{
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!date\n",
        "!python --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rsu3rv5lhpUt",
        "outputId": "711a91ea-b62a-4c34-f55e-8128c4911e9f"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Thu Jun  6 03:11:24 AM UTC 2024\n",
            "Python 3.10.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v8IkNtWTYpm6"
      },
      "source": [
        "# トピックモデルによるクラスタリング\n",
        "トピックモデルとは文書中の単語出現分布を元に傾向（≒トピックらしきもの）を観察しようとするアプローチで、クラスタリングの一種である。なお、一般的なクラスタリング（例えば[k平均法](https://ja.wikipedia.org/wiki/K平均法)）では一つのサンプルが一つのクラスタに属するという前提でグルーピングを行うのに対し、トピックモデルでは一つのサンプルが複数のクラスタを内包しているという前提でグルーピングを行う。次の例を眺めるとイメージをつかみやすいだろう。\n",
        "\n",
        "- 例1: [トピックモデル入門：WikipediaをLDAモデル化してみた](https://recruit.gmo.jp/engineer/jisedai/blog/topic-model/)\n",
        "- 例2: [Wikipedia: Topic model](https://en.wikipedia.org/wiki/Topic_model)\n",
        "\n",
        "基本的には文書を BoW (CountVectrizor) やそれの重みを調整した TF-IDF 等の「文書単語行列」を作成し、ここから文書館類似度や単語間類似度を元に集約（≒次元削減）を試みる。文書単語行列の作成方法や次元削減方法、類似度の求め方などで様々なアルゴリズムが提案されている。ここでは (1) Bow + [LDA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html) によりトピックモデルを行い、[PyLDAvis](https://github.com/bmabey/pyLDAvis)による可視化を通してトピックを観察してみよう。\n",
        "\n",
        "なお、トピックモデルの注意点として、トピックそのものは人手による解釈が求められる点が挙げられる。例えば先に上げた[トピックモデル入門：WikipediaをLDAモデル化してみた](https://recruit.gmo.jp/engineer/jisedai/blog/topic-model/)における図2（下図）では「政治」「スポーツ」「国際」といったトピックが並んでいるが、実際には「4-1. トピック観察」を行う必要がある。実際に観察してみよう。"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# spacy, ginzaインストール\n",
        "!pip install -U ginza ja_ginza\n",
        "\n",
        "# PyLDAvis\n",
        "!pip install pyldavis"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PNA5onfzhrrq",
        "outputId": "819e91e0-2818-466a-d0a4-991bfe450ff8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Installing collected packages: SudachiPy, plac, SudachiDict-core, ginza, ja_ginza\n",
            "Successfully installed SudachiDict-core-20240409 SudachiPy-0.6.8 ginza-5.2.0 ja_ginza-5.2.0 plac-1.4.3\n",
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            "Successfully installed funcy-2.0 pyldavis-3.4.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zXJ0jzI1Ypm-"
      },
      "source": [
        "## データの準備\n",
        "これまで見てきたいつものやつ。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0DaZZaFJYpm-",
        "outputId": "b1336ea5-de46-4d0b-c83b-525a39d1606c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100 34834  100 34834    0     0  15859      0  0:00:02  0:00:02 --:--:-- 15862\n"
          ]
        }
      ],
      "source": [
        "!curl -O https://ie.u-ryukyu.ac.jp/~tnal/2022/dm/static/r_assesment.pkl"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "qCxzwznFYpnA",
        "outputId": "38eaef78-5968-4394-dde9-576e9e44a6bf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   title  grade  required     q_id                       comment\n",
              "0  工業数学Ⅰ      1      True  Q21 (1)                          特になし\n",
              "1  工業数学Ⅰ      1      True  Q21 (2)            正直わかりずらい。むだに間があるし。\n",
              "2  工業数学Ⅰ      1      True  Q21 (2)          例題を取り入れて理解しやすくしてほしい。\n",
              "3  工業数学Ⅰ      1      True  Q21 (2)                          特になし\n",
              "4  工業数学Ⅰ      1      True  Q21 (2)  スライドに書く文字をもう少しわかりやすくして欲しいです。"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "assesment_df",
              "summary": "{\n  \"name\": \"assesment_df\",\n  \"rows\": 170,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"\\u5de5\\u696d\\u6570\\u5b66\\u2160\",\n          \"\\u6280\\u8853\\u8005\\u306e\\u502b\\u7406\",\n          \"\\u30a2\\u30eb\\u30b4\\u30ea\\u30ba\\u30e0\\u3068\\u30c7\\u30fc\\u30bf\\u69cb\\u9020\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"required\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"q_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Q21 (2)\",\n          \"Q22\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"comment\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 153,\n        \"samples\": [\n          \"\\u30fb\\u6559\\u79d1\\u66f8\\u304c\\u5fc5\\u8981\\u306a\\u306e\\u304b\\u5fc5\\u8981\\u3067\\u306a\\u3044\\u306e\\u304b\\u304c\\u66d6\\u6627\\u306a\\u307e\\u307e\\u6388\\u696d\\u304c\\u59cb\\u307e\\u308a\\u3001\\u975e\\u5e38\\u306b\\u4e0d\\u5b89\\u3060\\u3063\\u305f\\u305f\\u3081\\u3001\\u6559\\u79d1\\u66f8\\u304c\\u5fc5\\u9808\\u304b\\u305d\\u3046\\u3067\\u306a\\u3044\\u306e\\u304b\\u306f\\u6700\\u521d\\u306b\\u306f\\u3063\\u304d\\u308a\\u3057\\u3066\\u6b32\\u3057\\u3044\\u3002\\r\\n\\u30fb\\u8ab2\\u984c\\u3092\\u51fa\\u3059\\u3060\\u3051\\u51fa\\u3055\\u305b\\u3066\\u304a\\u3044\\u3066\\u3001\\u63a1\\u70b9\\u3082\\u305b\\u305a\\u3001\\u3069\\u3046\\u3044\\u3063\\u305f\\u89e3\\u7b54\\u304c\\u6b63\\u3057\\u3044\\u306e\\u304b\\u3068\\u3044\\u3063\\u305f\\u6307\\u91dd\\u3082\\u51fa\\u3059\\u306e\\u304c\\u3068\\u3066\\u3082\\u9045\\u3044\\u3002\\u8ab2\\u984c\\u306f\\u89e3\\u304f\\u3060\\u3051\\u3067\\u306f\\u77e5\\u8b58\\u306e\\u5b9a\\u7740\\u306b\\u3064\\u306a\\u304c\\u3089\\u306a\\u3044\\u3068\\u601d\\u3044\\u307e\\u3059\\u304c\\u3001\\u305d\\u3053\\u3089\\u3078\\u3093\\u306f\\u3069\\u3046\\u306a\\u3093\\u3067\\u3057\\u3087\\u3046\\u304b\\u3002\\r\\n\\u30fb\\u914d\\u5e03\\u8cc7\\u6599\\u3068\\u3057\\u3066\\u3001\\u904e\\u53bb\\u554f\\u3082\\u914d\\u5e03\\u3057\\u3066\\u304f\\u308c\\u308b\\u3068\\u3068\\u3066\\u3082\\u52a9\\u304b\\u308b\\u306a\\u3001\\u3068\\u601d\\u3044\\u307e\\u3059\\u3002\\u3054\\u691c\\u8a0e\\u304a\\u9858\\u3044\\u3057\\u307e\\u3059\\u3002\",\n          \"\\u30fb\\u4e2d\\u9593\\u30c6\\u30b9\\u30c8\\u3092\\u5ef6\\u671f\\u3057\\u7d9a\\u3051\\u3001\\u6700\\u7d42\\u7684\\u306b\\u4e2d\\u9593\\u30fb\\u671f\\u672b\\u8a66\\u9a13\\u3092\\uff12\\u9031\\u7d9a\\u3051\\u3066\\u3084\\u308b\\u3053\\u3068\\u3068\\u306a\\u308a\\u3001\\u8a08\\u753b\\u6027\\u304c\\u6b20\\u3051\\u3066\\u3044\\u308b\\u3002\\r\\n\\u30fb\\u914d\\u5e03\\u8cc7\\u6599\\u306e\\u8aa4\\u5b57\\u8131\\u5b57\\u304c\\u591a\\u3059\\u304e\\u308b\\u3002\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "import collections\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import spacy\n",
        "from wordcloud import WordCloud\n",
        "\n",
        "nlp = spacy.load(\"ja_ginza\")\n",
        "\n",
        "assesment_df = pd.read_pickle('r_assesment.pkl')\n",
        "assesment_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 691
        },
        "id": "Stv68NvmYpnB",
        "outputId": "b547fc32-cf8c-4968-d1b3-483ada3ca3ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        title  grade  required     q_id  \\\n",
              "0       工業数学Ⅰ      1      True  Q21 (1)   \n",
              "1       工業数学Ⅰ      1      True  Q21 (2)   \n",
              "2       工業数学Ⅰ      1      True  Q21 (2)   \n",
              "3       工業数学Ⅰ      1      True  Q21 (2)   \n",
              "4       工業数学Ⅰ      1      True  Q21 (2)   \n",
              "..        ...    ...       ...      ...   \n",
              "165  データマイニング      3     False      Q22   \n",
              "166  ICT実践英語Ⅰ      3     False      Q22   \n",
              "167   知能情報実験Ⅲ      3      True  Q21 (2)   \n",
              "168   知能情報実験Ⅲ      3      True      Q22   \n",
              "169   知能情報実験Ⅲ      3      True      Q22   \n",
              "\n",
              "                                               comment  \\\n",
              "0                                                 特になし   \n",
              "1                                   正直わかりずらい。むだに間があるし。   \n",
              "2                                 例題を取り入れて理解しやすくしてほしい。   \n",
              "3                                                 特になし   \n",
              "4                         スライドに書く文字をもう少しわかりやすくして欲しいです。   \n",
              "..                                                 ...   \n",
              "165  課題が難しいものが多く、時間を多くとってもらえたのは非常に良かったですがかなりきつかったです...   \n",
              "166                            オンラインなどで顔を合わせてやりたかったです。   \n",
              "167  unityの操作方法の説明などを最初に行ってもらえたらもう少しスムーズにできたのではないかと思う。   \n",
              "168  それぞれに任せるといった形で進められたものだったのでそれなりに進めやすかったですが、オンライ...   \n",
              "169  モバイルアプリ班\\r\\nHTML/CSS，JavaScriptなどを用いてアプリケーションを...   \n",
              "\n",
              "                                                wakati  \n",
              "0                                                特に なし  \n",
              "1                                   正直 わかる ずらい むだ 間 ある  \n",
              "2                                       例題 取り入れる 理解 する  \n",
              "3                                                特に なし  \n",
              "4                              スライド 書く 文字 もう 少し わかる する  \n",
              "..                                                 ...  \n",
              "165       課題 難しい もの 多い 時間 多い とる もらえる 非常 良い かなり きつい ござる  \n",
              "166                                    オンライン 顔 合わせる やる  \n",
              "167         unity 操作方法 説明 最初 行く もらえる もう 少し スムーズ できる 思う  \n",
              "168  それぞれ 任せる いう 形 進める もの なり 進める オンライン 班 員 指導 全く する...  \n",
              "169  モバイルアプリ 班 \\r\\n HTML CSS javascript 用いる アプリケーショ...  \n",
              "\n",
              "[170 rows x 6 columns]"
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              "      <td>工業数学Ⅰ</td>\n",
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              "      <td>True</td>\n",
              "      <td>Q21 (1)</td>\n",
              "      <td>特になし</td>\n",
              "      <td>特に なし</td>\n",
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              "      <th>1</th>\n",
              "      <td>工業数学Ⅰ</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>Q21 (2)</td>\n",
              "      <td>正直わかりずらい。むだに間があるし。</td>\n",
              "      <td>正直 わかる ずらい むだ 間 ある</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>工業数学Ⅰ</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>Q21 (2)</td>\n",
              "      <td>例題を取り入れて理解しやすくしてほしい。</td>\n",
              "      <td>例題 取り入れる 理解 する</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>工業数学Ⅰ</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>Q21 (2)</td>\n",
              "      <td>特になし</td>\n",
              "      <td>特に なし</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>工業数学Ⅰ</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>Q21 (2)</td>\n",
              "      <td>スライドに書く文字をもう少しわかりやすくして欲しいです。</td>\n",
              "      <td>スライド 書く 文字 もう 少し わかる する</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>データマイニング</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "      <td>Q22</td>\n",
              "      <td>課題が難しいものが多く、時間を多くとってもらえたのは非常に良かったですがかなりきつかったです...</td>\n",
              "      <td>課題 難しい もの 多い 時間 多い とる もらえる 非常 良い かなり きつい ござる</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>ICT実践英語Ⅰ</td>\n",
              "      <td>3</td>\n",
              "      <td>False</td>\n",
              "      <td>Q22</td>\n",
              "      <td>オンラインなどで顔を合わせてやりたかったです。</td>\n",
              "      <td>オンライン 顔 合わせる やる</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>知能情報実験Ⅲ</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>Q21 (2)</td>\n",
              "      <td>unityの操作方法の説明などを最初に行ってもらえたらもう少しスムーズにできたのではないかと思う。</td>\n",
              "      <td>unity 操作方法 説明 最初 行く もらえる もう 少し スムーズ できる 思う</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>知能情報実験Ⅲ</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>Q22</td>\n",
              "      <td>それぞれに任せるといった形で進められたものだったのでそれなりに進めやすかったですが、オンライ...</td>\n",
              "      <td>それぞれ 任せる いう 形 進める もの なり 進める オンライン 班 員 指導 全く する...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>知能情報実験Ⅲ</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>Q22</td>\n",
              "      <td>モバイルアプリ班\\r\\nHTML/CSS，JavaScriptなどを用いてアプリケーションを...</td>\n",
              "      <td>モバイルアプリ 班 \\r\\n HTML CSS javascript 用いる アプリケーショ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>170 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0dd852e7-99d7-4c81-89fc-16c5d18aa4d0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-0dd852e7-99d7-4c81-89fc-16c5d18aa4d0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-0dd852e7-99d7-4c81-89fc-16c5d18aa4d0');\n",
              "        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",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a9e2a51d-4f36-49e0-9ef1-7e6b45732da3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a9e2a51d-4f36-49e0-9ef1-7e6b45732da3')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    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",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      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-a9e2a51d-4f36-49e0-9ef1-7e6b45732da3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "assesment_df",
              "summary": "{\n  \"name\": \"assesment_df\",\n  \"rows\": 170,\n  \"fields\": [\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"\\u5de5\\u696d\\u6570\\u5b66\\u2160\",\n          \"\\u6280\\u8853\\u8005\\u306e\\u502b\\u7406\",\n          \"\\u30a2\\u30eb\\u30b4\\u30ea\\u30ba\\u30e0\\u3068\\u30c7\\u30fc\\u30bf\\u69cb\\u9020\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"grade\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1,\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"required\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"q_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Q21 (2)\",\n          \"Q22\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"comment\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 153,\n        \"samples\": [\n          \"\\u30fb\\u6559\\u79d1\\u66f8\\u304c\\u5fc5\\u8981\\u306a\\u306e\\u304b\\u5fc5\\u8981\\u3067\\u306a\\u3044\\u306e\\u304b\\u304c\\u66d6\\u6627\\u306a\\u307e\\u307e\\u6388\\u696d\\u304c\\u59cb\\u307e\\u308a\\u3001\\u975e\\u5e38\\u306b\\u4e0d\\u5b89\\u3060\\u3063\\u305f\\u305f\\u3081\\u3001\\u6559\\u79d1\\u66f8\\u304c\\u5fc5\\u9808\\u304b\\u305d\\u3046\\u3067\\u306a\\u3044\\u306e\\u304b\\u306f\\u6700\\u521d\\u306b\\u306f\\u3063\\u304d\\u308a\\u3057\\u3066\\u6b32\\u3057\\u3044\\u3002\\r\\n\\u30fb\\u8ab2\\u984c\\u3092\\u51fa\\u3059\\u3060\\u3051\\u51fa\\u3055\\u305b\\u3066\\u304a\\u3044\\u3066\\u3001\\u63a1\\u70b9\\u3082\\u305b\\u305a\\u3001\\u3069\\u3046\\u3044\\u3063\\u305f\\u89e3\\u7b54\\u304c\\u6b63\\u3057\\u3044\\u306e\\u304b\\u3068\\u3044\\u3063\\u305f\\u6307\\u91dd\\u3082\\u51fa\\u3059\\u306e\\u304c\\u3068\\u3066\\u3082\\u9045\\u3044\\u3002\\u8ab2\\u984c\\u306f\\u89e3\\u304f\\u3060\\u3051\\u3067\\u306f\\u77e5\\u8b58\\u306e\\u5b9a\\u7740\\u306b\\u3064\\u306a\\u304c\\u3089\\u306a\\u3044\\u3068\\u601d\\u3044\\u307e\\u3059\\u304c\\u3001\\u305d\\u3053\\u3089\\u3078\\u3093\\u306f\\u3069\\u3046\\u306a\\u3093\\u3067\\u3057\\u3087\\u3046\\u304b\\u3002\\r\\n\\u30fb\\u914d\\u5e03\\u8cc7\\u6599\\u3068\\u3057\\u3066\\u3001\\u904e\\u53bb\\u554f\\u3082\\u914d\\u5e03\\u3057\\u3066\\u304f\\u308c\\u308b\\u3068\\u3068\\u3066\\u3082\\u52a9\\u304b\\u308b\\u306a\\u3001\\u3068\\u601d\\u3044\\u307e\\u3059\\u3002\\u3054\\u691c\\u8a0e\\u304a\\u9858\\u3044\\u3057\\u307e\\u3059\\u3002\",\n          \"\\u30fb\\u4e2d\\u9593\\u30c6\\u30b9\\u30c8\\u3092\\u5ef6\\u671f\\u3057\\u7d9a\\u3051\\u3001\\u6700\\u7d42\\u7684\\u306b\\u4e2d\\u9593\\u30fb\\u671f\\u672b\\u8a66\\u9a13\\u3092\\uff12\\u9031\\u7d9a\\u3051\\u3066\\u3084\\u308b\\u3053\\u3068\\u3068\\u306a\\u308a\\u3001\\u8a08\\u753b\\u6027\\u304c\\u6b20\\u3051\\u3066\\u3044\\u308b\\u3002\\r\\n\\u30fb\\u914d\\u5e03\\u8cc7\\u6599\\u306e\\u8aa4\\u5b57\\u8131\\u5b57\\u304c\\u591a\\u3059\\u304e\\u308b\\u3002\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wakati\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 153,\n        \"samples\": [\n          \"\\u6559\\u79d1\\u66f8 \\u5fc5\\u8981 \\u5fc5\\u8981 \\u66d6\\u6627 \\u307e\\u307e \\u6388\\u696d \\u59cb\\u307e\\u308b \\u975e\\u5e38 \\u4e0d\\u5b89 \\u305f\\u3081 \\u6559\\u79d1\\u66f8 \\u5fc5\\u9808 \\u305d\\u3046 \\u6700\\u521d \\u306f\\u3063\\u304d\\u308a \\r\\n \\u8ab2\\u984c \\u51fa\\u3059 \\u51fa\\u3059 \\u304a\\u304f \\u63a1\\u70b9 \\u3059\\u308b \\u3069\\u3046 \\u3044\\u3046 \\u89e3\\u7b54 \\u6b63\\u3057\\u3044 \\u3044\\u3046 \\u6307\\u91dd \\u51fa\\u3059 \\u3068\\u3066\\u3082 \\u9045\\u3044 \\u8ab2\\u984c \\u89e3\\u304f \\u77e5\\u8b58 \\u5b9a\\u7740 \\u3064\\u306a\\u304c\\u308b \\u601d\\u3046 \\u3089 \\u3078\\u3093 \\u3069\\u3046 \\r\\n \\u914d\\u5e03\\u8cc7\\u6599 \\u904e\\u53bb\\u554f \\u914d\\u5e03 \\u304f\\u308c\\u308b \\u3068\\u3066\\u3082 \\u52a9\\u304b\\u308b \\u601d\\u3046 \\u3054 \\u691c\\u8a0e \\u304a \\u9858\\u3046\",\n          \"\\u4e2d\\u9593 \\u30c6\\u30b9\\u30c8 \\u5ef6\\u671f \\u7d9a\\u3051\\u308b \\u6700\\u7d42\\u7684 \\u4e2d\\u9593 \\u671f\\u672b \\u8a66\\u9a13 \\u9031 \\u7d9a\\u3051\\u308b \\u3084\\u308b \\u3053\\u3068 \\u306a\\u308b \\u8a08\\u753b\\u6027 \\u6b20\\u3051\\u308b \\u3044\\u308b \\r\\n \\u914d\\u5e03\\u8cc7\\u6599 \\u8aa4\\u5b57 \\u8131\\u5b57 \\u591a\\u3044 \\u3059\\u304e\\u308b\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "# 分かち書き\n",
        "poses = ['PROPN', 'NOUN', 'VERB', 'ADJ', 'ADV'] #名詞、動詞、形容詞、形容動詞\n",
        "\n",
        "assesment_df['wakati'] = ''\n",
        "for index, comment in enumerate(assesment_df['comment']):\n",
        "    doc = nlp(comment)\n",
        "    wakati_words = []\n",
        "    for token in doc:\n",
        "        if token.pos_ in poses:\n",
        "            wakati_words.append(token.lemma_)\n",
        "    wakati_text = ' '.join(wakati_words)\n",
        "    assesment_df.at[index, 'wakati'] = wakati_text\n",
        "\n",
        "assesment_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g85bcizRYpnB"
      },
      "source": [
        "## 文書ベクトルの作成\n",
        "ここでは CountVectorizer (Bag-of-Words) で作成してみよう。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJNIMbe3YpnB",
        "outputId": "449ef352-818b-47f2-a4e9-fb0f435939cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "bow_tf_vector.shape =  (170, 740)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "stop_words = ['こと', '\\r\\n', 'ため', '思う', 'いる', 'ある', 'する', 'なる']\n",
        "vectorizer = CountVectorizer(stop_words=stop_words)\n",
        "bow_tf_vector = vectorizer.fit_transform(assesment_df['wakati'])\n",
        "print('bow_tf_vector.shape = ', bow_tf_vector.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hv06M0vyYpnC"
      },
      "source": [
        "## LDAによるトピックモデル解析\n",
        "sklearnでは [LatentDirichletAllocation](https://scikit-learn.org/stable/modules/decomposition.html?highlight=lda#latent-dirichlet-allocation-lda) として用意されている。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQu3CNFLYpnC",
        "outputId": "6cc3487c-5f43-4ae7-a7c3-790535c24011"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iteration: 1 of max_iter: 100\n",
            "iteration: 2 of max_iter: 100\n",
            "iteration: 3 of max_iter: 100\n",
            "iteration: 4 of max_iter: 100\n",
            "iteration: 5 of max_iter: 100\n",
            "iteration: 6 of max_iter: 100\n",
            "iteration: 7 of max_iter: 100\n",
            "iteration: 8 of max_iter: 100\n",
            "iteration: 9 of max_iter: 100\n",
            "iteration: 10 of max_iter: 100\n",
            "iteration: 11 of max_iter: 100\n",
            "iteration: 12 of max_iter: 100\n",
            "iteration: 13 of max_iter: 100\n",
            "iteration: 14 of max_iter: 100\n",
            "iteration: 15 of max_iter: 100\n",
            "iteration: 16 of max_iter: 100\n",
            "iteration: 17 of max_iter: 100\n",
            "iteration: 18 of max_iter: 100\n",
            "iteration: 19 of max_iter: 100\n",
            "iteration: 20 of max_iter: 100\n",
            "iteration: 21 of max_iter: 100\n",
            "iteration: 22 of max_iter: 100\n",
            "iteration: 23 of max_iter: 100\n",
            "iteration: 24 of max_iter: 100\n",
            "iteration: 25 of max_iter: 100\n",
            "iteration: 26 of max_iter: 100\n",
            "iteration: 27 of max_iter: 100\n",
            "iteration: 28 of max_iter: 100\n",
            "iteration: 29 of max_iter: 100\n",
            "iteration: 30 of max_iter: 100\n",
            "iteration: 31 of max_iter: 100\n",
            "iteration: 32 of max_iter: 100\n",
            "iteration: 33 of max_iter: 100\n",
            "iteration: 34 of max_iter: 100\n",
            "iteration: 35 of max_iter: 100\n",
            "iteration: 36 of max_iter: 100\n",
            "iteration: 37 of max_iter: 100\n",
            "iteration: 38 of max_iter: 100\n",
            "iteration: 39 of max_iter: 100\n",
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            "iteration: 99 of max_iter: 100\n",
            "iteration: 100 of max_iter: 100\n"
          ]
        }
      ],
      "source": [
        "from sklearn.decomposition import LatentDirichletAllocation\n",
        "\n",
        "NUM_TOPICS = 20 #トピック数\n",
        "max_iter = 100  #LDAによる学習回数\n",
        "lda = LatentDirichletAllocation(n_components=NUM_TOPICS, max_iter=max_iter, learning_method='online',verbose=True)\n",
        "data_lda = lda.fit_transform(bow_tf_vector)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m0o0qSJPYpnC"
      },
      "source": [
        "## トピックの観察。\n",
        "- [pyLDAvis](https://github.com/bmabey/pyLDAvis)によりトピックを観察してみよう。\n",
        "- 下図の左側がトピック分布を表している。丸の大きさがトピック内に含まれる文書数、丸と丸の距離はトピック間の距離。\n",
        "- 下図の右側が単語の発生頻度を表している。\n",
        "  - トピックを選択するとそのトピックにおける単語の発生頻度を観察できる。\n",
        "  - 単語を選択すると、その単語がどのようにトピック分布上にバラけているかを観察できる。"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pyLDAvis.lda_model"
      ],
      "metadata": {
        "id": "_uyCmzIRi6gR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 915
        },
        "id": "Fo0-QNPTYpnD",
        "outputId": "e0d868fc-6fcd-4c85-b40f-792462207b93"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PreparedData(topic_coordinates=                x           y  topics  cluster       Freq\n",
              "topic                                                    \n",
              "14    -128.204605  -73.728752       1        1  53.340700\n",
              "16     -25.731833  273.743103       2        1   8.888549\n",
              "8     -172.758301   49.549717       3        1   7.389835\n",
              "10    -140.080338  191.974487       4        1   4.121394\n",
              "15      27.686457  151.515656       5        1   3.507018\n",
              "9      202.026978   45.057007       6        1   2.828444\n",
              "17      78.453629   28.120079       7        1   2.666101\n",
              "4     -253.791046  -67.026825       8        1   2.596971\n",
              "3       55.223969 -263.009918       9        1   2.467206\n",
              "6     -108.504471 -295.491272      10        1   1.696712\n",
              "18      -8.585955  -56.882542      11        1   1.690827\n",
              "19    -271.293854  139.392059      12        1   1.621914\n",
              "7      195.453903 -205.140854      13        1   1.581493\n",
              "1     -358.715942    9.534597      14        1   1.288830\n",
              "5      -47.121204 -174.312210      15        1   1.181598\n",
              "13     160.504410  183.042816      16        1   1.035671\n",
              "12     272.101196  -70.086845      17        1   0.802800\n",
              "0      -53.974472   60.768681      18        1   0.586825\n",
              "11    -216.418228 -196.161316      19        1   0.353556\n",
              "2      103.390175 -108.375671      20        1   0.353556, topic_info=     Term       Freq      Total Category  logprob  loglift\n",
              "621    良い  23.000000  23.000000  Default  30.0000  30.0000\n",
              "439    授業  38.000000  38.000000  Default  29.0000  29.0000\n",
              "662    試験  28.000000  28.000000  Default  28.0000  28.0000\n",
              "546    特に   8.000000   8.000000  Default  27.0000  27.0000\n",
              "80    とても  24.000000  24.000000  Default  26.0000  26.0000\n",
              "..    ...        ...        ...      ...      ...      ...\n",
              "23     いう   0.009901  19.709635  Topic20  -6.6065  -1.9513\n",
              "218  休み時間   0.009901   2.204622  Topic20  -6.6065   0.2392\n",
              "93     まだ   0.009901   3.498658  Topic20  -6.6065  -0.2226\n",
              "648    解説   0.009901   3.726482  Topic20  -6.6065  -0.2857\n",
              "52    しょう   0.009901   1.094344  Topic20  -6.6065   0.9396\n",
              "\n",
              "[946 rows x 6 columns], token_table=      Topic      Freq    Term\n",
              "term                         \n",
              "0         2  0.215741      cm\n",
              "0         3  0.431483      cm\n",
              "0         6  0.215741      cm\n",
              "4         2  0.552671  github\n",
              "8         9  0.585832      it\n",
              "...     ...       ...     ...\n",
              "733       1  0.704070      願う\n",
              "733       3  0.176017      願う\n",
              "734       1  0.918969      高い\n",
              "736       1  0.923311      高校\n",
              "737       3  0.892934     魅力的\n",
              "\n",
              "[513 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[15, 17, 9, 11, 16, 10, 18, 5, 4, 7, 19, 20, 8, 2, 6, 14, 13, 1, 12, 3])"
            ],
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el1471360154308497445765527936\" style=\"background-color:white;\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
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\"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [15, 17, 9, 11, 16, 10, 18, 5, 4, 7, 19, 20, 8, 2, 6, 14, 13, 1, 12, 3]};\n",
              "\n",
              "function LDAvis_load_lib(url, callback){\n",
              "  var s = document.createElement('script');\n",
              "  s.src = url;\n",
              "  s.async = true;\n",
              "  s.onreadystatechange = s.onload = callback;\n",
              "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
              "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
              "}\n",
              "\n",
              "if(typeof(LDAvis) !== \"undefined\"){\n",
              "   // already loaded: just create the visualization\n",
              "   !function(LDAvis){\n",
              "       new LDAvis(\"#\" + \"ldavis_el1471360154308497445765527936\", ldavis_el1471360154308497445765527936_data);\n",
              "   }(LDAvis);\n",
              "}else if(typeof define === \"function\" && define.amd){\n",
              "   // require.js is available: use it to load d3/LDAvis\n",
              "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
              "   require([\"d3\"], function(d3){\n",
              "      window.d3 = d3;\n",
              "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
              "        new LDAvis(\"#\" + \"ldavis_el1471360154308497445765527936\", ldavis_el1471360154308497445765527936_data);\n",
              "      });\n",
              "    });\n",
              "}else{\n",
              "    // require.js not available: dynamically load d3 & LDAvis\n",
              "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
              "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.4.0/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
              "                 new LDAvis(\"#\" + \"ldavis_el1471360154308497445765527936\", ldavis_el1471360154308497445765527936_data);\n",
              "            })\n",
              "         });\n",
              "}\n",
              "</script>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "import pyLDAvis\n",
        "from pyLDAvis import lda_model\n",
        "\n",
        "pyLDAvis.enable_notebook()\n",
        "dash = lda_model.prepare(lda, bow_tf_vector, vectorizer, mds='tsne')\n",
        "dash"
      ]
    }
  ],
  "metadata": {
    "interpreter": {
      "hash": "880b2a8c90f9e6beae80b56829e3f671fedd58b6d14887184ddce26124cedfbd"
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.9"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}