ThaiCoNet_Pipeline.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7hyP0ENtMBXe"
      },
      "source": [
        "# 🌐 ThaiCoNet : Thai Co-occurrence Network Analysis (Pipeline)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YoM1WUocVTq4"
      },
      "source": [
        "### 💡 About this notebook"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "🚧 pipeline_version : ```beta 0.0.6```"
      ],
      "metadata": {
        "id": "-HJRz5Qn-qld"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fIQxNntvCfz0"
      },
      "source": [
        "**[Access Project Repository (Github)](https://github.com/ChotanansubSoph/ThNTA)**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LHlGa5IW7XoQ"
      },
      "source": [
        "##### 🧑🏻‍💻 **Notebook Contributor**\n",
        "\n",
        "\n",
        "*   Chotanansub Sophaken, [Github](https://github.com/ChotanansubSoph)\n",
        "*   Kantapong Vongpanich, [Github](https://github.com/OnlyJust3rd)\n",
        "\n",
        "  > 🏢 Department of Computer Eningeering, King Mongkut’s University of Technology Thonburi (KMUTT)\n",
        "\n",
        "  > 🌱 Junior Science Talent Project by Siam Commercial Bank Scholarship (JSTP-SCB Scholarship)\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "svSvi0jVCS4V"
      },
      "source": [
        "##### **📚 Approach Based**\n",
        "\n",
        "* *A. Takhom, D. Leenoi, C. Sophaken, P. Boonkwan, and T. Supnithi, “An Approach of Network Analysis Enhancing Knowledge Extraction in Thai Newspapers Contexts,” J. Intell. Informatics Smart Technol., vol. 6, no. October 2021, pp. 19–24, 2021 [Acess](https://jiist.aiat.or.th/assets/uploads/1635853027829tBupD1635602106085fdegH39.pdf)*\n",
        "\n",
        "* Sophaken, C., Vongpanich, K., Takhom, A., Boonkwan, P., & Supnithi, T. (2023). Unsupervised Detection of Domain Switching in Thai Multidisciplinary Online News. IIAI Letters on Informatics and Interdisciplinary Research, 3. [Access](https://iaiai.org/letters/index.php/liir/article/view/77/50)\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i24UbKCMUOYs"
      },
      "source": [
        "#### **🎓 Acknowledgement**\n",
        "* Akkharawoot Takhom, PhD.\n",
        "  \n",
        "  > 🏢 Department of Electrical and Computer Engineering,\n",
        "Thammasat University\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NrFZelkYVWIX"
      },
      "source": [
        "### ⚙️ Tools & Resorces Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vl76iryjU3Va"
      },
      "source": [
        "Library & Module Installation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IuOPqC6eEh7a"
      },
      "source": [
        "\n",
        "\n",
        "* 🏃 Run the code once.\n",
        "* ⚠️ If you encounter any errors during the initial execution it may be due to factors such as dependencies or system configurations. To address any encountered errors, simply restart the runtime or kernel. Afterward, run the code cell again to ensure a successful execution.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "p1lzOz2OMtvl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "referenced_widgets": [
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            "0ad35c680eaa4a2f848e921a9f47a9c2",
            "feab5ca028944860b06cf136db550ef8",
            "09db2ce421bf482d822d26ca72f61678",
            "1744254860ba4397b987d766c42c6164",
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          ],
          "height": 315
        },
        "outputId": "72c9d20e-35e1-4d7c-a871-32a91ab14b18"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/9 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8c7d64169a65427c945c77bebf7775e2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[33m[ThaiCoNet]\u001b[0m upgrade-pip : \u001b[32mDownloaded\u001b[0m ☑️ \n",
            "\u001b[33m[ThaiCoNet]\u001b[0m wheel : \u001b[32mDownloaded\u001b[0m ☑️ \n",
            "\u001b[33m[ThaiCoNet]\u001b[0m tltk : \u001b[32mDownloaded\u001b[0m ☑️ \n",
            "\u001b[33m[ThaiCoNet]\u001b[0m longan : \u001b[32mDownloaded\u001b[0m ☑️ \n",
            "\u001b[33m[ThaiCoNet]\u001b[0m deepcut : \u001b[32mDownloaded\u001b[0m ☑️ \n",
            "\u001b[33m[ThaiCoNet]\u001b[0m pythainlp : \u001b[32mDownloaded\u001b[0m ☑️ \n",
            "\u001b[33m[ThaiCoNet]\u001b[0m pyvis : \u001b[32mDownloaded\u001b[0m ☑️ \n",
            "\u001b[33m[ThaiCoNet]\u001b[0m graphviz : \u001b[32mDownloaded\u001b[0m ☑️ \n",
            "\u001b[33m[ThaiCoNet]\u001b[0m pygraphviz : \u001b[32mDownloaded\u001b[0m ☑️ \n",
            "\n",
            "----------------------------------------------------------------------\n",
            "\u001b[33m[ThaiCoNet]\u001b[0m All required packages \u001b[32mhave completely installed\u001b[0m ✅ \n",
            "----------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        }
      ],
      "source": [
        "import subprocess\n",
        "import sys\n",
        "import pip\n",
        "\n",
        "from google.colab import runtime\n",
        "from tqdm.notebook import tqdm_notebook as tqdm\n",
        "\n",
        "\n",
        "REQUIRED_MODULES = {\n",
        "    'upgrade-pip' : ['pip', 'install', '--upgrade', 'pip'],\n",
        "    'wheel' : ['pip', 'install', '--upgrade', 'setuptools', 'wheel'],\n",
        "    'tltk' : ['pip', 'install', 'tltk==1.6.8', '-q'],\n",
        "    'longan' : ['pip', 'install', 'longan','--extra-index-url', 'https://installer:glpat-dDG6MBuvUjUKWymz5uBu@gitlab.com/api/v4/projects/35051317/packages/pypi/simple'],\n",
        "    'deepcut' : ['pip', 'install', 'deepcut==0.7.0.0', '-q'],\n",
        "    'pythainlp' : ['pip', 'install', 'pythainlp==4.0.2', '-q'],\n",
        "    'pyvis' : ['pip', 'install', 'pyvis==0.1.9', '-q'],\n",
        "    'graphviz' : ['apt-get', 'install', '-y', 'graphviz', 'libgraphviz-dev', 'pkg-config', '-q'],\n",
        "    'pygraphviz' : ['pip', 'install', 'pygraphviz==1.7', '-q'],\n",
        "}\n",
        "\n",
        "def str_color(text, color):\n",
        "    colors = {'black': '\\033[30m','red': '\\033[31m', 'green': '\\033[32m','yellow': '\\033[33m','blue': '\\033[34m', 'purple': '\\033[35m','cyan': '\\033[36m','white': '\\033[37m', 'reset': '\\033[0m'}\n",
        "    return f\"{colors[color]}{text}{colors['reset']}\"\n",
        "\n",
        "def install_colab_packages(lib_dicts,excepts=[]):\n",
        "    lib_lists  = [lib for lib in list(lib_dicts.keys()) if lib not in excepts]\n",
        "    for lib in tqdm(lib_lists):\n",
        "      try:\n",
        "         subprocess.check_call(REQUIRED_MODULES[lib])\n",
        "         print(f'{str_color(\"[ThaiCoNet]\",\"yellow\")} {lib} : {str_color(\"Downloaded\",\"green\")} ☑️ ')\n",
        "      except:\n",
        "        print(f\"\\n{'-'*70}\\n{str_color('[ThaiCoNet]', 'yellow')} {lib} : ⚠️ {str_color('failed to download!', 'red')}\\n🔄 please reconnection and try again\\n{'-'*70}\")\n",
        "        print('\\n')\n",
        "        runtime.unassign()\n",
        "\n",
        "def setup(notebook = \"colab\", excepts=[]):\n",
        "    if notebook == \"colab\":\n",
        "        install_colab_packages(REQUIRED_MODULES,excepts)\n",
        "\n",
        "    #Data manipulation\n",
        "    global pd,np\n",
        "    import pandas as pd\n",
        "    import numpy as np\n",
        "\n",
        "    #NLP\n",
        "    global nltk,tltk,longan,deepcut\n",
        "    global FreqDist,bigrams,term_tokenize\n",
        "    global pythainlp_word_tokenize, pythainlp_pos_tag, pythainlp_stopwords\n",
        "    global Dictionary, TfidfModel\n",
        "    import nltk\n",
        "    from nltk import FreqDist, bigrams\n",
        "    from nltk.tokenize import word_tokenize as term_tokenize\n",
        "    nltk.download('punkt')\n",
        "\n",
        "    if 'tltk' not in excepts:\n",
        "      import tltk\n",
        "    if 'pythainlp' not in excepts:\n",
        "      from pythainlp import word_tokenize as pythainlp_word_tokenize\n",
        "      from pythainlp import pos_tag as pythainlp_pos_tag\n",
        "      from pythainlp.corpus.common import thai_stopwords as pythainlp_stopwords\n",
        "    if 'longan' not in excepts:\n",
        "      import longan\n",
        "    if 'deepcut' not in excepts:\n",
        "      import deepcut\n",
        "\n",
        "    from gensim.corpora import Dictionary\n",
        "    from gensim.models import TfidfModel\n",
        "\n",
        "    #Graph Visulazation\n",
        "    global nx, plt, Network, display, HTML\n",
        "    import networkx as nx\n",
        "    import matplotlib.pyplot as plt\n",
        "    from pyvis.network import Network\n",
        "    from IPython.display import display, HTML\n",
        "\n",
        "    #Add-on\n",
        "    global itemgetter, Counter, defaultdict, islic, re, os, requests\n",
        "    from operator import itemgetter\n",
        "    from collections import Counter,defaultdict\n",
        "    from itertools import islice\n",
        "    import re\n",
        "    import os\n",
        "    import requests\n",
        "\n",
        "    print(f'\\n{\"-\"*70}\\n{str_color(\"[ThaiCoNet]\", \"yellow\")} All required packages {str_color(\"have completely installed\", \"green\")} ✅ \\n{\"-\"*70}')\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\" and 'ipykernel' in sys.modules:\n",
        "  __is_ipython_kernel__ = True\n",
        "else:\n",
        "  __is_ipython_kernel__ = False\n",
        "\n",
        "def __sample_setup__():\n",
        "  if __is_ipython_kernel__:\n",
        "    setup()\n",
        "    import requests\n",
        "\n",
        "__sample_setup__()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4bS_VdiSUxTl"
      },
      "source": [
        "Sample Resources prepararion"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "OL3_JQ_JJZvM"
      },
      "outputs": [],
      "source": [
        "#sample data\n",
        "def download_data(url, file_name=None):\n",
        "    if file_name is None:\n",
        "        file_name = url.split('/')[-1]\n",
        "    response = requests.get(url)\n",
        "    with open(file_name, \"wb\") as file:\n",
        "        file.write(response.content)\n",
        "\n",
        "def notebook_download_sample_data():\n",
        "  if __is_ipython_kernel__:\n",
        "      url = \"https://github.com/ChotanansubSoph/ThNTA/raw/main/resources/sample_data/thai_electronic_news_2022.csv\"\n",
        "      download_data(url=url)\n",
        "\n",
        "notebook_download_sample_data()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZXC6zQeoVMPX"
      },
      "source": [
        "### 🤖 Methods"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "vrgp_bEE0THS"
      },
      "outputs": [],
      "source": [
        "########## String operation ##########\n",
        "def isEnglish(s):\n",
        "  return all(ord(char) < 128 for char in s)\n",
        "\n",
        "\n",
        "########## List Manipulation ##########\n",
        "def flatten_nested_list(nested_list):\n",
        "  flattened_list = [item for sublist in nested_list for item in sublist]\n",
        "  return flattened_list\n",
        "\n",
        "\n",
        "######### DataFrame Manipulation #######\n",
        "def convert_dataframe_to_paired_tuples(df):\n",
        "    return list(zip(df.iloc[:, 0].tolist(), df.iloc[:, 1].tolist()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "13JMUAbXMvM2"
      },
      "outputs": [],
      "source": [
        "########## Stopwords ##########\n",
        "def read_stopwords(file_path : str) ->list:\n",
        "  with open(file_path, 'r', encoding='utf-8') as file:\n",
        "      lines = file.readlines()\n",
        "  stopwords = [line.strip() for line in lines]\n",
        "  return stopwords\n",
        "\n",
        "########## Tokenization ##########\n",
        "def pythainlp_tokenize_pos(text): #Secondary Tokenizer\n",
        "  wordList= pythainlp_word_tokenize(text, keep_whitespace=False)\n",
        "  posList = pythainlp_pos_tag(wordList)\n",
        "  return posList\n",
        "\n",
        "\n",
        "def TNC_extract_tltk_pos_pairs(result): #Inactivated\n",
        "    word_pos_pairs = []\n",
        "    pattern = r'<w tran=\"(.*?)\" POS=\"(.*?)\">(.*?)</w>'\n",
        "    matches = re.findall(pattern, result)\n",
        "\n",
        "    for match in matches:\n",
        "        word_pos_pairs.append((match[2], match[1]))\n",
        "\n",
        "    return word_pos_pairs\n",
        "\n",
        "# def TNC_tokenize_pos_ner_(text): #Inactivated\n",
        "#   result = []\n",
        "#   for partial_text in text.split(\" \"):\n",
        "#     partial_text = partial_text.replace(\")\",\" \").replace(\"(\",\" \")\n",
        "#     result += tltk.nlp.TNC_tag(partial_text,POS=\"Y\")\n",
        "#   return tltk.nlp.ner(TNC_extract_tltk_pos_pairs(result))\n",
        "\n",
        "\n",
        "########## Term Frequency ##########\n",
        "def count_word_frequency(pairs_data):\n",
        "    words = [word for sublist in pairs_data for word in sublist]\n",
        "    tokens = term_tokenize(\" \".join(words))\n",
        "    freq_dist = FreqDist(tokens)\n",
        "    return freq_dist\n",
        "\n",
        "\n",
        "def count_word_pos_frequency(data):\n",
        "    flat_data = [item for sublist in data for item in sublist]\n",
        "    word_pos_freq = Counter(flat_data)\n",
        "    result = [(word, pos, freq) for (word, pos), freq in word_pos_freq.items()]\n",
        "    result.sort(key=lambda x: x[2], reverse=True)\n",
        "    return result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DivqqP4z41S0"
      },
      "source": [
        "### 📝 Text preprocess"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2xED09ggfmL"
      },
      "source": [
        "Tokenization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "h0nxLPWvgeKc"
      },
      "outputs": [],
      "source": [
        "def text_tokenize(text: str,tokenizer=\"pythainlp\") -> list:\n",
        "    term_list = list()\n",
        "    if tokenizer == \"tltk\" or tokenizer == \"tltk-colloc\":\n",
        "      term_list = tltk.nlp.word_segment(text).split(\"|\")\n",
        "    elif tokenizer == \"tltk-mm\" or tokenizer == \"tltk-ngram\":\n",
        "      term_list = tltk.nlp.word_segment(text, method=\"mm\").split(\"|\")\n",
        "    elif tokenizer == \"tltk-w2v\":\n",
        "      term_list = tltk.nlp.word_segment(text, method=\"w2v\").split(\"|\")\n",
        "\n",
        "    elif tokenizer == \"pythainlp\" or tokenizer == \"newmm\":\n",
        "      term_list = pythainlp_word_tokenize(text)\n",
        "\n",
        "    elif tokenizer == \"deepcut\":\n",
        "      term_list = deepcut.tokenize(text)\n",
        "\n",
        "    elif tokenizer ==\"longan\":\n",
        "      term_list = longan.tokenize(text)\n",
        "\n",
        "    return term_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EAV5KgVNhKRn",
        "outputId": "57a815da-0626-4177-ead8-6eacf66a56a3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenizer Test\n",
            " ------------------------------------------------------------\n",
            "tltk  ['ประกาศ', 'ให้', 'มี', 'การ', 'สวม', 'หน้ากาก', 'อนามัย', 'ตลอด', 'เวลา', '<s/>']\n",
            "tltk-mm  ['ประกาศ', 'ให้', 'มี', 'การ', 'สวม', 'หน้ากาก', 'อนามัย', 'ตลอด', 'เวลา', '<s/>']\n",
            "tltk-w2v  ['', '<s/>']\n",
            "pythainlp (newmm)  ['ประกาศ', 'ให้', 'มี', 'การ', 'สวมหน้ากาก', 'อนามัย', 'ตลอดเวลา']\n",
            "2/2 [==============================] - 0s 18ms/step\n",
            "deepcut  ['ประกาศ', 'ให้', 'มี', 'การ', 'สวม', 'หน้ากาก', 'อนามัย', 'ตลอด', 'เวลา']\n",
            "longan  ['ประกาศ', 'ให้', 'มีการ', 'สวม', 'หน้ากาก', 'อนามัย', 'ตลอด', 'เวลา']\n"
          ]
        }
      ],
      "source": [
        "def __test_tokenize__():\n",
        "  if __is_ipython_kernel__:\n",
        "    sample_text = \"ประกาศให้มีการสวมหน้ากากอนามัยตลอดเวลา\"\n",
        "    print(\"Tokenizer Test\\n\",\"-\"*60)\n",
        "    print(\"tltk \",text_tokenize(sample_text,\"tltk\"))\n",
        "    print(\"tltk-mm \",text_tokenize(sample_text,\"tltk-mm\"))\n",
        "    print(\"tltk-w2v \",text_tokenize(sample_text,\"tltk-w2v\"))\n",
        "    print(\"pythainlp (newmm) \",text_tokenize(sample_text,\"pythainlp\"))\n",
        "    print(\"deepcut \",text_tokenize(sample_text,\"deepcut\"))\n",
        "    print(\"longan \",text_tokenize(sample_text,\"longan\"))\n",
        "\n",
        "__test_tokenize__()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CDtPwYSZgiw0"
      },
      "source": [
        "POS Tagging"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "R1NWkKgkgl0H"
      },
      "outputs": [],
      "source": [
        "def pos_tagging(term_list, pos_tagger):\n",
        "  term_pairs = list()\n",
        "  if pos_tagger == \"tltk\" or pos_tagger == \"tltk-pos-tagger\":\n",
        "    term_pairs =  tltk.pos_tag_wordlist(term_list)\n",
        "  elif pos_tagger == \"pythainlp\" or pos_tagger == \"pythainlp-pos-tagger\":\n",
        "    term_pairs = pythainlp_pos_tag(term_list)\n",
        "  elif pos_tagger == \"pythainlp-pud\":\n",
        "    term_pairs = pythainlp_pos_tag(term_list,corpus=\"pud\")\n",
        "  elif pos_tagger == \"longan\":\n",
        "    pos_list = longan.pos(term_list)\n",
        "    term_pairs = list(map(lambda term, pos: (term,pos), term_list, pos_list))\n",
        "\n",
        "  return term_pairs"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def __sample_pos_tagging__():\n",
        "    if __is_ipython_kernel__:\n",
        "      print(\"POS Tagging Test\\n\",\"-\"*60)\n",
        "      print(pos_tagging(['ประกาศ', 'ให้', 'มี', 'การ', 'สวม', 'หน้ากาก', 'อนามัย', 'ตลอด', 'เวลา', '<s/>'],\"tltk\"))\n",
        "__sample_pos_tagging__()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHS6vLLxFx_4",
        "outputId": "4195ad0f-80ba-403c-94d7-fda2ee87d4aa"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "POS Tagging Test\n",
            " ------------------------------------------------------------\n",
            "[('ประกาศ', 'VERB'), ('ให้', 'SCONJ'), ('มี', 'VERB'), ('การ', 'NOUN'), ('สวม', 'VERB'), ('หน้ากาก', 'NOUN'), ('อนามัย', 'NOUN'), ('ตลอด', 'ADV'), ('เวลา', 'NOUN'), ('<s/>', 'PUNCT')]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZbUO2nsugoHP"
      },
      "source": [
        "Token Filtering"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "o_F--DzDYTPA"
      },
      "outputs": [],
      "source": [
        "def token_filter(pos_pairs: list, stopwords: set, keep_pos=[]):\n",
        "  stopwords = []\n",
        "  regex = re.compile('[@_!#$%^&*()<>?/\\|}{~:.]')\n",
        "\n",
        "  if len(keep_pos) > 0:\n",
        "    pos_condition = lambda pos: True if pos in keep_pos else False\n",
        "  else:\n",
        "    pos_condition = lambda pos:True\n",
        "\n",
        "\n",
        "  filtered_pairs = [(term,pos) for term, pos in pos_pairs\n",
        "              if pos_condition(pos)\n",
        "              and term not in stopwords\n",
        "              and len(term) > 1\n",
        "              and not isEnglish(term)\n",
        "              and regex.search(term) is None\n",
        "              and \"\\xa0\" not in term]\n",
        "\n",
        "  return filtered_pairs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKdtg9mNgy4z"
      },
      "source": [
        "Warp-up Token prepairation process"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "fBc-rFPVgyIz"
      },
      "outputs": [],
      "source": [
        "def feed_preprocess(docs: list, stopwords = None, tokenizer=\"deepcut\",pos_tagger = \"tltk\",keep_pos=['NOUN','VERB'], is_filter = True, is_pos = True, is_tokenize=True) -> list:\n",
        "    preprocessed_docs = []\n",
        "\n",
        "    if stopwords is None:\n",
        "      stopwords = pythainlp_stopwords()\n",
        "\n",
        "    for text in tqdm(docs):\n",
        "\n",
        "        if tokenizer==\"pythainlp\" and pos_tagger==\"pythainlp\":\n",
        "          pos_pairs = pythainlp_tokenize_pos(text)\n",
        "\n",
        "        else:\n",
        "\n",
        "          #Tokenization\n",
        "          term_list = text\n",
        "          if is_tokenize:\n",
        "            term_list = text_tokenize(\n",
        "                          text=term_list,\n",
        "                          tokenizer=tokenizer,\n",
        "                        )\n",
        "\n",
        "          #POS Tagger\n",
        "          pos_pairs = term_list\n",
        "          if is_pos:\n",
        "            pos_pairs = pos_tagging(pos_pairs,pos_tagger)\n",
        "\n",
        "          #Token Filtering\n",
        "          preprocessed_terms = pos_pairs\n",
        "          if is_filter:\n",
        "            preprocessed_terms = token_filter(pos_pairs = preprocessed_terms,\n",
        "                                            stopwords = stopwords,\n",
        "                                            keep_pos = keep_pos\n",
        "                                            )\n",
        "\n",
        "          preprocessed_docs.append(preprocessed_terms)\n",
        "\n",
        "    return preprocessed_docs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oVo47ASqSs3J"
      },
      "source": [
        "Tokenization & Token Filtering Demonstration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "UR-qwbCYSwEa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "7c7ea473-8a54-40b9-8910-36f4aa3cc9b4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       domain                                            content\n",
              "0     politic  กรมราชทัณฑ์ ชี้แจง ต่อข้อสงสัย กรณีการนำตัวนาย...\n",
              "1     politic  พลเอก ประยุทธ์ จันทร์โอชา นายกรัฐมนตรีและรัฐมน...\n",
              "2     politic  นางสาวคุ้มเกล้า ส่งสมบูรณ์ ทนายความศูนย์ทนายคว...\n",
              "3     politic  วันที่ (1 พฤษภาคม 2564) เวลา 11.00 น. นายสิธิช...\n",
              "4     politic  ด่านคัดกรองคนเข้าเมืองยะลา ยกระดับคุมเข้ม ป้อง...\n",
              "...       ...                                                ...\n",
              "8976  culture  สำนักงานวัฒนธรรมจังหวัดสตูล ลงพื้นที่จัดเก็บข้...\n",
              "8977  culture  จังหวัดศรีสะเกษ โดย พช.ห้วยทับทัน จัดกิจกรรมเอ...\n",
              "8978  culture  นายชีวันธร พิศพนาวัน จากโครงการ U2T ตำบลห้วยห้...\n",
              "8979  culture  วันนี้ (24 พ.ย. 64) ที่ หอประชุม 100 ปี โรงเรี...\n",
              "8980  culture  นางพวงคำ ทองทั่ว วัฒนธรรมจังหวัดยโสธร เปิดเผยว...\n",
              "\n",
              "[8981 rows x 2 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>domain</th>\n",
              "      <th>content</th>\n",
              "    </tr>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>politic</td>\n",
              "      <td>กรมราชทัณฑ์ ชี้แจง ต่อข้อสงสัย กรณีการนำตัวนาย...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>politic</td>\n",
              "      <td>พลเอก ประยุทธ์ จันทร์โอชา นายกรัฐมนตรีและรัฐมน...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>politic</td>\n",
              "      <td>นางสาวคุ้มเกล้า ส่งสมบูรณ์ ทนายความศูนย์ทนายคว...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>politic</td>\n",
              "      <td>วันที่ (1 พฤษภาคม 2564) เวลา 11.00 น. นายสิธิช...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>politic</td>\n",
              "      <td>ด่านคัดกรองคนเข้าเมืองยะลา ยกระดับคุมเข้ม ป้อง...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8976</th>\n",
              "      <td>culture</td>\n",
              "      <td>สำนักงานวัฒนธรรมจังหวัดสตูล ลงพื้นที่จัดเก็บข้...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8977</th>\n",
              "      <td>culture</td>\n",
              "      <td>จังหวัดศรีสะเกษ โดย พช.ห้วยทับทัน จัดกิจกรรมเอ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8978</th>\n",
              "      <td>culture</td>\n",
              "      <td>นายชีวันธร พิศพนาวัน จากโครงการ U2T ตำบลห้วยห้...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8979</th>\n",
              "      <td>culture</td>\n",
              "      <td>วันนี้ (24 พ.ย. 64) ที่ หอประชุม 100 ปี โรงเรี...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8980</th>\n",
              "      <td>culture</td>\n",
              "      <td>นางพวงคำ ทองทั่ว วัฒนธรรมจังหวัดยโสธร เปิดเผยว...</td>\n",
              "    </tr>\n",
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              "<p>8981 rows × 2 columns</p>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "  </style>\n",
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              "        document.querySelector('#df-ca4e5d1a-f81e-4622-a7f1-afe956d584db button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ed97ff89-3c73-408b-abbd-41ea906860f5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "def __sample_load_data__():\n",
        "  if __is_ipython_kernel__:\n",
        "    global sample_data\n",
        "    sample_data = pd.read_csv(\"thai_electronic_news_2022.csv\")\n",
        "    display(sample_data)\n",
        "\n",
        "__sample_load_data__()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "fa0ad89d7f884b49803b7f71b8c8839a",
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            "5ee2ac5c060d44cfb8e5e2d74092bc10",
            "ca6b2df607434241b9cfb275fc9f0868"
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        },
        "id": "LvT9e4CBWKPf",
        "outputId": "2d6dade7-afa6-42f4-ed8d-16ed2fdcc27a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/8981 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fa0ad89d7f884b49803b7f71b8c8839a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "def __sample_feed_process__():\n",
        "  if __is_ipython_kernel__:\n",
        "    global sample_tokenized_data\n",
        "    sample_tokenized_data = feed_preprocess(docs=sample_data[\"content\"],\n",
        "                                    tokenizer=\"pythainlp\",\n",
        "                                    pos_tagger=\"tltk-pos-tagger\",\n",
        "                                    keep_pos=[])\n",
        "    #approximate time : pythainlp ~2.17 min / tltk ~ min /longan\n",
        "__sample_feed_process__()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "cMt_9gb_q8Fd",
        "outputId": "a0611306-3c0f-41a0-c523-3cf7254349d1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[('กรมราชทัณฑ์', 'NOUN'),\n",
              " ('ชี้แจง', 'VERB'),\n",
              " ('ต่อ', 'ADP'),\n",
              " ('ข้อสงสัย', 'NOUN'),\n",
              " ('กรณี', 'NOUN'),\n",
              " ('การนำ', 'NOUN'),\n",
              " ('ตัว', 'NOUN'),\n",
              " ('นาย', 'NOUN'),\n",
              " ('พริษฐ์', 'PROPN'),\n",
              " ('ชิ', 'VERB')]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[('พลเอก', 'NOUN'),\n",
              " ('ประยุทธ์', 'NOUN'),\n",
              " ('จันทร์', 'PROPN'),\n",
              " ('โอชา', 'NOUN'),\n",
              " ('นายกรัฐมนตรี', 'NOUN'),\n",
              " ('และ', 'CCONJ'),\n",
              " ('รัฐมนตรี', 'NOUN'),\n",
              " ('ว่าการ', 'VERB'),\n",
              " ('กระทรวงกลาโหม', 'NOUN'),\n",
              " ('มี', 'VERB')]"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "def __sample_show_tokenized_data__():\n",
        "  if __is_ipython_kernel__:\n",
        "    display(sample_tokenized_data[0][:10])\n",
        "    display(sample_tokenized_data[1][:10])\n",
        "\n",
        "__sample_show_tokenized_data__()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def flat_pos_list(pos_list):\n",
        "  result = list()\n",
        "  for doc in pos_list:\n",
        "    collect = []\n",
        "    for pair in doc:\n",
        "      collect.append(pair[0]+\"|\"+pair[1])\n",
        "    result.append(collect)\n",
        "  return result\n",
        "\n",
        "def __sample_show_flated_pos_list__():\n",
        "  if __is_ipython_kernel__:\n",
        "    global flated_pos_list\n",
        "    flated_pos_list = flat_pos_list(sample_tokenized_data[:2])\n",
        "    display(flated_pos_list[0][:15])\n",
        "\n",
        "__sample_show_flated_pos_list__()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "8cQ8XKOYm-hH",
        "outputId": "205a937f-6394-4cb7-f274-e7602307d1c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "['กรมราชทัณฑ์|NOUN',\n",
              " 'ชี้แจง|VERB',\n",
              " 'ต่อ|ADP',\n",
              " 'ข้อสงสัย|NOUN',\n",
              " 'กรณี|NOUN',\n",
              " 'การนำ|NOUN',\n",
              " 'ตัว|NOUN',\n",
              " 'นาย|NOUN',\n",
              " 'พริษฐ์|PROPN',\n",
              " 'ชิ|VERB',\n",
              " 'วา|NOUN',\n",
              " 'รักษ์|NOUN',\n",
              " 'หรือ|CCONJ',\n",
              " 'เพนกวิน|NOUN',\n",
              " 'เข้า|VERB']"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def calculate_tfidf(documents):\n",
        "    # Create a dictionary from the documents\n",
        "    dictionary = Dictionary(documents)\n",
        "\n",
        "    # Create a bag-of-words representation for the documents\n",
        "    corpus = [dictionary.doc2bow(doc) for doc in documents]\n",
        "\n",
        "    # Create a TF-IDF model from the bag-of-words corpus\n",
        "    tfidf_model = TfidfModel(corpus)\n",
        "\n",
        "    # Compute TF-IDF values for the documents\n",
        "    tfidf_values = [tfidf_model[doc] for doc in corpus]\n",
        "\n",
        "    # Convert TF-IDF values to dictionaries\n",
        "    tfidf_list = []\n",
        "    for doc in tfidf_values:\n",
        "        tfidf_dict = {dictionary[id]: value for id, value in doc}\n",
        "        tfidf_list.append(tfidf_dict)\n",
        "\n",
        "    return tfidf_list\n",
        "\n",
        "def __sample_show_tfidf_pairs__():\n",
        "  if __is_ipython_kernel__:\n",
        "    global tfidf_pairs\n",
        "    tfidf_pairs = calculate_tfidf(flated_pos_list)\n",
        "    display(dict(list(islice(tfidf_pairs[0].items(), 15))))\n",
        "\n",
        "__sample_show_tfidf_pairs__()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "-K97BJR5nFT7",
        "outputId": "42f50404-ab43-477f-f359-50dfe0cf46bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'กรณี|NOUN': 0.03826393658966905,\n",
              " 'กรด|NOUN': 0.03826393658966905,\n",
              " 'กรมราชทัณฑ์|NOUN': 0.11479180976900714,\n",
              " 'กระเพาะ|NOUN': 0.03826393658966905,\n",
              " 'กระเพาะอาหาร|NOUN': 0.0765278731793381,\n",
              " 'กรุงเทพ|PROPN': 0.03826393658966905,\n",
              " 'กรุงเทพมหานคร|PROPN': 0.03826393658966905,\n",
              " 'การชี้แจง|NOUN': 0.03826393658966905,\n",
              " 'การตรวจรักษา|NOUN': 0.03826393658966905,\n",
              " 'การนำ|NOUN': 0.03826393658966905,\n",
              " 'การรักษา|NOUN': 0.19131968294834525,\n",
              " 'การวินิจฉัย|NOUN': 0.03826393658966905,\n",
              " 'การวินิจฉัย|VERB': 0.03826393658966905,\n",
              " 'การเพิ่มเติม|VERB': 0.03826393658966905,\n",
              " 'ข้อมูล|NOUN': 0.0765278731793381}"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "tbS7SwvTSN2S",
        "outputId": "cb335fbb-18fa-4d99-a308-98a29c9b8471"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[('และ', 'CCONJ', 63909),\n",
              " ('ใน', 'ADP', 45562),\n",
              " ('การ', 'NOUN', 40154),\n",
              " ('ที่', 'SCONJ', 34184),\n",
              " ('มี', 'VERB', 29901),\n",
              " ('จังหวัด', 'NOUN', 26955),\n",
              " ('ของ', 'ADP', 25631),\n",
              " ('เป็น', 'VERB', 20936),\n",
              " ('ให้', 'SCONJ', 17712),\n",
              " ('พื้นที่', 'NOUN', 14963),\n",
              " ('เพื่อ', 'SCONJ', 14291),\n",
              " ('ได้', 'AUX', 13515),\n",
              " ('จาก', 'ADP', 12788),\n",
              " ('โดย', 'SCONJ', 12157),\n",
              " ('อำเภอ', 'NOUN', 11726)]"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "def __sample_freq_detection__():\n",
        "  if __is_ipython_kernel__:\n",
        "    global sample_tokenized_freq\n",
        "    sample_tokenized_freq = count_word_pos_frequency(sample_tokenized_data)\n",
        "    display(sample_tokenized_freq[:15])\n",
        "\n",
        "__sample_freq_detection__()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uzU38MX4Gi7B"
      },
      "source": [
        "generate bag of co-occurence terminology"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PhaEdGxdAHHf"
      },
      "outputs": [],
      "source": [
        "def generate_bigram_freq(term_list)->list:\n",
        "    bigram_list = []\n",
        "\n",
        "    for word_list in term_list:\n",
        "        try:\n",
        "            bigrams_list = list(bigrams(word_list))\n",
        "            bigram_list.extend(bigrams_list)\n",
        "        except:\n",
        "            continue\n",
        "\n",
        "    frequency_dist = FreqDist(bigram_list)\n",
        "    bigram_freq = sorted(frequency_dist.items(), key=itemgetter(1), reverse=True)\n",
        "\n",
        "    return bigram_freq\n",
        "\n",
        "\n",
        "def generate_trigrams(data):\n",
        "  result = []\n",
        "  for doc in data:\n",
        "    collect=[]\n",
        "    for i in range(len(doc[:-2])):\n",
        "      triple = (doc[i],doc[i+1],doc[i+2])\n",
        "      collect.append(triple)\n",
        "    result.append(collect)\n",
        "  return result\n",
        "\n",
        "\n",
        "def count_triple_frequency(docs):\n",
        "    triples = [word for sublist in docs for word in sublist]\n",
        "    triple_frequency = Counter(triples)\n",
        "    result = list(triple_frequency.items())\n",
        "    return  sorted(result, key=lambda x: x[1], reverse=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "DAXbeKlSSW3Y",
        "outputId": "38a0787a-56b4-424a-b351-7059a3548734"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[(('กรมราชทัณฑ์', 'NOUN'), ('ชี้แจง', 'VERB'), ('ต่อ', 'ADP')),\n",
              " (('ชี้แจง', 'VERB'), ('ต่อ', 'ADP'), ('ข้อสงสัย', 'NOUN')),\n",
              " (('ต่อ', 'ADP'), ('ข้อสงสัย', 'NOUN'), ('กรณี', 'NOUN')),\n",
              " (('ข้อสงสัย', 'NOUN'), ('กรณี', 'NOUN'), ('การนำ', 'NOUN')),\n",
              " (('กรณี', 'NOUN'), ('การนำ', 'NOUN'), ('ตัว', 'NOUN')),\n",
              " (('การนำ', 'NOUN'), ('ตัว', 'NOUN'), ('นาย', 'NOUN')),\n",
              " (('ตัว', 'NOUN'), ('นาย', 'NOUN'), ('พริษฐ์', 'PROPN')),\n",
              " (('นาย', 'NOUN'), ('พริษฐ์', 'PROPN'), ('ชิ', 'VERB')),\n",
              " (('พริษฐ์', 'PROPN'), ('ชิ', 'VERB'), ('วา', 'NOUN')),\n",
              " (('ชิ', 'VERB'), ('วา', 'NOUN'), ('รักษ์', 'NOUN')),\n",
              " (('วา', 'NOUN'), ('รักษ์', 'NOUN'), ('หรือ', 'CCONJ')),\n",
              " (('รักษ์', 'NOUN'), ('หรือ', 'CCONJ'), ('เพนกวิน', 'NOUN')),\n",
              " (('หรือ', 'CCONJ'), ('เพนกวิน', 'NOUN'), ('เข้า', 'VERB')),\n",
              " (('เพนกวิน', 'NOUN'), ('เข้า', 'VERB'), ('รับ', 'VERB')),\n",
              " (('เข้า', 'VERB'), ('รับ', 'VERB'), ('การรักษา', 'NOUN')),\n",
              " (('รับ', 'VERB'), ('การรักษา', 'NOUN'), ('ที่', 'SCONJ')),\n",
              " (('การรักษา', 'NOUN'), ('ที่', 'SCONJ'), ('โรงพยาบาล', 'NOUN')),\n",
              " (('ที่', 'SCONJ'), ('โรงพยาบาล', 'NOUN'), ('รามาธิบดี', 'VERB')),\n",
              " (('โรงพยาบาล', 'NOUN'), ('รามาธิบดี', 'VERB'), ('ว่า', 'SCONJ')),\n",
              " (('รามาธิบดี', 'VERB'), ('ว่า', 'SCONJ'), ('เป็น', 'VERB'))]"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "def __sample_gen_trigrams__():\n",
        "  if __is_ipython_kernel__:\n",
        "    global sample_cooc_data\n",
        "    sample_cooc_data = generate_trigrams(sample_tokenized_data)\n",
        "    display(sample_cooc_data[0][:20])\n",
        "\n",
        "__sample_gen_trigrams__()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def flat_triple_doc(triple_docs):\n",
        "  result=[]\n",
        "  for doc in triple_docs:\n",
        "    collect = []\n",
        "    for triple in doc:\n",
        "      collect.append('|'.join([f'{token}|{pos}' for token, pos in triple]))\n",
        "    result.append(collect)\n",
        "  return result\n",
        "\n",
        "def __sample__show_flat_triple_doc__():\n",
        "  if __is_ipython_kernel__:\n",
        "   display(flat_triple_doc(sample_cooc_data[:1])[0][:15])\n",
        "\n",
        "__sample__show_flat_triple_doc__()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "fhAYQm-en7Rw",
        "outputId": "7fe91e77-2d38-4833-86c0-980788475c72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "['กรมราชทัณฑ์|NOUN|ชี้แจง|VERB|ต่อ|ADP',\n",
              " 'ชี้แจง|VERB|ต่อ|ADP|ข้อสงสัย|NOUN',\n",
              " 'ต่อ|ADP|ข้อสงสัย|NOUN|กรณี|NOUN',\n",
              " 'ข้อสงสัย|NOUN|กรณี|NOUN|การนำ|NOUN',\n",
              " 'กรณี|NOUN|การนำ|NOUN|ตัว|NOUN',\n",
              " 'การนำ|NOUN|ตัว|NOUN|นาย|NOUN',\n",
              " 'ตัว|NOUN|นาย|NOUN|พริษฐ์|PROPN',\n",
              " 'นาย|NOUN|พริษฐ์|PROPN|ชิ|VERB',\n",
              " 'พริษฐ์|PROPN|ชิ|VERB|วา|NOUN',\n",
              " 'ชิ|VERB|วา|NOUN|รักษ์|NOUN',\n",
              " 'วา|NOUN|รักษ์|NOUN|หรือ|CCONJ',\n",
              " 'รักษ์|NOUN|หรือ|CCONJ|เพนกวิน|NOUN',\n",
              " 'หรือ|CCONJ|เพนกวิน|NOUN|เข้า|VERB',\n",
              " 'เพนกวิน|NOUN|เข้า|VERB|รับ|VERB',\n",
              " 'เข้า|VERB|รับ|VERB|การรักษา|NOUN']"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def __sample__show_triple_tfidf_list__():\n",
        "  if __is_ipython_kernel__:\n",
        "    global triple_tfidf_list\n",
        "    triple_tfidf_list = calculate_tfidf(flat_triple_doc(sample_cooc_data))\n",
        "    display(triple_tfidf_list[0])\n",
        "\n",
        "__sample__show_triple_tfidf_list__()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lAhI4QVOybmA",
        "outputId": "74a4d816-f362-442a-a682-e6af482df0dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "{'กรณี|NOUN|การนำ|NOUN|ตัว|NOUN': 0.05103257992424078,\n",
              " 'กรด|NOUN|ใน|ADP|กระเพาะอาหาร|NOUN': 0.047146651372167295,\n",
              " 'กรมราชทัณฑ์|NOUN|จะ|AUX|นำเสนอ|VERB': 0.05103257992424078,\n",
              " 'กรมราชทัณฑ์|NOUN|ชี้แจง|VERB|ต่อ|ADP': 0.05103257992424078,\n",
              " 'กรมราชทัณฑ์|NOUN|โดย|ADP|ทัณฑสถาน|NOUN': 0.05103257992424078,\n",
              " 'กระเพาะ|NOUN|รวมทั้ง|CCONJ|ยา|NOUN': 0.05103257992424078,\n",
              " 'กระเพาะอาหาร|NOUN|อักเสบ|VERB|ซึ่ง|SCONJ': 0.05103257992424078,\n",
              " 'กระเพาะอาหาร|NOUN|เพื่อ|SCONJ|บรรเทา|VERB': 0.05103257992424078,\n",
              " 'กรุงเทพ|PROPN|และ|CCONJ|ทัณฑสถาน|NOUN': 0.05103257992424078,\n",
              " 'กรุงเทพมหานคร|PROPN|ได้|ADV|เข้า|VERB': 0.05103257992424078,\n",
              " 'กับ|ADP|แพทย์|NOUN|ที่|SCONJ': 0.05103257992424078,\n",
              " 'การ|NOUN|ดูแล|VERB|ของ|ADP': 0.034525437112611836,\n",
              " 'การ|NOUN|ตรวจ|VERB|ทาง|NOUN': 0.05103257992424078,\n",
              " 'การ|NOUN|ตรวจ|VERB|และ|CCONJ': 0.03758947582343231,\n",
              " 'การ|NOUN|ถ่ายอุจจาระ|VERB|ผิดปกติ|NOUN': 0.05103257992424078,\n",
              " 'การ|NOUN|ถ่ายอุจจาระ|VERB|เป็น|VERB': 0.05103257992424078,\n",
              " 'การ|NOUN|หลั่ง|VERB|กรด|NOUN': 0.05103257992424078,\n",
              " 'การ|NOUN|อดอาหาร|VERB|เป็นเวลา|NOUN': 0.05103257992424078,\n",
              " 'การชี้แจง|NOUN|ไป|VERB|แล้ว|ADV': 0.05103257992424078,\n",
              " 'การตรวจรักษา|NOUN|จึง|SCONJ|ได้|AUX': 0.05103257992424078,\n",
              " 'การนำ|NOUN|ตัว|NOUN|นาย|NOUN': 0.05103257992424078,\n",
              " 'การรักษา|NOUN|ที่|SCONJ|ถูกต้อง|VERB': 0.047146651372167295,\n",
              " 'การรักษา|NOUN|ที่|SCONJ|โรงพยาบาล|NOUN': 0.060987769065542846,\n",
              " 'การรักษา|NOUN|เบื้องต้น|ADJ|โดย|ADP': 0.05103257992424078,\n",
              " 'การรักษา|NOUN|เพื่อ|SCONJ|ตรวจ|VERB': 0.05103257992424078,\n",
              " 'การวินิจฉัย|NOUN|สุดท้าย|ADJ|และ|CCONJ': 0.05103257992424078,\n",
              " 'การวินิจฉัย|VERB|เบื้องต้น|ADV|ว่า|SCONJ': 0.05103257992424078,\n",
              " 'การเพิ่มเติม|VERB|พบ|VERB|ว่า|SCONJ': 0.05103257992424078,\n",
              " 'ของ|ADP|นาย|NOUN|พริษฐ์ฯ|PROPN': 0.15309773977272234,\n",
              " 'ของ|ADP|อาการ|NOUN|นาย|NOUN': 0.05103257992424078,\n",
              " 'ของ|ADP|อุจจาระ|NOUN|ที่|SCONJ': 0.05103257992424078,\n",
              " 'ของ|ADP|แพทย์|NOUN|ผู้เชี่ยวชาญ|NOUN': 0.05103257992424078,\n",
              " 'ข้อมูล|NOUN|กับ|ADP|แพทย์|NOUN': 0.05103257992424078,\n",
              " 'ข้อมูล|NOUN|ที่แจ้ง|VERB|เกี่ยวกับ|ADP': 0.05103257992424078,\n",
              " 'ข้อสงสัย|NOUN|กรณี|NOUN|การนำ|NOUN': 0.05103257992424078,\n",
              " 'ครั้ง|NOUN|เมื่อ|ADP|วันก่อน|NOUN': 0.05103257992424078,\n",
              " 'คลาย|VERB|ความกังวล|NOUN|ต่อ|ADP': 0.05103257992424078,\n",
              " 'ควร|AUX|ส่งตัว|VERB|นาย|NOUN': 0.05103257992424078,\n",
              " 'ความ|NOUN|เชี่ยวชาญ|VERB|เฉพาะ|ADP': 0.044873528888722654,\n",
              " 'ความกังวล|NOUN|ต่อ|ADP|อาการป่วย|NOUN': 0.05103257992424078,\n",
              " 'ความคืบหน้า|NOUN|ของ|ADP|อาการ|NOUN': 0.05103257992424078,\n",
              " 'ความเห็น|NOUN|ว่า|SCONJ|ควร|AUX': 0.047146651372167295,\n",
              " 'จะ|AUX|นำเสนอ|VERB|ให้|SCONJ': 0.05103257992424078,\n",
              " 'จาก|ADP|กระเพาะอาหาร|NOUN|อักเสบ|VERB': 0.05103257992424078,\n",
              " 'จาก|ADP|การ|NOUN|อดอาหาร|VERB': 0.05103257992424078,\n",
              " 'จาก|ADP|ทัณฑสถาน|NOUN|โรงพยาบาล|NOUN': 0.047146651372167295,\n",
              " 'จำนวน|NOUN|ครั้ง|NOUN|เมื่อ|ADP': 0.05103257992424078,\n",
              " 'จึง|SCONJ|ต้อง|AUX|ถอด|VERB': 0.05103257992424078,\n",
              " 'จึง|SCONJ|ยัง|AUX|ไม่|PART': 0.040123399276813104,\n",
              " 'จึง|SCONJ|อยาก|VERB|ให้|SCONJ': 0.03396434824129497,\n",
              " 'จึง|SCONJ|ได้|AUX|ดำเนินการ|VERB': 0.03623747072473962,\n",
              " 'จึง|SCONJ|ได้|AUX|ให้|SCONJ': 0.047146651372167295,\n",
              " 'ชนิด|NOUN|รับประทาน|VERB|เนื่องจาก|SCONJ': 0.05103257992424078,\n",
              " 'ชิ|VERB|วา|NOUN|รักษ์|NOUN': 0.039374794268020326,\n",
              " 'ชี้แจง|VERB|ต่อ|ADP|ข้อสงสัย|NOUN': 0.05103257992424078,\n",
              " 'ซึ่ง|SCONJ|ทาง|NOUN|เรือนจำ|VERB': 0.05103257992424078,\n",
              " 'ซึ่ง|SCONJ|แพทย์|NOUN|ได้|AUX': 0.05103257992424078,\n",
              " 'ญาติ|NOUN|และ|CCONJ|สังคม|NOUN': 0.05103257992424078,\n",
              " 'ดังกล่าว|DET|จึง|SCONJ|อยาก|VERB': 0.05103257992424078,\n",
              " 'ดำเนินการ|VERB|ส่งตัว|VERB|นาย|NOUN': 0.05103257992424078,\n",
              " 'ดื่ม|VERB|น้ำ|NOUN|เกลือแร่|NOUN': 0.05103257992424078,\n",
              " 'ดูแล|VERB|ของ|ADP|แพทย์|NOUN': 0.04200973324445639,\n",
              " 'ด้าน|NOUN|เท่านั้น|ADJ|ทั้งนี้|CCONJ': 0.05103257992424078,\n",
              " 'ด้าน|NOUN|เพื่อ|SCONJ|พิจารณา|VERB': 0.05103257992424078,\n",
              " 'ตรวจ|VERB|ทาง|NOUN|ทวารหนัก|NOUN': 0.05103257992424078,\n",
              " 'ตรวจ|VERB|รักษา|VERB|ต่อ|ADP': 0.05103257992424078,\n",
              " 'ตรวจ|VERB|วินิจฉัย|VERB|ทางเดินอาหาร|NOUN': 0.05103257992424078,\n",
              " 'ตรวจ|VERB|ว่า|SCONJ|มี|VERB': 0.05103257992424078,\n",
              " 'ตรวจ|VERB|อา|NOUN|การเพิ่มเติม|VERB': 0.05103257992424078,\n",
              " 'ตรวจ|VERB|อาการ|NOUN|ประจำวัน|ADJ': 0.05103257992424078,\n",
              " 'ตรวจ|VERB|อาการ|NOUN|โดยละเอียด|VERB': 0.05103257992424078,\n",
              " 'ตรวจ|VERB|และ|CCONJ|ยืนยัน|VERB': 0.047146651372167295,\n",
              " 'ตัว|NOUN|นาย|NOUN|พริษฐ์|PROPN': 0.05103257992424078,\n",
              " 'ตามที่|SCONJ|ได้|AUX|มี|VERB': 0.0381238046923829,\n",
              " 'ต่อ|ADP|ข้อสงสัย|NOUN|กรณี|NOUN': 0.05103257992424078,\n",
              " 'ต่อ|ADP|ผู้ต้องขัง|NOUN|มาก|ADV': 0.05103257992424078,\n",
              " 'ต่อ|ADP|อาการป่วย|NOUN|ของ|ADP': 0.05103257992424078,\n",
              " 'ต่อ|ADP|ใน|ADP|โรงพยาบาล|NOUN': 0.05103257992424078,\n",
              " 'ต้อง|AUX|ถอด|VERB|สาย|NOUN': 0.05103257992424078,\n",
              " 'ถอด|VERB|สาย|NOUN|น้ำเกลือ|NOUN': 0.05103257992424078,\n",
              " 'ถูกต้อง|VERB|และ|CCONJ|เป็นประโยชน์|VERB': 0.05103257992424078,\n",
              " 'ถ่ายอุจจาระ|NOUN|เป็น|VERB|เลือด|NOUN': 0.05103257992424078,\n",
              " 'ถ่ายอุจจาระ|VERB|ผิดปกติ|NOUN|อาจ|AUX': 0.05103257992424078,\n",
              " 'ถ่ายอุจจาระ|VERB|อีก|ADV|โดย|SCONJ': 0.05103257992424078,\n",
              " 'ถ่ายอุจจาระ|VERB|เป็น|VERB|เลือด|NOUN': 0.05103257992424078,\n",
              " 'ทวารหนัก|NOUN|เพิ่มเติม|VERB|ซึ่ง|SCONJ': 0.05103257992424078,\n",
              " 'ทัณฑสถาน|NOUN|โรงพยาบาล|NOUN|ราชทัณฑ์|NOUN': 0.12602919973336918,\n",
              " 'ทั่วไป|DET|ปกติ|VERB|มี|VERB': 0.05103257992424078,\n",
              " 'ทั้งนี้|CCONJ|หาก|SCONJ|มี|VERB': 0.03482854930113104,\n",
              " 'ทาง|ADP|หลอดเลือดดำ|NOUN|จึง|SCONJ': 0.05103257992424078,\n",
              " 'ทาง|ADP|เส้น|NOUN|เลือดดำ|NOUN': 0.05103257992424078,\n",
              " 'ทาง|NOUN|ทวารหนัก|NOUN|เพิ่มเติม|VERB': 0.05103257992424078,\n",
              " 'ทาง|NOUN|เรือนจำ|VERB|พิเศษ|ADJ': 0.05103257992424078,\n",
              " 'ทางเดินอาหาร|NOUN|เพิ่มเติม|VERB|ให้|SCONJ': 0.05103257992424078,\n",
              " 'ทำ|VERB|การตรวจรักษา|NOUN|จึง|SCONJ': 0.05103257992424078,\n",
              " 'ที่|SCONJ|ถูกต้อง|VERB|และ|CCONJ': 0.03585068220893825,\n",
              " 'ที่|SCONJ|ผิดปกติ|VERB|เหมือน|VERB': 0.05103257992424078,\n",
              " 'ที่|SCONJ|มี|VERB|ความ|NOUN': 0.02690308779797445,\n",
              " 'ที่|SCONJ|อด|VERB|อาการ|NOUN': 0.05103257992424078,\n",
              " 'ที่|SCONJ|เกิดขึ้น|VERB|ได้|ADV': 0.0381238046923829,\n",
              " 'ที่|SCONJ|เข้า|VERB|ตรวจ|VERB': 0.05103257992424078,\n",
              " 'ที่|SCONJ|เข้า|VERB|ทำ|VERB': 0.043260722820093814,\n",
              " 'ที่|SCONJ|โรงพยาบาล|NOUN|รามาธิบดี|VERB': 0.09429330274433459,\n",
              " 'ที่|SCONJ|ให้|VERB|สาร|NOUN': 0.05103257992424078,\n",
              " 'ที่จะ|NOUN|เปิด|VERB|เส้นเลือด|NOUN': 0.05103257992424078,\n",
              " 'ที่สุด|ADV|กรมราชทัณฑ์|NOUN|โดย|ADP': 0.05103257992424078,\n",
              " 'ที่แจ้ง|VERB|เกี่ยวกับ|ADP|การ|NOUN': 0.05103257992424078,\n",
              " 'นั้น|DET|เนื่องจาก|SCONJ|ใน|ADP': 0.047146651372167295,\n",
              " 'นาน|ADV|และ|CCONJ|อาการ|NOUN': 0.05103257992424078,\n",
              " 'นาน|ADV|และ|CCONJ|เป็นการ|NOUN': 0.05103257992424078,\n",
              " 'นาย|NOUN|พริษฐ์|PROPN|ชิ|VERB': 0.039374794268020326,\n",
              " 'นาย|NOUN|พริษฐ์ฯ|PROPN|ปฏิเสธ|VERB': 0.05103257992424078,\n",
              " 'นาย|NOUN|พริษฐ์ฯ|PROPN|พบ|VERB': 0.05103257992424078,\n",
              " 'นาย|NOUN|พริษฐ์ฯ|PROPN|รู้สึก|VERB': 0.05103257992424078,\n",
              " 'นาย|NOUN|พริษฐ์ฯ|PROPN|ว่า|SCONJ': 0.05103257992424078,\n",
              " 'นาย|NOUN|พริษฐ์ฯ|PROPN|เกิด|VERB': 0.05103257992424078,\n",
              " 'นาย|NOUN|พริษฐ์ฯ|PROPN|เข้า|VERB': 0.10206515984848157,\n",
              " 'นาย|NOUN|พริษฐ์ฯ|PROPN|เพิ่มเติม|VERB': 0.05103257992424078,\n",
              " 'นำเสนอ|VERB|ให้|SCONJ|สังคม|NOUN': 0.05103257992424078,\n",
              " 'นี้|DET|เจ้าหน้าที่|NOUN|พยาบาล|NOUN': 0.05103257992424078,\n",
              " 'น้อยลง|NOUN|และ|CCONJ|มี|VERB': 0.047146651372167295,\n",
              " 'น้ำ|NOUN|ทาง|ADP|หลอดเลือดดำ|NOUN': 0.05103257992424078,\n",
              " 'น้ำ|NOUN|เกลือแร่|NOUN|ได้|ADV': 0.05103257992424078,\n",
              " 'น้ำเกลือ|NOUN|รวมทั้ง|CCONJ|ให้ยา|VERB': 0.05103257992424078,\n",
              " 'น้ำเกลือ|NOUN|ออก|VERB|และ|CCONJ': 0.05103257992424078,\n",
              " 'บรรเทา|VERB|อาการ|NOUN|และ|CCONJ': 0.05103257992424078,\n",
              " 'บริเวณ|NOUN|ที่|SCONJ|ให้|VERB': 0.05103257992424078,\n",
              " 'บางครั้ง|NOUN|และ|CCONJ|เจ้าตัว|VERB': 0.05103257992424078,\n",
              " 'ปกติ|VERB|มี|VERB|อาการ|NOUN': 0.05103257992424078,\n",
              " 'ปฏิเสธ|VERB|การ|NOUN|ตรวจ|VERB': 0.05103257992424078,\n",
              " 'ปฏิเสธ|VERB|ที่จะ|NOUN|เปิด|VERB': 0.05103257992424078,\n",
              " 'ปน|VERB|จำนวน|NOUN|ครั้ง|NOUN': 0.05103257992424078,\n",
              " 'ประจำวัน|ADJ|ของ|ADP|นาย|NOUN': 0.05103257992424078,\n",
              " 'ประสาน|VERB|แพทย์|NOUN|และ|CCONJ': 0.05103257992424078,\n",
              " 'ปวด|VERB|บริเวณ|NOUN|ที่|SCONJ': 0.044873528888722654,\n",
              " 'ปวด|VERB|แสบท้อง|NOUN|เป็น|VERB': 0.05103257992424078,\n",
              " 'ปวดท้อง|VERB|ร่วมกับ|ADP|ข้อมูล|NOUN': 0.05103257992424078,\n",
              " 'ผิดปกติ|NOUN|อาจ|AUX|เกิด|VERB': 0.05103257992424078,\n",
              " 'ผิดปกติ|VERB|เหมือน|VERB|มี|VERB': 0.05103257992424078,\n",
              " 'ผู้ต้องขัง|NOUN|มาก|ADV|ที่สุด|ADV': 0.05103257992424078,\n",
              " 'ผู้ป่วย|NOUN|ที่|SCONJ|อด|VERB': 0.05103257992424078,\n",
              " 'ผู้เชี่ยวชาญ|NOUN|เฉพาะ|ADP|ด้าน|NOUN': 0.05103257992424078,\n",
              " 'พบ|VERB|ว่า|SCONJ|นาย|NOUN': 0.047146651372167295,\n",
              " 'พบ|VERB|ว่า|SCONJ|มี|VERB': 0.022545160714697775,\n",
              " 'พบ|VERB|ว่า|SCONJ|อาการ|NOUN': 0.047146651372167295,\n",
              " 'พยาบาล|NOUN|จาก|ADP|ทัณฑสถาน|NOUN': 0.05103257992424078,\n",
              " 'พยาบาล|NOUN|เรือนจำ|VERB|พิเศษ|ADJ': 0.05103257992424078,\n",
              " 'พริษฐ์|PROPN|ชิ|VERB|วา|NOUN': 0.039374794268020326,\n",
              " 'พริษฐ์ฯ|PROPN|ปฏิเสธ|VERB|ที่จะ|NOUN': 0.05103257992424078,\n",
              " 'พริษฐ์ฯ|PROPN|พบ|VERB|ว่า|SCONJ': 0.05103257992424078,\n",
              " 'พริษฐ์ฯ|PROPN|รู้สึก|VERB|อ่อนเพลีย|VERB': 0.05103257992424078,\n",
              " 'พริษฐ์ฯ|PROPN|ว่า|SCONJ|เป็น|VERB': 0.05103257992424078,\n",
              " 'พริษฐ์ฯ|PROPN|เกิด|VERB|จาก|ADP': 0.05103257992424078,\n",
              " 'พริษฐ์ฯ|PROPN|เข้า|VERB|ตรวจ|VERB': 0.05103257992424078,\n",
              " 'พริษฐ์ฯ|PROPN|เข้า|VERB|รับ|VERB': 0.05103257992424078,\n",
              " 'พริษฐ์ฯ|PROPN|เพิ่มเติม|VERB|กรมราชทัณฑ์|NOUN': 0.05103257992424078,\n",
              " 'พิจารณา|VERB|ให้|SCONJ|สารอาหาร|VERB': 0.05103257992424078,\n",
              " 'พิเศษ|ADJ|กรุงเทพ|PROPN|และ|CCONJ': 0.05103257992424078,\n",
              " 'พิเศษ|ADJ|กรุงเทพมหานคร|PROPN|ได้|ADV': 0.05103257992424078,\n",
              " 'ภายใต้|ADP|การ|NOUN|ดูแล|VERB': 0.03665293556828955,\n",
              " 'ภาวะ|NOUN|ที่|SCONJ|เกิดขึ้น|VERB': 0.05103257992424078,\n",
              " 'ภาวะ|NOUN|อ่อนเพลีย|NOUN|และ|CCONJ': 0.05103257992424078,\n",
              " 'มาก|ADV|ที่สุด|ADV|กรมราชทัณฑ์|NOUN': 0.05103257992424078,\n",
              " 'มี|VERB|การ|NOUN|ตรวจ|VERB': 0.02488836173377443,\n",
              " 'มี|VERB|การ|NOUN|ถ่ายอุจจาระ|VERB': 0.05103257992424078,\n",
              " 'มี|VERB|การชี้แจง|NOUN|ไป|VERB': 0.05103257992424078,\n",
              " 'มี|VERB|ความ|NOUN|เชี่ยวชาญ|VERB': 0.03665293556828955,\n",
              " 'มี|VERB|ความคืบหน้า|NOUN|ของ|ADP': 0.047146651372167295,\n",
              " 'มี|VERB|ลักษณะ|NOUN|ของ|ADP': 0.03710167178457568,\n",
              " 'มี|VERB|อาการ|NOUN|ปวด|VERB': 0.08974705777744531,\n",
              " 'มี|VERB|อาการ|NOUN|อ่อนเพลีย|VERB': 0.04200973324445639,\n",
              " 'มี|VERB|เลือด|NOUN|ปน|VERB': 0.05103257992424078,\n",
              " 'ยัง|AUX|ไม่|PART|พบ|VERB': 0.031263062811813194,\n",
              " 'ยัง|AUX|ไม่|PART|มี|VERB': 0.024783569713539413,\n",
              " 'ยัง|AUX|ไม่|PART|ได้|AUX': 0.025868342489231668,\n",
              " 'ยา|NOUN|ลด|VERB|การ|NOUN': 0.044873528888722654,\n",
              " 'ยืนยัน|VERB|ว่า|SCONJ|ยัง|AUX': 0.047146651372167295,\n",
              " 'รวมทั้ง|CCONJ|ยา|NOUN|ลด|VERB': 0.05103257992424078,\n",
              " 'รวมทั้ง|CCONJ|ให้ยา|VERB|เคลือบ|ADV': 0.05103257992424078,\n",
              " 'รักษา|VERB|ต่อ|ADP|ใน|ADP': 0.05103257992424078,\n",
              " 'รักษ์|NOUN|หรือ|CCONJ|เพนกวิน|NOUN': 0.039374794268020326,\n",
              " 'รับ|VERB|การรักษา|NOUN|ที่|SCONJ': 0.05963523743857069,\n",
              " 'รับประทาน|VERB|อาหาร|NOUN|เป็นเวลา|ADJ': 0.05103257992424078,\n",
              " 'รับประทาน|VERB|เนื่องจาก|SCONJ|นาย|NOUN': 0.05103257992424078,\n",
              " 'ราชทัณฑ์|NOUN|จึง|SCONJ|ยัง|AUX': 0.05103257992424078,\n",
              " 'ราชทัณฑ์|NOUN|จึง|SCONJ|ได้|AUX': 0.05103257992424078,\n",
              " 'ราชทัณฑ์|NOUN|เพื่อ|SCONJ|เข้า|VERB': 0.05103257992424078,\n",
              " 'รามาธิบดี|VERB|ตามที่|SCONJ|ได้|AUX': 0.05103257992424078,\n",
              " 'รามาธิบดี|VERB|ว่า|SCONJ|เป็น|VERB': 0.05103257992424078,\n",
              " 'ริมฝีปาก|NOUN|แห้ง|ADJ|เล็กน้อย|ADV': 0.05103257992424078,\n",
              " 'รู้สึก|VERB|อ่อนเพลีย|VERB|และ|CCONJ': 0.05103257992424078,\n",
              " 'ร่วมกับ|ADP|ข้อมูล|NOUN|ที่แจ้ง|VERB': 0.05103257992424078,\n",
              " 'ลด|VERB|การ|NOUN|หลั่ง|VERB': 0.05103257992424078,\n",
              " 'ลดลง|NOUN|ของ|ADP|นาย|NOUN': 0.05103257992424078,\n",
              " 'ลดลง|VERB|ริมฝีปาก|NOUN|แห้ง|ADJ': 0.05103257992424078,\n",
              " 'ลักษณะ|NOUN|ของ|ADP|อุจจาระ|NOUN': 0.05103257992424078,\n",
              " 'วันก่อน|NOUN|หลังจากนั้น|DET|ยัง|AUX': 0.05103257992424078,\n",
              " 'วา|NOUN|รักษ์|NOUN|หรือ|CCONJ': 0.039374794268020326,\n",
              " 'วิตามิน|NOUN|ทาง|ADP|เส้น|NOUN': 0.05103257992424078,\n",
              " 'วินิจฉัย|VERB|ทางเดินอาหาร|NOUN|เพิ่มเติม|VERB': 0.05103257992424078,\n",
              " 'ว่า|SCONJ|ควร|AUX|ส่งตัว|VERB': 0.05103257992424078,\n",
              " 'ว่า|SCONJ|นาย|NOUN|พริษฐ์ฯ|PROPN': 0.05103257992424078,\n",
              " 'ว่า|SCONJ|ภาวะ|NOUN|อ่อนเพลีย|NOUN': 0.05103257992424078,\n",
              " 'ว่า|SCONJ|มี|VERB|การ|NOUN': 0.028669498265612898,\n",
              " 'ว่า|SCONJ|มี|VERB|ลักษณะ|NOUN': 0.05103257992424078,\n",
              " 'ว่า|SCONJ|ยัง|AUX|ไม่|PART': 0.04200973324445639,\n",
              " 'ว่า|SCONJ|อาการ|NOUN|ทั่วไป|DET': 0.05103257992424078,\n",
              " 'ว่า|SCONJ|เป็น|VERB|ภาวะ|NOUN': 0.047146651372167295,\n",
              " 'ว่า|SCONJ|เป็น|VERB|เพราะ|SCONJ': 0.047146651372167295,\n",
              " 'สังคม|NOUN|คลาย|VERB|ความกังวล|NOUN': 0.05103257992424078,\n",
              " 'สังคม|NOUN|ได้|AUX|รับทราบ|VERB': 0.05103257992424078,\n",
              " 'สาย|NOUN|น้ำเกลือ|NOUN|ออก|VERB': 0.05103257992424078,\n",
              " 'สาร|NOUN|น้ำ|NOUN|ทาง|ADP': 0.05103257992424078,\n",
              " 'สารอาหาร|VERB|และ|CCONJ|วิตามิน|NOUN': 0.05103257992424078,\n",
              " 'สุดท้าย|ADJ|และ|CCONJ|ให้|SCONJ': 0.05103257992424078,\n",
              " 'ส่งตัว|VERB|นาย|NOUN|พริษฐ์ฯ|PROPN': 0.10206515984848157,\n",
              " 'ส่งต่อ|VERB|การรักษา|NOUN|เพื่อ|SCONJ': 0.05103257992424078,\n",
              " 'หรือ|CCONJ|เพนกวิน|NOUN|เข้า|VERB': 0.05103257992424078,\n",
              " 'หรือไม่|PART|ได้|AUX|รับประทาน|VERB': 0.05103257992424078,\n",
              " 'หลอดเลือดดำ|NOUN|จึง|SCONJ|ต้อง|AUX': 0.05103257992424078,\n",
              " 'หลังจากนั้น|DET|ยัง|AUX|ไม่|PART': 0.05103257992424078,\n",
              " 'หลั่ง|VERB|กรด|NOUN|ใน|ADP': 0.05103257992424078,\n",
              " 'หาก|SCONJ|มี|VERB|ความคืบหน้า|NOUN': 0.044873528888722654,\n",
              " 'อด|VERB|อาการ|NOUN|หรือไม่|PART': 0.05103257992424078,\n",
              " 'อดอาหาร|VERB|เป็นเวลา|NOUN|นาน|ADV': 0.05103257992424078,\n",
              " 'อยาก|VERB|ให้|SCONJ|ญาติ|NOUN': 0.05103257992424078,\n",
              " 'ออก|VERB|และ|CCONJ|ได้|AUX': 0.05103257992424078,\n",
              " 'อักเสบ|VERB|ซึ่ง|SCONJ|แพทย์|NOUN': 0.05103257992424078,\n",
              " 'อา|NOUN|การเพิ่มเติม|VERB|พบ|VERB': 0.05103257992424078,\n",
              " 'อาการ|NOUN|ทั่วไป|DET|ปกติ|VERB': 0.05103257992424078,\n",
              " 'อาการ|NOUN|นาย|NOUN|พริษฐ์ฯ|PROPN': 0.05103257992424078,\n",
              " 'อาการ|NOUN|ประจำวัน|ADJ|ของ|ADP': 0.05103257992424078,\n",
              " 'อาการ|NOUN|ปวด|VERB|บริเวณ|NOUN': 0.047146651372167295,\n",
              " 'อาการ|NOUN|ปวด|VERB|แสบท้อง|NOUN': 0.05103257992424078,\n",
              " 'อาการ|NOUN|ปวดท้อง|VERB|ร่วมกับ|ADP': 0.05103257992424078,\n",
              " 'อาการ|NOUN|หรือไม่|PART|ได้|AUX': 0.05103257992424078,\n",
              " 'อาการ|NOUN|อ่อนเพลีย|VERB|เล็กน้อย|ADV': 0.047146651372167295,\n",
              " 'อาการ|NOUN|และ|CCONJ|แพทย์|NOUN': 0.05103257992424078,\n",
              " 'อาการ|NOUN|โดยละเอียด|VERB|ภายใต้|ADP': 0.05103257992424078,\n",
              " 'อาการป่วย|NOUN|ของ|ADP|นาย|NOUN': 0.047146651372167295,\n",
              " 'อาจ|AUX|เกิด|VERB|จาก|ADP': 0.03871447785320452,\n",
              " 'อาหาร|NOUN|เป็นเวลา|ADJ|นาน|ADV': 0.05103257992424078,\n",
              " 'อีก|ADV|โดย|SCONJ|เจ้าตัว|VERB': 0.05103257992424078,\n",
              " 'อุจจาระ|NOUN|ที่|SCONJ|ผิดปกติ|VERB': 0.05103257992424078,\n",
              " 'อ่อนเพลีย|NOUN|และ|CCONJ|เรี่ยวแรง|NOUN': 0.05103257992424078,\n",
              " 'อ่อนเพลีย|VERB|เล็กน้อย|ADV|แต่|CCONJ': 0.05103257992424078,\n",
              " 'อ่อนเพลีย|VERB|และ|CCONJ|เรี่ยวแรง|VERB': 0.05103257992424078,\n",
              " 'เกลือแร่|NOUN|ชนิด|NOUN|รับประทาน|VERB': 0.05103257992424078,\n",
              " 'เกลือแร่|NOUN|ได้|ADV|น้อยลง|NOUN': 0.05103257992424078,\n",
              " 'เกิด|VERB|จาก|ADP|กระเพาะอาหาร|NOUN': 0.05103257992424078,\n",
              " 'เกิด|VERB|จาก|ADP|การ|NOUN': 0.02956841281005197,\n",
              " 'เกิดขึ้น|VERB|ได้|ADV|ใน|ADP': 0.044873528888722654,\n",
              " 'เกี่ยวกับ|ADP|การ|NOUN|ถ่ายอุจจาระ|VERB': 0.05103257992424078,\n",
              " 'เข้า|VERB|ตรวจ|VERB|รักษา|VERB': 0.05103257992424078,\n",
              " 'เข้า|VERB|ตรวจ|VERB|ว่า|SCONJ': 0.05103257992424078,\n",
              " 'เข้า|VERB|ตรวจ|VERB|อา|NOUN': 0.05103257992424078,\n",
              " 'เข้า|VERB|ตรวจ|VERB|อาการ|NOUN': 0.05103257992424078,\n",
              " 'เข้า|VERB|ทำ|VERB|การตรวจรักษา|NOUN': 0.05103257992424078,\n",
              " 'เข้า|VERB|รับ|VERB|การรักษา|NOUN': 0.052655174136406704,\n",
              " 'เคลือบ|ADV|กระเพาะ|NOUN|รวมทั้ง|CCONJ': 0.05103257992424078,\n",
              " 'เจ้าตัว|VERB|ได้|ADV|ปฏิเสธ|VERB': 0.05103257992424078,\n",
              " 'เจ้าตัว|VERB|ได้|ADV|ให้|VERB': 0.05103257992424078,\n",
              " 'เจ้าหน้าที่|NOUN|พยาบาล|NOUN|เรือนจำ|VERB': 0.05103257992424078,\n",
              " 'เฉพาะ|ADP|ด้าน|NOUN|เท่านั้น|ADJ': 0.05103257992424078,\n",
              " 'เฉพาะ|ADP|ด้าน|NOUN|เพื่อ|SCONJ': 0.047146651372167295,\n",
              " 'เชี่ยวชาญ|VERB|เฉพาะ|ADP|ด้าน|NOUN': 0.043260722820093814,\n",
              " 'เท่านั้น|ADJ|ทั้งนี้|CCONJ|หาก|SCONJ': 0.05103257992424078,\n",
              " 'เนื่องจาก|SCONJ|นาย|NOUN|พริษฐ์ฯ|PROPN': 0.05103257992424078,\n",
              " 'เนื่องจาก|SCONJ|ใน|ADP|เมื่อวาน|NOUN': 0.05103257992424078,\n",
              " 'เบื้องต้น|ADJ|โดย|ADP|ให้น้ำ|ADJ': 0.05103257992424078,\n",
              " 'เบื้องต้น|ADV|ว่า|SCONJ|ภาวะ|NOUN': 0.05103257992424078,\n",
              " 'เปิด|VERB|เส้นเลือด|NOUN|ให้|SCONJ': 0.05103257992424078,\n",
              " 'เป็น|VERB|บางครั้ง|NOUN|และ|CCONJ': 0.05103257992424078,\n",
              " 'เป็น|VERB|ภาวะ|NOUN|ที่|SCONJ': 0.047146651372167295,\n",
              " 'เป็น|VERB|เพราะ|SCONJ|ถ่ายอุจจาระ|NOUN': 0.05103257992424078,\n",
              " 'เป็น|VERB|เลือด|NOUN|นั้น|DET': 0.05103257992424078,\n",
              " 'เป็น|VERB|เลือด|NOUN|แต่อย่างใด|VERB': 0.05103257992424078,\n",
              " 'เป็นการ|NOUN|ส่งต่อ|VERB|การรักษา|NOUN': 0.05103257992424078,\n",
              " 'เป็นประโยชน์|VERB|ต่อ|ADP|ผู้ต้องขัง|NOUN': 0.05103257992424078,\n",
              " 'เป็นเวลา|ADJ|นาน|ADV|และ|CCONJ': 0.05103257992424078,\n",
              " 'เป็นเวลา|NOUN|นาน|ADV|และ|CCONJ': 0.05103257992424078,\n",
              " 'เพนกวิน|NOUN|เข้า|VERB|รับ|VERB': 0.05103257992424078,\n",
              " 'เพราะ|SCONJ|ถ่ายอุจจาระ|NOUN|เป็น|VERB': 0.05103257992424078,\n",
              " 'เพิ่มเติม|VERB|กรมราชทัณฑ์|NOUN|จะ|AUX': 0.05103257992424078,\n",
              " 'เพิ่มเติม|VERB|ซึ่ง|SCONJ|ทาง|NOUN': 0.05103257992424078,\n",
              " 'เพิ่มเติม|VERB|ให้|SCONJ|ได้ผล|NOUN': 0.05103257992424078,\n",
              " 'เพื่อ|SCONJ|ตรวจ|VERB|อาการ|NOUN': 0.05103257992424078,\n",
              " 'เพื่อ|SCONJ|บรรเทา|VERB|อาการ|NOUN': 0.047146651372167295,\n",
              " 'เพื่อ|SCONJ|พิจารณา|VERB|ให้|SCONJ': 0.03871447785320452,\n",
              " 'เพื่อ|SCONJ|เข้า|VERB|ตรวจ|VERB': 0.05103257992424078,\n",
              " 'เมื่อ|ADP|วันก่อน|NOUN|หลังจากนั้น|DET': 0.05103257992424078,\n",
              " 'เมื่อวาน|NOUN|นี้|DET|เจ้าหน้าที่|NOUN': 0.043260722820093814,\n",
              " 'เริ่ม|VERB|ดื่ม|VERB|น้ำ|NOUN': 0.05103257992424078,\n",
              " 'เรี่ยวแรง|NOUN|ลดลง|NOUN|ของ|ADP': 0.05103257992424078,\n",
              " 'เรี่ยวแรง|VERB|ลดลง|VERB|ริมฝีปาก|NOUN': 0.05103257992424078,\n",
              " 'เรือนจำ|VERB|พิเศษ|ADJ|กรุงเทพ|PROPN': 0.047146651372167295,\n",
              " 'เรือนจำ|VERB|พิเศษ|ADJ|กรุงเทพมหานคร|PROPN': 0.05103257992424078,\n",
              " 'เลือด|NOUN|นั้น|DET|เนื่องจาก|SCONJ': 0.05103257992424078,\n",
              " 'เลือด|NOUN|ปน|VERB|จำนวน|NOUN': 0.05103257992424078,\n",
              " 'เลือด|NOUN|แต่อย่างใด|VERB|แพทย์|NOUN': 0.05103257992424078,\n",
              " 'เลือดดำ|NOUN|และ|CCONJ|ตรวจ|VERB': 0.05103257992424078,\n",
              " 'เล็กน้อย|ADV|มี|VERB|อาการ|NOUN': 0.05103257992424078,\n",
              " 'เล็กน้อย|ADV|แต่|CCONJ|เริ่ม|VERB': 0.05103257992424078,\n",
              " 'เส้น|NOUN|เลือดดำ|NOUN|และ|CCONJ': 0.05103257992424078,\n",
              " 'เส้นเลือด|NOUN|ให้|SCONJ|น้ำเกลือ|NOUN': 0.05103257992424078,\n",
              " 'เหมือน|VERB|มี|VERB|เลือด|NOUN': 0.05103257992424078,\n",
              " 'แต่|CCONJ|เริ่ม|VERB|ดื่ม|VERB': 0.05103257992424078,\n",
              " 'แต่อย่างใด|VERB|แพทย์|NOUN|ที่|SCONJ': 0.05103257992424078,\n",
              " 'แพทย์|NOUN|ที่|SCONJ|เข้า|VERB': 0.10206515984848157,\n",
              " 'แพทย์|NOUN|ผู้เชี่ยวชาญ|NOUN|เฉพาะ|ADP': 0.05103257992424078,\n",
              " 'แพทย์|NOUN|และ|CCONJ|พยาบาล|NOUN': 0.03482854930113104,\n",
              " 'แพทย์|NOUN|ได้|AUX|ให้|VERB': 0.10206515984848157,\n",
              " 'และ|CCONJ|ตรวจ|VERB|วินิจฉัย|VERB': 0.05103257992424078,\n",
              " 'และ|CCONJ|ทัณฑสถาน|NOUN|โรงพยาบาล|NOUN': 0.05103257992424078,\n",
              " 'และ|CCONJ|พยาบาล|NOUN|จาก|ADP': 0.044873528888722654,\n",
              " 'และ|CCONJ|มี|VERB|อาการ|NOUN': 0.03710167178457568,\n",
              " 'และ|CCONJ|ยืนยัน|VERB|ว่า|SCONJ': 0.040987600336649166,\n",
              " 'และ|CCONJ|วิตามิน|NOUN|ทาง|ADP': 0.05103257992424078,\n",
              " 'และ|CCONJ|สังคม|NOUN|คลาย|VERB': 0.05103257992424078,\n",
              " 'และ|CCONJ|อาการ|NOUN|ปวดท้อง|VERB': 0.05103257992424078,\n",
              " 'และ|CCONJ|เจ้าตัว|VERB|ได้|ADV': 0.05103257992424078,\n",
              " 'และ|CCONJ|เป็นการ|NOUN|ส่งต่อ|VERB': 0.05103257992424078,\n",
              " 'และ|CCONJ|เป็นประโยชน์|VERB|ต่อ|ADP': 0.043260722820093814,\n",
              " 'และ|CCONJ|เรี่ยวแรง|NOUN|ลดลง|NOUN': 0.05103257992424078,\n",
              " 'และ|CCONJ|เรี่ยวแรง|VERB|ลดลง|VERB': 0.05103257992424078,\n",
              " 'และ|CCONJ|แพทย์|NOUN|ได้|AUX': 0.05103257992424078,\n",
              " 'และ|CCONJ|ให้|SCONJ|ได้รับ|VERB': 0.047146651372167295,\n",
              " 'และ|CCONJ|ได้|AUX|ประสาน|VERB': 0.044873528888722654,\n",
              " 'แล้ว|ADV|ดังกล่าว|DET|จึง|SCONJ': 0.05103257992424078,\n",
              " 'แสบท้อง|NOUN|เป็น|VERB|บางครั้ง|NOUN': 0.05103257992424078,\n",
              " 'แห้ง|ADJ|เล็กน้อย|ADV|มี|VERB': 0.05103257992424078,\n",
              " 'โดย|ADP|ทัณฑสถาน|NOUN|โรงพยาบาล|NOUN': 0.05103257992424078,\n",
              " 'โดย|ADP|ให้น้ำ|ADJ|เกลือแร่|NOUN': 0.05103257992424078,\n",
              " 'โดย|SCONJ|เจ้าตัว|VERB|ได้|ADV': 0.047146651372167295,\n",
              " 'โดยละเอียด|VERB|ภายใต้|ADP|การ|NOUN': 0.05103257992424078,\n",
              " 'โรงพยาบาล|NOUN|ที่|SCONJ|มี|VERB': 0.05103257992424078,\n",
              " 'โรงพยาบาล|NOUN|ราชทัณฑ์|NOUN|จึง|SCONJ': 0.10206515984848157,\n",
              " 'โรงพยาบาล|NOUN|ราชทัณฑ์|NOUN|เพื่อ|SCONJ': 0.047146651372167295,\n",
              " 'โรงพยาบาล|NOUN|รามาธิบดี|VERB|ตามที่|SCONJ': 0.05103257992424078,\n",
              " 'โรงพยาบาล|NOUN|รามาธิบดี|VERB|ว่า|SCONJ': 0.05103257992424078,\n",
              " 'ใน|ADP|กระเพาะอาหาร|NOUN|เพื่อ|SCONJ': 0.05103257992424078,\n",
              " 'ใน|ADP|ผู้ป่วย|NOUN|ที่|SCONJ': 0.043260722820093814,\n",
              " 'ใน|ADP|เมื่อวาน|NOUN|นี้|DET': 0.05103257992424078,\n",
              " 'ใน|ADP|โรงพยาบาล|NOUN|ที่|SCONJ': 0.047146651372167295,\n",
              " 'ให้|SCONJ|การวินิจฉัย|VERB|เบื้องต้น|ADV': 0.05103257992424078,\n",
              " 'ให้|SCONJ|ญาติ|NOUN|และ|CCONJ': 0.047146651372167295,\n",
              " 'ให้|SCONJ|น้ำเกลือ|NOUN|รวมทั้ง|CCONJ': 0.05103257992424078,\n",
              " 'ให้|SCONJ|สังคม|NOUN|ได้|AUX': 0.04200973324445639,\n",
              " 'ให้|SCONJ|สารอาหาร|VERB|และ|CCONJ': 0.05103257992424078,\n",
              " 'ให้|SCONJ|ได้ผล|NOUN|การวินิจฉัย|NOUN': 0.05103257992424078,\n",
              " 'ให้|SCONJ|ได้รับ|VERB|การรักษา|NOUN': 0.047146651372167295,\n",
              " 'ให้|VERB|การรักษา|NOUN|เบื้องต้น|ADJ': 0.05103257992424078,\n",
              " 'ให้|VERB|ข้อมูล|NOUN|กับ|ADP': 0.05103257992424078,\n",
              " 'ให้|VERB|ความเห็น|NOUN|ว่า|SCONJ': 0.05103257992424078,\n",
              " 'ให้|VERB|สาร|NOUN|น้ำ|NOUN': 0.05103257992424078,\n",
              " 'ให้น้ำ|ADJ|เกลือแร่|NOUN|ชนิด|NOUN': 0.05103257992424078,\n",
              " 'ให้ยา|VERB|เคลือบ|ADV|กระเพาะ|NOUN': 0.05103257992424078,\n",
              " 'ได้|ADV|น้อยลง|NOUN|และ|CCONJ': 0.047146651372167295,\n",
              " 'ได้|ADV|ปฏิเสธ|VERB|การ|NOUN': 0.05103257992424078,\n",
              " 'ได้|ADV|เข้า|VERB|ตรวจ|VERB': 0.05103257992424078,\n",
              " 'ได้|ADV|ใน|ADP|ผู้ป่วย|NOUN': 0.05103257992424078,\n",
              " 'ได้|ADV|ให้|VERB|ข้อมูล|NOUN': 0.04200973324445639,\n",
              " 'ได้|AUX|ดำเนินการ|VERB|ส่งตัว|VERB': 0.05103257992424078,\n",
              " 'ได้|AUX|ถ่ายอุจจาระ|VERB|อีก|ADV': 0.05103257992424078,\n",
              " 'ได้|AUX|ประสาน|VERB|แพทย์|NOUN': 0.05103257992424078,\n",
              " 'ได้|AUX|มี|VERB|การชี้แจง|NOUN': 0.047146651372167295,\n",
              " 'ได้|AUX|รับทราบ|VERB|ต่อไป|ADV': 0.044873528888722654,\n",
              " 'ได้|AUX|รับประทาน|VERB|อาหาร|NOUN': 0.05103257992424078,\n",
              " 'ได้|AUX|ให้|SCONJ|การวินิจฉัย|VERB': 0.05103257992424078,\n",
              " 'ได้|AUX|ให้|VERB|การรักษา|NOUN': 0.05103257992424078,\n",
              " 'ได้|AUX|ให้|VERB|ความเห็น|NOUN': 0.044873528888722654,\n",
              " 'ได้ผล|NOUN|การวินิจฉัย|NOUN|สุดท้าย|ADJ': 0.05103257992424078,\n",
              " 'ได้รับ|VERB|การรักษา|NOUN|ที่|SCONJ': 0.047146651372167295,\n",
              " 'ไป|VERB|แล้ว|ADV|ดังกล่าว|DET': 0.05103257992424078,\n",
              " 'ไม่|PART|พบ|VERB|ว่า|SCONJ': 0.03758947582343231,\n",
              " 'ไม่|PART|มี|VERB|การ|NOUN': 0.024287120784995488,\n",
              " 'ไม่|PART|ได้|AUX|ถ่ายอุจจาระ|VERB': 0.05103257992424078}"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "id": "XFjSKJQgAN0t",
        "outputId": "2a0b9768-acf1-4bb9-a2bc-7dd71beb5779"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[((('การ', 'NOUN'), ('แพร่ระบาด', 'VERB'), ('ของ', 'ADP')), 2381),\n",
              " ((('โค', 'NOUN'), ('โร', 'NOUN'), ('นา', 'NOUN')), 1325),\n",
              " ((('หน่วยงาน', 'NOUN'), ('ที่', 'SCONJ'), ('เกี่ยวข้อง', 'VERB')), 1195),\n",
              " ((('ใน', 'ADP'), ('พื้นที่', 'NOUN'), ('จังหวัด', 'NOUN')), 1194),\n",
              " ((('ไวรัส', 'NOUN'), ('โค', 'NOUN'), ('โร', 'NOUN')), 1096),\n",
              " ((('สถานการณ์', 'NOUN'), ('การ', 'NOUN'), ('แพร่ระบาด', 'VERB')), 1065),\n",
              " ((('โรคติดเชื้อ', 'NOUN'), ('ไวรัส', 'NOUN'), ('โค', 'NOUN')), 1064),\n",
              " ((('และ', 'CCONJ'), ('บรรเทา', 'VERB'), ('สาธารณภัย', 'NOUN')), 997),\n",
              " ((('ป้องกัน', 'VERB'), ('และ', 'CCONJ'), ('บรรเทา', 'VERB')), 909),\n",
              " ((('ของ', 'ADP'), ('โรคติดเชื้อ', 'NOUN'), ('ไวรัส', 'NOUN')), 894)]"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "def __sample_cooc_freqs_detection__():\n",
        "  if __is_ipython_kernel__:\n",
        "    global sample_cooc_freqs\n",
        "    sample_cooc_freqs = count_triple_frequency(sample_cooc_data)\n",
        "    display(sample_cooc_freqs[:10])\n",
        "\n",
        "__sample_cooc_freqs_detection__()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0vOkisK6_zoj"
      },
      "outputs": [],
      "source": [
        "def filter_pos_triples(data,keeps = ('NOUN','VERB','NOUN')):\n",
        "    filtered_triples = [ pair for pair in data\n",
        "        if pair[0][0][1] == keeps[0] and pair[0][1][1] == keeps[1] and pair[0][2][1] == keeps[2]\n",
        "    ]\n",
        "    return filtered_triples"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "kd4Hmj_M_z6V",
        "outputId": "88b170d7-73e4-4d9a-8809-0571f9791b9a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[((('ผู้', 'NOUN'), ('ติดเชื้อ', 'VERB'), ('โควิด', 'NOUN')), 600),\n",
              " ((('ผู้', 'NOUN'), ('ติดเชื้อ', 'VERB'), ('ราย', 'NOUN')), 559),\n",
              " ((('สำนักงาน', 'NOUN'), ('ประชาสัมพันธ์', 'VERB'), ('จังหวัด', 'NOUN')), 438),\n",
              " ((('การ', 'NOUN'), ('ฉีดวัคซีน', 'VERB'), ('โควิด', 'NOUN')), 350),\n",
              " ((('การ', 'NOUN'), ('จัด', 'VERB'), ('การแข่งขัน', 'NOUN')), 340),\n",
              " ((('การ', 'NOUN'), ('แพร่ระบาด', 'VERB'), ('โรค', 'NOUN')), 280),\n",
              " ((('ศูนย์', 'NOUN'), ('บริหาร', 'VERB'), ('สถานการณ์', 'NOUN')), 253),\n",
              " ((('รัฐมนตรี', 'NOUN'), ('ว่าการ', 'VERB'), ('กระทรวง', 'NOUN')), 251),\n",
              " ((('การ', 'NOUN'), ('ระบาย', 'VERB'), ('น้ำ', 'NOUN')), 246),\n",
              " ((('กองทุน', 'NOUN'), ('พัฒนา', 'VERB'), ('เด็ก', 'NOUN')), 230),\n",
              " ((('ภูมิลำเนา', 'NOUN'), ('อยู่', 'VERB'), ('ตำบล', 'NOUN')), 212),\n",
              " ((('การ', 'NOUN'), ('ตรวจหา', 'VERB'), ('เชื้อ', 'NOUN')), 183),\n",
              " ((('การ', 'NOUN'), ('เตรียม', 'VERB'), ('ความพร้อม', 'NOUN')), 180),\n",
              " ((('รัฐมนตรี', 'NOUN'), ('ว่าการ', 'VERB'), ('กระทรวงกลาโหม', 'NOUN')), 176),\n",
              " ((('ผล', 'NOUN'), ('เป็น', 'VERB'), ('บวก', 'NOUN')), 165),\n",
              " ((('การบริหาร', 'NOUN'), ('จัดการ', 'VERB'), ('น้ำ', 'NOUN')), 162),\n",
              " ((('การ', 'NOUN'), ('ลง', 'VERB'), ('พื้นที่', 'NOUN')), 159),\n",
              " ((('กลุ่ม', 'NOUN'), ('สัมผัส', 'VERB'), ('ผู้ป่วย', 'NOUN')), 155),\n",
              " ((('โฆษก', 'NOUN'), ('ประจำ', 'VERB'), ('สำนักนายกรัฐมนตรี', 'NOUN')), 154),\n",
              " ((('การ', 'NOUN'), ('ให้', 'VERB'), ('ความช่วยเหลือ', 'NOUN')), 149)]"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "def __sample_filter_pos__():\n",
        "  if __is_ipython_kernel__:\n",
        "    global sample_filtered_cooc\n",
        "    sample_filtered_cooc = filter_pos_triples(sample_cooc_freqs)\n",
        "    display(sample_filtered_cooc[:20])\n",
        "\n",
        "__sample_filter_pos__()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0D902xEqAeXS"
      },
      "outputs": [],
      "source": [
        "def bgs_filter_extreme(bgs_list, min_percent=0.05, max_percent=0.8):\n",
        "  result = list()\n",
        "  bgs_list = sorted(bgs_list, key=itemgetter(1), reverse=True)\n",
        "  most_freq = bgs_list[0][1]\n",
        "  max_freq = most_freq * max_percent\n",
        "  min_freq = most_freq * min_percent\n",
        "\n",
        "  result = [(pair, count) for pair, count in bgs_list if min_freq <= count <= max_freq and pair[0] != pair[1]]\n",
        "  return result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qlNOc8j0CoRP"
      },
      "source": [
        "## 🔮 Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KKBxoDswVFVH"
      },
      "outputs": [],
      "source": [
        "def visualize_cooccurrence(data, file_name=\"thaiconet_result.html\"):\n",
        "    # Phase 1: NetworkX\n",
        "    G = nx.Graph()\n",
        "\n",
        "    # Create a dictionary to store the degree of each node\n",
        "    node_degrees = {}\n",
        "\n",
        "    for triples, freq in data:\n",
        "        sbj = triples[0][0]\n",
        "        pred = triples[1][0]\n",
        "        obj = triples[2][0]\n",
        "\n",
        "        # Update the degree of term1\n",
        "        node_degrees[sbj] = node_degrees.get(sbj, 0) + 1\n",
        "\n",
        "        # Update the degree of term2\n",
        "        node_degrees[obj] = node_degrees.get(obj, 0) + 1\n",
        "\n",
        "        # Add nodes and set their size based on the degree\n",
        "        G.add_node(sbj, size=min(node_degrees[sbj] * 5, 30), node_type = \"sbj\")\n",
        "        G.add_node(obj, size=min(node_degrees[obj] * 5, 30), node_type = \"obj\")\n",
        "\n",
        "        # Add edge\n",
        "        G.add_edge(sbj, obj, weight=freq, label=pred, sbj_verifier = sbj)\n",
        "\n",
        "    # pos = nx.spring_layout(G)\n",
        "    # sizes = [G.nodes[n]['size'] for n in G.nodes()]\n",
        "\n",
        "    # Phase 2: PyviZ\n",
        "    net = Network(height=\"1000px\",\n",
        "                  width=\"100%\",\n",
        "                  notebook=True,\n",
        "                  directed =True,\n",
        "                 )\n",
        "\n",
        "    for node in G.nodes():\n",
        "        net.add_node(node, size=G.nodes[node]['size'])\n",
        "\n",
        "\n",
        "    #display(G.edges(data=True))\n",
        "\n",
        "    for u, v, data in G.edges(data=True):\n",
        "        weight = data['weight']\n",
        "        label = data['label']\n",
        "        sbj_verifier = data['sbj_verifier']\n",
        "\n",
        "        color = \"orange\" if weight > 45 else \"gray\"\n",
        "\n",
        "        if u != sbj_verifier:\n",
        "          u,v = v,u\n",
        "\n",
        "        net.add_edge(u, v, value=weight, color=color, label=label)\n",
        "\n",
        "    net.show_buttons(filter_=['physics'])\n",
        "    net.show(file_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "YZvZAxbAFwPj",
        "outputId": "c858ee85-e7c0-4ddc-9a63-d04489dfc327"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<html>\n",
              "<head>\n",
              "<link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/vis/4.16.1/vis.css\" type=\"text/css\" />\n",
              "<script type=\"text/javascript\" src=\"https://cdnjs.cloudflare.com/ajax/libs/vis/4.16.1/vis-network.min.js\"> </script>\n",
              "<center>\n",
              "<h1></h1>\n",
              "</center>\n",
              "\n",
              "<!-- <link rel=\"stylesheet\" href=\"../node_modules/vis/dist/vis.min.css\" type=\"text/css\" />\n",
              "<script type=\"text/javascript\" src=\"../node_modules/vis/dist/vis.js\"> </script>-->\n",
              "\n",
              "<style type=\"text/css\">\n",
              "\n",
              "        #mynetwork {\n",
              "            width: 100%;\n",
              "            height: 1000px;\n",
              "            background-color: #ffffff;\n",
              "            border: 1px solid lightgray;\n",
              "            position: relative;\n",
              "            float: left;\n",
              "        }\n",
              "\n",
              "        \n",
              "        #loadingBar {\n",
              "            position:absolute;\n",
              "            top:0px;\n",
              "            left:0px;\n",
              "            width: 100%;\n",
              "            height: 1000px;\n",
              "            background-color:rgba(200,200,200,0.8);\n",
              "            -webkit-transition: all 0.5s ease;\n",
              "            -moz-transition: all 0.5s ease;\n",
              "            -ms-transition: all 0.5s ease;\n",
              "            -o-transition: all 0.5s ease;\n",
              "            transition: all 0.5s ease;\n",
              "            opacity:1;\n",
              "        }\n",
              "\n",
              "        #bar {\n",
              "            position:absolute;\n",
              "            top:0px;\n",
              "            left:0px;\n",
              "            width:20px;\n",
              "            height:20px;\n",
              "            margin:auto auto auto auto;\n",
              "            border-radius:11px;\n",
              "            border:2px solid rgba(30,30,30,0.05);\n",
              "            background: rgb(0, 173, 246); /* Old browsers */\n",
              "            box-shadow: 2px 0px 4px rgba(0,0,0,0.4);\n",
              "        }\n",
              "\n",
              "        #border {\n",
              "            position:absolute;\n",
              "            top:10px;\n",
              "            left:10px;\n",
              "            width:500px;\n",
              "            height:23px;\n",
              "            margin:auto auto auto auto;\n",
              "            box-shadow: 0px 0px 4px rgba(0,0,0,0.2);\n",
              "            border-radius:10px;\n",
              "        }\n",
              "\n",
              "        #text {\n",
              "            position:absolute;\n",
              "            top:8px;\n",
              "            left:530px;\n",
              "            width:30px;\n",
              "            height:50px;\n",
              "            margin:auto auto auto auto;\n",
              "            font-size:22px;\n",
              "            color: #000000;\n",
              "        }\n",
              "\n",
              "        div.outerBorder {\n",
              "            position:relative;\n",
              "            top:400px;\n",
              "            width:600px;\n",
              "            height:44px;\n",
              "            margin:auto auto auto auto;\n",
              "            border:8px solid rgba(0,0,0,0.1);\n",
              "            background: rgb(252,252,252); /* Old browsers */\n",
              "            background: -moz-linear-gradient(top,  rgba(252,252,252,1) 0%, rgba(237,237,237,1) 100%); /* FF3.6+ */\n",
              "            background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,rgba(252,252,252,1)), color-stop(100%,rgba(237,237,237,1))); /* Chrome,Safari4+ */\n",
              "            background: -webkit-linear-gradient(top,  rgba(252,252,252,1) 0%,rgba(237,237,237,1) 100%); /* Chrome10+,Safari5.1+ */\n",
              "            background: -o-linear-gradient(top,  rgba(252,252,252,1) 0%,rgba(237,237,237,1) 100%); /* Opera 11.10+ */\n",
              "            background: -ms-linear-gradient(top,  rgba(252,252,252,1) 0%,rgba(237,237,237,1) 100%); /* IE10+ */\n",
              "            background: linear-gradient(to bottom,  rgba(252,252,252,1) 0%,rgba(237,237,237,1) 100%); /* W3C */\n",
              "            filter: progid:DXImageTransform.Microsoft.gradient( startColorstr='#fcfcfc', endColorstr='#ededed',GradientType=0 ); /* IE6-9 */\n",
              "            border-radius:72px;\n",
              "            box-shadow: 0px 0px 10px rgba(0,0,0,0.2);\n",
              "        }\n",
              "        \n",
              "\n",
              "        \n",
              "        #config {\n",
              "            float: left;\n",
              "            width: 400px;\n",
              "            height: 600px;\n",
              "        }\n",
              "        \n",
              "\n",
              "        \n",
              "</style>\n",
              "\n",
              "</head>\n",
              "\n",
              "<body>\n",
              "<div id = \"mynetwork\"></div>\n",
              "\n",
              "<div id=\"loadingBar\">\n",
              "    <div class=\"outerBorder\">\n",
              "        <div id=\"text\">0%</div>\n",
              "        <div id=\"border\">\n",
              "            <div id=\"bar\"></div>\n",
              "        </div>\n",
              "    </div>\n",
              "</div>\n",
              "\n",
              "\n",
              "<div id = \"config\"></div>\n",
              "\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "    // initialize global variables.\n",
              "    var edges;\n",
              "    var nodes;\n",
              "    var network; \n",
              "    var container;\n",
              "    var options, data;\n",
              "\n",
              "    \n",
              "    // This method is responsible for drawing the graph, returns the drawn network\n",
              "    function drawGraph() {\n",
              "        var container = document.getElementById('mynetwork');\n",
              "        \n",
              "        \n",
              "\n",
              "        // parsing and collecting nodes and edges from the python\n",
              "        nodes = new vis.DataSet([{\"id\": \"\\u0e1c\\u0e39\\u0e49\", \"label\": \"\\u0e1c\\u0e39\\u0e49\", \"shape\": \"dot\", \"size\": 30}, {\"id\": \"\\u0e42\\u0e04\\u0e27\\u0e34\\u0e14\", \"label\": \"\\u0e42\\u0e04\\u0e27\\u0e34\\u0e14\", \"shape\": \"dot\", \"size\": 10}, {\"id\": \"\\u0e23\\u0e32\\u0e22\", \"label\": \"\\u0e23\\u0e32\\u0e22\", \"shape\": \"dot\", \"size\": 30}, {\"id\": \"\\u0e2a\\u0e33\\u0e19\\u0e31\\u0e01\\u0e07\\u0e32\\u0e19\", \"label\": \"\\u0e2a\\u0e33\\u0e19\\u0e31\\u0e01\\u0e07\\u0e32\\u0e19\", \"shape\": \"dot\", \"size\": 25}, {\"id\": \"\\u0e08\\u0e31\\u0e07\\u0e2b\\u0e27\\u0e31\\u0e14\", \"label\": \"\\u0e08\\u0e31\\u0e07\\u0e2b\\u0e27\\u0e31\\u0e14\", \"shape\": \"dot\", \"size\": 30}, {\"id\": \"\\u0e28\\u0e39\\u0e19\\u0e22\\u0e4c\", \"label\": \"\\u0e28\\u0e39\\u0e19\\u0e22\\u0e4c\", \"shape\": \"dot\", \"size\": 30}, {\"id\": \"\\u0e2a\\u0e16\\u0e32\\u0e19\\u0e01\\u0e32\\u0e23\\u0e13\\u0e4c\", \"label\": \"\\u0e2a\\u0e16\\u0e32\\u0e19\\u0e01\\u0e32\\u0e23\\u0e13\\u0e4c\", \"shape\": \"dot\", \"size\": 15}, {\"id\": \"\\u0e23\\u0e31\\u0e10\\u0e21\\u0e19\\u0e15\\u0e23\\u0e35\", \"label\": \"\\u0e23\\u0e31\\u0e10\\u0e21\\u0e19\\u0e15\\u0e23\\u0e35\", \"shape\": \"dot\", \"size\": 25}, {\"id\": \"\\u0e01\\u0e23\\u0e30\\u0e17\\u0e23\\u0e27\\u0e07\", \"label\": \"\\u0e01\\u0e23\\u0e30\\u0e17\\u0e23\\u0e27\\u0e07\", \"shape\": \"dot\", \"size\": 5}, {\"id\": \"\\u0e01\\u0e2d\\u0e07\\u0e17\\u0e38\\u0e19\", \"label\": \"\\u0e01\\u0e2d\\u0e07\\u0e17\\u0e38\\u0e19\", \"shape\": \"dot\", \"size\": 15}, {\"id\": \"\\u0e40\\u0e14\\u0e47\\u0e01\", \"label\": \"\\u0e40\\u0e14\\u0e47\\u0e01\", \"shape\": \"dot\", \"size\": 10}, {\"id\": \"\\u0e20\\u0e39\\u0e21\\u0e34\\u0e25\\u0e33\\u0e40\\u0e19\\u0e32\", \"label\": \"\\u0e20\\u0e39\\u0e21\\u0e34\\u0e25\\u0e33\\u0e40\\u0e19\\u0e32\", \"shape\": \"dot\", \"size\": 5}, {\"id\": \"\\u0e15\\u0e33\\u0e1a\\u0e25\", \"label\": \"\\u0e15\\u0e33\\u0e1a\\u0e25\", 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              "\n",
              "        // adding nodes and edges to the graph\n",
              "        data = {nodes: nodes, edges: edges};\n",
              "\n",
              "        var options = {\n",
              "    \"configure\": {\n",
              "        \"enabled\": true,\n",
              "        \"filter\": [\n",
              "            \"physics\"\n",
              "        ]\n",
              "    },\n",
              "    \"edges\": {\n",
              "        \"color\": {\n",
              "            \"inherit\": true\n",
              "        },\n",
              "        \"smooth\": {\n",
              "            \"enabled\": false,\n",
              "            \"type\": \"continuous\"\n",
              "        }\n",
              "    },\n",
              "    \"interaction\": {\n",
              "        \"dragNodes\": true,\n",
              "        \"hideEdgesOnDrag\": false,\n",
              "        \"hideNodesOnDrag\": false\n",
              "    },\n",
              "    \"physics\": {\n",
              "        \"enabled\": true,\n",
              "        \"stabilization\": {\n",
              "            \"enabled\": true,\n",
              "            \"fit\": true,\n",
              "            \"iterations\": 1000,\n",
              "            \"onlyDynamicEdges\": false,\n",
              "            \"updateInterval\": 50\n",
              "        }\n",
              "    }\n",
              "};\n",
              "        \n",
              "        \n",
              "\n",
              "        \n",
              "        // if this network requires displaying the configure window,\n",
              "        // put it in its div\n",
              "        options.configure[\"container\"] = document.getElementById(\"config\");\n",
              "        \n",
              "\n",
              "        network = new vis.Network(container, data, options);\n",
              "\t \n",
              "        \n",
              "\n",
              "\n",
              "        \n",
              "        network.on(\"stabilizationProgress\", function(params) {\n",
              "      \t\tdocument.getElementById('loadingBar').removeAttribute(\"style\");\n",
              "\t        var maxWidth = 496;\n",
              "\t        var minWidth = 20;\n",
              "\t        var widthFactor = params.iterations/params.total;\n",
              "\t        var width = Math.max(minWidth,maxWidth * widthFactor);\n",
              "\n",
              "\t        document.getElementById('bar').style.width = width + 'px';\n",
              "\t        document.getElementById('text').innerHTML = Math.round(widthFactor*100) + '%';\n",
              "\t    });\n",
              "\t    network.once(\"stabilizationIterationsDone\", function() {\n",
              "\t        document.getElementById('text').innerHTML = '100%';\n",
              "\t        document.getElementById('bar').style.width = '496px';\n",
              "\t        document.getElementById('loadingBar').style.opacity = 0;\n",
              "\t        // really clean the dom element\n",
              "\t        setTimeout(function () {document.getElementById('loadingBar').style.display = 'none';}, 500);\n",
              "\t    });\n",
              "        \n",
              "\n",
              "        return network;\n",
              "\n",
              "    }\n",
              "\n",
              "    drawGraph();\n",
              "\n",
              "</script>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "def __sample__visualization__():\n",
        "  if __is_ipython_kernel__:\n",
        "\n",
        "    custom_stop_words = ['การ']\n",
        "    selected = []\n",
        "    for triple in list(sample_filtered_cooc):\n",
        "\n",
        "      is_valid = True\n",
        "      for pairs in triple[0]:\n",
        "        if pairs[0] in custom_stop_words:\n",
        "          is_valid = False\n",
        "          break\n",
        "      if is_valid:\n",
        "        selected.append(triple)\n",
        "\n",
        "    visualize_cooccurrence(selected[:200])\n",
        "    display(HTML(\"thaiconet_result.html\"))\n",
        "\n",
        "__sample__visualization__()"
      ]
    }
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