027*\"chemistry\" + 0.023*\"lab\" + '\n",
            "  '0.023*\"lecture\" + 0.016*\"instructor\" + 0.012*\"class\" + 0.011*\"video\" + '\n",
            "  '0.010*\"exam\" + 0.010*\"grade\"'),\n",
            " (11,\n",
            "  '0.058*\"group\" + 0.050*\"test\" + 0.035*\"score\" + 0.018*\"student\" + '\n",
            "  '0.018*\"study\" + 0.015*\"control\" + 0.014*\"result\" + 0.012*\"post\" + '\n",
            "  '0.011*\"table\" + 0.011*\"pre\"'),\n",
            " (2,\n",
            "  '0.026*\"engineering\" + 0.013*\"design\" + 0.010*\"rate\" + 0.010*\"system\" + '\n",
            "  '0.009*\"chemical\" + 0.009*\"process\" + 0.008*\"student\" + 0.008*\"control\" + '\n",
            "  '0.007*\"experiment\" + 0.006*\"engineer\"')]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3nGNFO3Io6f",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimal_model_lda = gensim.models.wrappers.ldamallet.malletmodel2ldamodel(optimal_model)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NEf8OIzoH_4Q",
        "colab_type": "code",
        "outputId": "8562817e-3d8b-415f-a013-6b95d7b562bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Visualize the topics\n",
        "pyLDAvis.enable_notebook()\n",
        "vis = pyLDAvis.gensim.prepare(optimal_model_lda, corpus, id2word)\n",
        "vis"
      ],
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pyLDAvis/_prepare.py:223: RuntimeWarning: divide by zero encountered in log\n",
            "  kernel = (topic_given_term * np.log((topic_given_term.T / topic_proportion).T))\n",
            "/usr/local/lib/python3.6/dist-packages/pyLDAvis/_prepare.py:240: RuntimeWarning: divide by zero encountered in log\n",
            "  log_lift = np.log(topic_term_dists / term_proportion)\n",
            "/usr/local/lib/python3.6/dist-packages/pyLDAvis/_prepare.py:241: RuntimeWarning: divide by zero encountered in log\n",
            "  log_ttd = np.log(topic_term_dists)\n",
            "/usr/local/lib/python3.6/dist-packages/pyLDAvis/_prepare.py:257: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
            "of pandas will change to not sort by default.\n",
            "\n",
            "To accept the future behavior, pass 'sort=False'.\n",
            "\n",
            "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
            "\n",
            "  return pd.concat([default_term_info] + list(topic_dfs))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el1391398411602598883958326238\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el1391398411602598883958326238_data = {\"mdsDat\": {\"x\": [-0.0505088977546102, 0.03060244037167227, 0.06888958713509771, -0.020599633335906226, 0.0030190382278782435, -0.050417343510730746, 0.06012987686631387, 0.16226606592699022, -0.18037882890087817, 0.04916259864241501, 0.033824182976276, 0.07062140319297425, 0.08876542431315029, 0.0647099417404052, 0.05802390308832029, -0.24697236697147634, 0.08640644782307302, -0.10287494351122682, 0.14351253720787416, 0.05434465333816508, 0.1283293067455904, 0.045523495837716116, -0.1526943462894449, -0.2142595662622186, -0.1120807243636155, -0.017344252533803714], \"y\": [0.13372988976090017, -0.06657190865579991, 0.05365602311442854, 0.10575063667722215, -0.00766822769014556, 0.1392322917703034, 0.038897144648336074, -0.03736736102060932, 0.05315311497211271, 0.15750898373323408, -0.14920383178104676, 0.06861726329500956, 0.017563437654981393, -0.04787041227019546, -0.16047286395010102, -0.18450326695392827, 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\"simulator\", \"sinapyl\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"site\", \"situational_competence\", \"situational_interest\", \"situmorang\", \"sjoblom\", \"skeletal_muscle\", \"sketchfab\", \"skill\", \"skill\", \"skill\", \"skill\", \"skill\", \"skill\", \"skill\", \"skill\", \"skill\", \"skill\", \"sl\", \"sl\", \"sl\", \"slavishly\", \"slei\", \"slocum\", \"slootmaeker\", \"sls\", \"small_intestine\", \"small_laccase\", \"sn\", \"snitgen\", \"social\", \"social\", \"social\", \"social\", \"social\", \"social\", \"social\", \"social_psychology\", \"social_psychology\", \"social_psychology\", \"socialize\", \"sociocultural\", \"sodium_bicarbonate\", \"sodium_hydroxide\", \"sodium_hydroxide\", \"sodium_hydroxide\", \"soft\", \"soft\", \"soft\", \"software\", \"software\", \"software\", \"software\", \"software\", \"software\", \"software\", \"software\", \"soil\", \"solubility\", \"solubility\", \"solubility\", \"solubility\", \"solubility\", \"solubility\", \"solubility_equilibrium\", \"solubility_equilibrium\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solution\", \"solvent\", \"solvent\", \"solvent\", \"solvent\", \"solvent\", \"solvent\", \"solvent\", \"sonicating\", \"sonnert\", \"soph\", \"sotl_vol\", \"soton\", \"southampton\", \"southern\", \"sp\", \"space\", \"space\", \"space\", \"space\", \"space\", \"space\", \"space\", \"space\", \"space\", \"spatial\", \"spatial\", \"spatial_ability\", \"spear\", \"specialise\", \"speci\\ufb01c\", \"speci\\ufb01c\", \"speci\\ufb01c\", \"speci\\ufb01c\", \"speci\\ufb01c\", \"speci\\ufb01c\", \"speci\\ufb01cally\", \"speci\\ufb01cally\", \"speci\\ufb01cally\", \"speci\\ufb01cally\", \"speci\\ufb01cally\", \"speci\\ufb01cs\", \"spectra\", \"spectra\", \"spectral\", \"spectrochim\", \"spectrometer\", \"spectroscopic\", \"spectroscopic\", \"spectroscopy\", \"spectroscopy\", \"spectrum\", \"spectrum\", \"spectrum\", \"spell\", \"spie\", \"spiky\", \"spinal\", \"spry\", \"spst\", \"sqac\", \"sqac_achievement\", \"sqacs\", \"src\", \"srss\", \"staff\", \"staff\", \"staff\", \"stamp\", \"stance\", \"standseminar\", \"stankovski\", \"stanley\", \"static_image\", \"statistical\", \"statistical\", \"statistical\", \"statistical\", \"statistical\", \"statistical\", \"statistical\", \"statistical\", \"statistical\", \"statistical_signi\\ufb01cance\", \"statistically_significant\", \"statistically_significant\", \"statistically_significant\", \"statistically_significant\", \"statistically_significant\", \"statistically_signi\\ufb01cant\", \"statistically_signi\\ufb01cant\", \"statistically_signi\\ufb01cant\", \"sta\\ufb00\", \"sta\\ufb00\", \"sta\\ufb00\", \"steam\", \"steam\", \"steam\", \"steel_wool\", \"stem\", \"stem\", \"stem\", \"stem\", \"stem\", \"stem\", \"step\", \"step\", \"step\", \"step\", \"step\", \"step\", \"step\", \"step\", \"step\", \"step\", \"step\", \"step\", \"step\", \"step\", \"step\", \"stich\", \"stieff\", \"stm\", \"stoj\", \"storoni\", \"stratagene\", \"strategic\", \"strategic\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strategy\", \"strauss_corbin\", \"streptomyce\", \"stressful\", \"stressful\", \"structural\", \"structural\", \"structural\", \"structural\", \"structural\", \"structural\", \"structural\", \"structural\", \"structural\", \"structural_isomer\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"structure\", \"struvite\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"student\", \"studio\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"study\", \"stu\\ufb00\", \"style\", \"style\", \"style\", \"style\", \"style\", \"style\", \"style\", \"style\", \"subdimension\", \"subheading\", \"subject\", \"subject\", \"subject\", \"subject\", \"subject\", \"subject\", \"subject\", \"subject\", \"subject\", \"subject\", \"subject\", \"subject\", \"subject\", \"subject_matter\", \"subject_matter\", \"subject_matter\", \"subject_matter\", \"submicroscopic\", \"submicroscopic\", \"submicroscopic\", \"subscale\", \"subscale\", \"subscale\", \"subscale\", \"substance\", \"substance\", \"substance\", \"substance\", \"substance\", \"substance\", \"substituent\", \"substrate\", \"subt\", \"subtest\", \"subtest\", \"subtest\", \"subtheme\", \"suf\\ufb01ciency\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"sugar_solution\", \"suljak\", \"sultan\", \"sulzinski\", \"summon\", \"superloop\", \"superose\", \"superoxide\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support\", \"support_progression\", \"supporting_information\", \"supporting_information\", \"supporting_information\", \"suppose\", \"suppose\", \"suppose\", \"suppose\", \"supragingival\", \"supramolecular\", \"surgical\", \"survey\", \"survey\", \"survey\", \"survey\", \"survey\", \"survey\", \"survey\", \"survey\", \"survey\", \"survey\", \"sustainability\", \"sustainability\", \"sustainable\", \"sustainable\", \"sustainable\", \"suture\", \"su\\ufb03ce\", \"swap\", \"sweat\", \"swet\", \"swh\", \"swinglehurst\", \"symmetry\", \"symmetry\", \"symmetry_element\", \"symptom\", \"synopsis\", \"synopsis\", \"synopsis\", \"synthesis\", \"synthesis\", \"synthesis\", \"synthesis\", \"synthesis\", \"synthesis\", \"synthesis\", \"synthesis\", \"synthetic\", \"synthetic\", \"synthetic\", \"syringe\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"szteinberg\", \"s\\u0131n\\u0131flarda\", \"taat\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"table\", \"tail\", \"tail\", \"tail\", \"tail\", \"tail\", \"tale\", \"talk\", \"talk\", \"talk\", \"talk\", \"tami\", \"tamir\", \"tangible_user\", \"tanjong\", \"tank\", \"targus\", \"tarng\", \"tas\", \"tas\", \"tas\", \"tas\", \"tas\", \"tasar\\u0131m\", \"task\", \"task\", \"task\", \"task\", \"task\", \"task\", \"task\", \"task\", \"task\", \"task\", \"task\", \"task\", \"task\", \"tawfik\", \"tbl\", \"te\", \"te\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teach\", \"teacher\", \"teacher\", \"teacher\", \"teacher\", \"teacher\", \"teacher\", \"teacher\", \"teacher\", \"teacher_candidate\", \"teacher_canditate\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"teaching\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"team\", \"teamwork\", \"teamwork\", \"teamwork\", \"teamwork\", \"teamwork\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technique\", \"technological_acceptance\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"technology\", \"tekkaya\", \"teknigi\", \"teknik\", \"temperature\", \"temperature\", \"temperature\", \"temperature\", \"temperature\", \"temperature_gradient\", \"template\", \"template\", \"template_condition\", \"temporal\", \"temporal\", \"teratogen\", \"terre\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"test\", \"testi\\ufb01ed\", \"tetracycline\", \"text\", \"text\", \"text\", \"text\", \"text\", \"text\", \"text\", \"text\", \"text\", \"text\", \"textbox\", \"textual_representation\", \"tfa\", \"thai\", \"thai\", \"theme\", \"theme\", \"theme\", \"theme\", \"theme\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"theory\", \"therapeutic\", \"therapeutic\", \"thermodyanmic\", \"thermodynamic\", \"thermodynamic\", \"thermodynamic\", \"thermolab\", \"thermophoresis\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thing\", \"thinking\", \"thinking\", \"thinking\", \"thinking\", \"thinking\", \"thinking\", \"thinking\", \"thinking\", \"thisjournalis\", \"thisjournalis\", \"thisjournalis_publishe\", \"thisjournalis_publishe\", \"thisjournalis_published\", \"thisjournalis_published\", \"thrombin\", \"tick\", \"tien_tien\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"time\", \"timetable\", \"timetable\", \"tissue\", \"tissue\", \"tissue\", \"titles_template\", \"tjwatt\", \"tlc\", \"tlc\", \"tlc\", \"tll\", \"tobin\", \"tobin\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"tool\", \"topic\", \"topic\", \"topic\", \"topic\", \"topic\", \"topic\", \"topic\", \"topic\", \"topic\", \"topic\", \"topic\", \"topic\", \"topic\", \"topic\", \"topic\", \"topic\", \"torrent\", \"totality\", \"totem\", \"toulmin\", \"tourangeau\", \"towota\", \"trad\", \"traditional\", \"traditional\", \"traditional\", \"traditional\", \"traditional\", \"traditional\", \"traditional\", \"traditional\", \"traditional\", \"traditional\", \"training\", \"training\", \"training\", \"training\", \"training\", \"training\", \"training\", \"training\", \"training\", \"training\", \"training\", \"trajectory\", \"transcinnamate\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"transfer\", \"transfer_credit\", \"transferable\", \"transferable\", \"transformed\", \"transitioning\", \"treagust\", \"treagust\", \"treagust\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"treatment\", \"trigwell\", \"trimethoprim\", \"trinidad\", \"trioxide\", \"tru\", \"ttest\", \"tube_heat\", \"ture\", \"tutorial\", \"tutorial\", \"tutorial\", \"tutorial\", \"tutorial\", \"tutorial\", \"tutorial\", \"t\\u0131trat\\u0131on\", \"ug\", \"ugs\", \"ukzn\", \"ulisp\", \"uncharacterise\", \"undergrad\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"undergraduate\", \"underqualifie\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understand\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understanding\", \"understate\", \"undertake\", \"undertake\", \"undertake\", \"undertake\", \"undertake\", \"unguided\", \"unguided_independent\", \"uninformative\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit_operation\", \"universitat\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"university\", \"unknown\", \"unknown\", \"unknown\", \"unknown\", \"unknown\", \"unknown\", \"unknown\", \"unprepare\", \"unproblematic\", \"unreadable\", \"unsound\", \"unsw\", \"upc\", \"upk\", \"urdan\", \"ure\", \"usability\", \"usability\", \"usability\", \"usability\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user\", \"user_interface\", \"uta\", \"uv\", \"uzel\", \"uzuntiryaki\", \"vadehra\", \"valence_electron\", \"value_orientation\", \"valve\", \"vapour\", \"vapour_phase\", \"vapour_pressure\", \"variable\", \"variable\", \"variable\", \"variable\", \"variable\", \"variable\", \"variable\", \"variable\", \"variance\", \"variance\", \"variance\", \"variate\", \"vascular\", \"vee\", \"venn\", \"verage\", \"verbal\", \"verbal\", \"verbal\", \"verbal\", \"verbal\", \"verbal\", \"verbal\", \"verbal_interaction\", \"verbal_interaction\", \"verbalisation\", \"verification\", \"verification\", \"vermette\", \"veteran\", \"vibuljanc\", \"video\", \"video\", \"video_game\", \"videotape\", \"videotape\", \"videotape\", \"view\", \"view\", \"view\", \"view\", \"view\", \"view\", \"view\", \"view\", \"view\", \"view\", \"view\", \"view\", \"view\", \"view\", \"view\", \"view\", \"view\", \"view\", \"view_article\", \"view_article\", \"villalta\", \"virtual\", \"virtual\", \"virtual_chemlab\", \"virtual_glucose\", \"virtual_reality\", \"virtual_world\", \"virtualise\", \"visible_spectrometry\", \"visual\", \"visual\", \"visual\", \"visual\", \"visual_media\", \"visual_representation\", \"visualization\", \"visualization\", \"visualization\", \"visualization\", \"vlahavas\", \"vle\", \"vol\", \"vol\", \"vol\", \"vol\", \"vol\", \"vol\", \"vol\", \"vol\", \"vol\", \"vol\", \"vol\", \"vol\", \"vol\", \"volatility\", \"voltaic_cell\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volume\", \"volumetric_flask\", \"vongpatanasin\", \"vr\", \"waive\", \"walberg\", \"wale\", \"walker\", \"walker\", \"walker\", \"walker\", \"walker_sampson\", \"wanda\", \"wanna\", \"warwick\", \"warwick\", \"wass\", \"watch\", \"watch\", \"watch\", \"watch\", \"watch\", \"watching\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"wavelength\", \"wavelength\", \"wavelength\", \"weak_writer\", \"web\", \"web\", \"web\", \"web\", \"web\", \"web\", \"web\", \"web\", \"webct\", \"webct\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week_contraste\", \"weichen\", \"weidman\", \"weijer\", \"weinhem\", \"wellesley\", \"wellington\", \"wendy_se\", \"wenger\", \"wessner\", \"wheatley\", \"wiatro\", \"wigfield\", \"wiki\", \"willoughby\", \"willoughby\", \"wilson_stacy\", \"windschitl\", \"winsgw\", \"winsolgaswater\", \"winthrop\", \"wiscon\", \"wit\", \"wit\", \"withdrawal\", \"witherell\", \"wold\", \"woodfield\", \"wordy\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"work\", \"workforce\", \"workforce\", \"workforce\", \"workforce\", \"workhorse\", \"worksheet\", \"worksheet\", \"worksheet\", \"worksheet\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"write\", \"writing\", \"writing\", \"writing\", \"writing_heuristic\", \"yahoo\", \"yan\\u0131lg\\u0131lar\\u0131n\\u0131n\", \"yasemin_burhan\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"year\", \"yeast\", \"yehudit_judy\", \"yii_le\", \"ylostalo\", \"yogurt\", \"yoo\", \"yore\", \"yr\", \"zacharia\", \"zakian\", \"zappala\", \"zerwa\", \"ziane\", \"zn\", \"zohar\", \"zvia_kaberman\", \"\\u03bcm\", \"\\ufb01ll\", \"\\ufb01ll\", \"\\ufb01nal\", \"\\ufb01nal\", \"\\ufb01nal\", \"\\ufb01nal\", \"\\ufb01nal_examination\", 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            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster      Freq\n",
              "topic                                               \n",
              "6     -0.050509  0.133730       1        1  6.663063\n",
              "11     0.030602 -0.066572       2        1  6.030296\n",
              "12     0.068890  0.053656       3        1  6.008988\n",
              "24    -0.020600  0.105751       4        1  5.980645\n",
              "13     0.003019 -0.007668       5        1  5.913742\n",
              "...         ...       ...     ...      ...       ...\n",
              "5      0.045523  0.053125      22        1  2.460439\n",
              "10    -0.152694  0.042092      23        1  2.459427\n",
              "2     -0.214260 -0.106467      24        1  2.094125\n",
              "1     -0.112081  0.140784      25        1  1.970889\n",
              "7     -0.017344 -0.130605      26        1  1.750647\n",
              "\n",
              "[26 rows x 5 columns], topic_info=    Category          Freq         Term         Total  loglift  logprob\n",
              "405  Default  22563.000000   laboratory  22563.000000  30.0000  30.0000\n",
              "669  Default   7505.000000        skill   7505.000000  29.0000  29.0000\n",
              "269  Default   9147.000000   experiment   9147.000000  28.0000  28.0000\n",
              "641  Default  11678.000000      science  11678.000000  27.0000  27.0000\n",
              "106  Default  15707.000000    chemistry  15707.000000  26.0000  26.0000\n",
              "..       ...           ...          ...           ...      ...      ...\n",
              "751  Topic26    116.022724        topic   1705.195558   1.3575  -5.4354\n",
              "621  Topic26    137.017312       result   6752.717369   0.1476  -5.2691\n",
              "698  Topic26    186.741337      student  55277.242220  -1.6452  -4.9595\n",
              "252  Topic26    116.022724  environment   2558.648884   0.9517  -5.4354\n",
              "405  Topic26    110.497832   laboratory  22563.251165  -1.2739  -5.4842\n",
              "\n",
              "[2775 rows x 6 columns], token_table=       Topic      Freq        Term\n",
              "term                              \n",
              "22536     12  0.917579        aamc\n",
              "35998     23  1.010669         aas\n",
              "25294     10  1.017617  abbreviate\n",
              "25386     11  0.902137    abeyance\n",
              "1279       1  0.141596     ability\n",
              "...      ...       ...         ...\n",
              "2139      14  0.077885         ﬂow\n",
              "2139      16  0.084376         ﬂow\n",
              "2139      24  0.623084         ﬂow\n",
              "27116     13  0.908554  ﬂowability\n",
              "24268     15  0.913826     ﬂuorous\n",
              "\n",
              "[6605 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[7, 12, 13, 25, 14, 21, 22, 15, 9, 18, 20, 23, 19, 1, 4, 24, 10, 5, 17, 26, 16, 6, 11, 3, 2, 8])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IHxmms4AyRxY",
        "colab_type": "text"
      },
      "source": [
        "## Finding the dominant topic in each document"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXdzL4vMyVYG",
        "colab_type": "code",
        "outputId": "acc0f229-ef55-4a3f-fb62-66e01605cd91",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "def format_topics_sentences(ldamodel=mallet_lda_model, corpus=corpus, texts=data):\n",
        "    # Init output\n",
        "    sent_topics_df = pd.DataFrame()\n",
        "\n",
        "    # Get main topic in each document\n",
        "    for i, row in enumerate(ldamodel[corpus]):\n",
        "        row = sorted(row, key=lambda x: (x[1]), reverse=True)\n",
        "        # Get the Dominant topic, Perc Contribution and Keywords for each document\n",
        "        for j, (topic_num, prop_topic) in enumerate(row):\n",
        "            if j == 0:  # => dominant topic\n",
        "                wp = ldamodel.show_topic(topic_num)\n",
        "                topic_keywords = \", \".join([word for word, prop in wp])\n",
        "                sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)\n",
        "            else:\n",
        "                break\n",
        "    sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']\n",
        "\n",
        "    # Add original text to the end of the output\n",
        "    contents = pd.Series(texts)\n",
        "    sent_topics_df = pd.concat([sent_topics_df, contents], axis=1)\n",
        "    return(sent_topics_df)\n",
        "\n",
        "\n",
        "df_topic_sents_keywords = format_topics_sentences(ldamodel=mallet_lda_model, corpus=corpus, texts=data)\n",
        "\n",
        "# Format\n",
        "df_dominant_topic = df_topic_sents_keywords.reset_index()\n",
        "df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']\n",
        "\n",
        "# Show\n",
        "df_dominant_topic.head(10)"
      ],
      "execution_count": 161,
      "outputs": [
        {
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          "metadata": {
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          "execution_count": 161
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cHG447cT5SVp",
        "colab_type": "code",
        "outputId": "985e3169-7f69-4a84-c686-e5c90058d724",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df_dominant_topic = df_dominant_topic.sort_values(by=['Dominant_Topic'])\n",
        "df_dominant_topic.head(40)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>Chemistry Education Research and Practice PAPE...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>128</th>\n",
              "      <td>128</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.5551</td>\n",
              "      <td>student, laboratory, chemistry, science, skill...</td>\n",
              "      <td>EXPERIMENTAL ACTIVITIES IN THE LABORATORY OF A...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>337</th>\n",
              "      <td>337</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.2511</td>\n",
              "      <td>student, laboratory, chemistry, science, skill...</td>\n",
              "      <td>Chemistry Education Research and Practice PAPE...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     Document_No  ...                                               Text\n",
              "278          278  ...  Sustainability 2015, 7, 1798-1818; doi:10.3390...\n",
              "303          303  ...  International Journal of Science Education ISS...\n",
              "305          305  ...  Validity Studies Development and Psychometric ...\n",
              "167          167  ...  C O G N I T I V E P S Y C H O L O G Y 1 8 , 2 ...\n",
              "160          160  ...  Chemistry Education Research and Practice Dyna...\n",
              "310          310  ...  JOURNAL OF RESEARCH IN SCIENCE TEACHING VOL. 5...\n",
              "315          315  ...  International Journal of Science Education ISS...\n",
              "45            45  ...  Australian Journal of Teacher Education The Re...\n",
              "152          152  ...   Associations between Student Outcomes and Psy...\n",
              "322          322  ...  Chemistry Education Research and Practice Dyna...\n",
              "10            10  ...  JOURNAL OF RESEARCH IN SCIENCE TEACHING VOL. 4...\n",
              "127          127  ...  © 2007 by The International Union of Biochemis...\n",
              "118          118  ...  This article was downloaded by: [University of...\n",
              "414          414  ...  Article A Sustainability Innovation Experienti...\n",
              "145          145  ...  l D o w n o a d e d f r o m h t t p s : / / j ...\n",
              "338          338  ...  . s r e h s i l b u p d e i l l a s t i f o e ...\n",
              "47            47  ...  Learning Environ Res (2017) 20:325–338 DOI 10....\n",
              "98            98  ...                                                   \n",
              "369          369  ...  R e s e a r c h i n S c i e n c e E d u c a t ...\n",
              "357          357  ...  Motiv Emot (2014) 38:496–512 DOI 10.1007/s1103...\n",
              "87            87  ...   Frontline Learning Research Vol.4 No. 1 (2016...\n",
              "86            86  ...  Article pubs.acs.org/jchemeduc Development of ...\n",
              "258          258  ...  JOURNAL OF RESEARCH IN SCIENCE TEACHING VOL. 2...\n",
              "267          267  ...  Chemistry Education Research and Practice PAPE...\n",
              "231          231  ...  National Science Teachers Association Do High ...\n",
              "353          353  ...  Article Mentor-Mentee Interaction and Laborato...\n",
              "406          406  ...  MAGESWARY KARPUDEWAN, ZURIDA ISMAIL and WOLFF-...\n",
              "225          225  ...  JOURNAL OF RESEARCH IN SCIENCE TEACHING VOL. 1...\n",
              "71            71  ...  2 YC3 joule/ Anxiety Reduction for Beginning C...\n",
              "381          381  ...  Cite This: J. Chem. Educ. 2019, 96, 12−24 pubs...\n",
              "55            55  ...  problems of education in the 21st century Volu...\n",
              "407          407  ...  Chemistry Education Research and Practice Dyna...\n",
              "105          105  ...  Article pubs.acs.org/jchemeduc Measuring Meani...\n",
              "130          130  ...  (cid:7)(cid:194)(cid:146)(cid:157)(cid:146)(ci...\n",
              "349          349  ...  (cid:1)(cid:2)(cid:3)(cid:4)(cid:5)(cid:6)(cid...\n",
              "336          336  ...  R. Ben-Zvi, A. Hofstein, D. Samuel Science Tea...\n",
              "42            42  ...  Journal of Education and Training Studies Vol....\n",
              "116          116  ...  Chemistry Education Research and Practice PAPE...\n",
              "128          128  ...  EXPERIMENTAL ACTIVITIES IN THE LABORATORY OF A...\n",
              "337          337  ...  Chemistry Education Research and Practice PAPE...\n",
              "\n",
              "[40 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 228
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2-FR71Ipydap",
        "colab_type": "text"
      },
      "source": [
        "## Finding the most representative documents for each topic"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ptBr6_LaygPO",
        "colab_type": "code",
        "outputId": "c9959892-35d8-4367-b799-e98cbe55788b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 231
        }
      },
      "source": [
        "# Group top 5 sentences under each topic\n",
        "sent_topics_sorteddf_mallet = pd.DataFrame()\n",
        "\n",
        "sent_topics_outdf_grpd = df_topic_sents_keywords.groupby('Dominant_Topic')\n",
        "\n",
        "for i, grp in sent_topics_outdf_grpd:\n",
        "    sent_topics_sorteddf_mallet = pd.concat([sent_topics_sorteddf_mallet, \n",
        "                                             grp.sort_values(['Perc_Contribution'], ascending=[0]).head(1)], \n",
        "                                            axis=0)\n",
        "\n",
        "# Reset Index    \n",
        "sent_topics_sorteddf_mallet.reset_index(drop=True, inplace=True)\n",
        "\n",
        "# Format\n",
        "sent_topics_sorteddf_mallet.columns = ['Topic_Num', \"Topic_Perc_Contrib\", \"Keywords\", \"Text\"]\n",
        "\n",
        "# Show\n",
        "sent_topics_sorteddf_mallet.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-183-4f3274bfb4e5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0msent_topics_sorteddf_mallet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msent_topics_outdf_grpd\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_topic_sents_keywords\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Dominant_Topic'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrp\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msent_topics_outdf_grpd\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'df_topic_sents_keywords' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b48_Os0CyhQf",
        "colab_type": "text"
      },
      "source": [
        "## Topic distribution across documents"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nd89UIkVyrTE",
        "colab_type": "code",
        "outputId": "4e919b2f-13d1-4a17-f80c-340962f19cdb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "# Number of Documents for Each Topic\n",
        "topic_counts = df_topic_sents_keywords['Dominant_Topic'].value_counts()\n",
        "\n",
        "# Percentage of Documents for Each Topic\n",
        "topic_contribution = round(topic_counts/topic_counts.sum(), 4)\n",
        "\n",
        "# Topic Number and Keywords\n",
        "topic_num_keywords = df_topic_sents_keywords[['Dominant_Topic', 'Topic_Keywords']]\n",
        "\n",
        "# Concatenate Column wise\n",
        "df_dominant_topics = pd.concat([topic_num_keywords, topic_counts, topic_contribution], axis=1)\n",
        "\n",
        "# Change Column names\n",
        "df_dominant_topics.columns = ['Dominant_Topic', 'Topic_Keywords', 'Num_Documents', 'Perc_Documents']\n",
        "\n",
        "# Show\n",
        "df_dominant_topics"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dominant_Topic</th>\n",
              "      <th>Topic_Keywords</th>\n",
              "      <th>Num_Documents</th>\n",
              "      <th>Perc_Documents</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>9.0</td>\n",
              "      <td>child, game, student, make, class, day, time, ...</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0.0280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>8.0</td>\n",
              "      <td>engineering, education, refer, financial, engi...</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0144</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>4.0</td>\n",
              "      <td>student, study, question, learn, work, knowled...</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.0208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>31.0</td>\n",
              "      <td>number, sequence, solution, give, system, meth...</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0.0200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>9.0</td>\n",
              "      <td>child, game, student, make, class, day, time, ...</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.0319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1247.0</th>\n",
              "      <td>7.0</td>\n",
              "      <td>item, sample, probability, response, test, lev...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1248.0</th>\n",
              "      <td>28.0</td>\n",
              "      <td>function, graph, equation, point, change, curv...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1249.0</th>\n",
              "      <td>19.0</td>\n",
              "      <td>datum, graph, type, statistical, data, level, ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1250.0</th>\n",
              "      <td>13.0</td>\n",
              "      <td>teacher, school, assessment, curriculum, stand...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1251.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>school, student, language, teacher, education,...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1252 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Dominant_Topic  ... Perc_Documents\n",
              "0.0                9.0  ...         0.0280\n",
              "1.0                8.0  ...         0.0144\n",
              "2.0                4.0  ...         0.0208\n",
              "3.0               31.0  ...         0.0200\n",
              "4.0                9.0  ...         0.0319\n",
              "...                ...  ...            ...\n",
              "1247.0             7.0  ...            NaN\n",
              "1248.0            28.0  ...            NaN\n",
              "1249.0            19.0  ...            NaN\n",
              "1250.0            13.0  ...            NaN\n",
              "1251.0             1.0  ...            NaN\n",
              "\n",
              "[1252 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    }
  ]
}