LDA_TA.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LDA_TA.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/kobemawu/www/blob/master/LDA_TA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m7Ryeraj5R5h"
      },
      "source": [
        "# nltkの文書群にgensimのLDAを適用してみる\n",
        "\n",
        "## 準備"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pwEfP4mP5R5i"
      },
      "source": [
        "必要ライブラリ(全部pipで入ります)\n",
        " * nltk\n",
        " * gensim\n",
        " * pyLDAvis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-f9QBDPX5W0D"
      },
      "source": [
        "!pip install nltk\n",
        "!pip install gensim\n",
        "!pip install pyLDAvis"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MoehXvgS5R5j"
      },
      "source": [
        "#nltk使ったことない人は、pip install してから対話環境等で以下のdataset等をダウンロードしてください\n",
        "import nltk\n",
        "nltk.download(\"stopwords\")\n",
        "nltk.download(\"wordnet\")\n",
        "nltk.download(\"reuters\")\n",
        "nltk.download(\"punkt\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LHnu7qCG5R5o"
      },
      "source": [
        "## データロード・前処理"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "632tVqBN5R5p"
      },
      "source": [
        "#dataset読み込み\n",
        "from nltk.corpus import reuters as corpus"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kwxl83Tu5R5r"
      },
      "source": [
        "### 今回はこういう文書(をBOW化したもの)を用います"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iCsIG0y8sJsc"
      },
      "source": [
        "#必要に応じて以下のコードを実行してください．\n",
        "!unzip /root/nltk_data/corpora/reuters.zip -d /root/nltk_data/corpora\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YtMuouMY5R5s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e6d2267-e7b5-4e88-9c65-7cc989ebc6a5"
      },
      "source": [
        "for n,item in enumerate(corpus.words(corpus.fileids()[0])[:300]):\n",
        "    print(item, end=\" \")\n",
        "    if (n%25) ==24:\n",
        "      print(\" \")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ASIAN EXPORTERS FEAR DAMAGE FROM U . S .- JAPAN RIFT Mounting trade friction between the U . S . And Japan has raised fears  \n",
            "among many of Asia ' s exporting nations that the row could inflict far - reaching economic damage , businessmen and officials said . They  \n",
            "told Reuter correspondents in Asian capitals a U . S . Move against Japan might boost protectionist sentiment in the U . S . And  \n",
            "lead to curbs on American imports of their products . But some exporters said that while the conflict would hurt them in the long -  \n",
            "run , in the short - term Tokyo ' s loss might be their gain . The U . S . Has said it will  \n",
            "impose 300 mln dlrs of tariffs on imports of Japanese electronics goods on April 17 , in retaliation for Japan ' s alleged failure to  \n",
            "stick to a pact not to sell semiconductors on world markets at below cost . Unofficial Japanese estimates put the impact of the tariffs at  \n",
            "10 billion dlrs and spokesmen for major electronics firms said they would virtually halt exports of products hit by the new taxes . \" We  \n",
            "wouldn ' t be able to do business ,\" said a spokesman for leading Japanese electronics firm Matsushita Electric Industrial Co Ltd & lt ;  \n",
            "MC . T >. \" If the tariffs remain in place for any length of time beyond a few months it will mean the complete  \n",
            "erosion of exports ( of goods subject to tariffs ) to the U . S .,\" said Tom Murtha , a stock analyst at the  \n",
            "Tokyo office of broker & lt ; James Capel and Co >. In Taiwan , businessmen and officials are also worried . \" We are  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q8Zid8Cvc29B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04673069-3d04-48d7-c38d-8908c3ae753b"
      },
      "source": [
        "#全document数\n",
        "len(corpus.fileids())"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10788"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ncFipN0C5R5v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c76eda14-2572-49c8-c4d1-e14bbb426cf0"
      },
      "source": [
        "\n",
        "#前からk個のdocumentのみで学習する場合\n",
        "#k=1000\n",
        "#docs=[corpus.words(fileid) for fileid in corpus.fileids()[:k]]\n",
        "\n",
        "#全documentで学習する場合\n",
        "docs=[corpus.words(fileid) for fileid in corpus.fileids()]\n",
        "\n",
        "print(docs[:5])\n",
        "print(\"num of docs:\", len(docs))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['ASIAN', 'EXPORTERS', 'FEAR', 'DAMAGE', 'FROM', 'U', ...], ['CHINA', 'DAILY', 'SAYS', 'VERMIN', 'EAT', '7', '-', ...], ['JAPAN', 'TO', 'REVISE', 'LONG', '-', 'TERM', ...], ['THAI', 'TRADE', 'DEFICIT', 'WIDENS', 'IN', 'FIRST', ...], ['INDONESIA', 'SEES', 'CPO', 'PRICE', 'RISING', ...]]\n",
            "num of docs: 10788\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6xwOmH7H5R5x"
      },
      "source": [
        "## 前処理 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xyDt9_1J5R5y"
      },
      "source": [
        "#ストップワードリストの作成\n",
        "\n",
        "#1 nltkのストップワードリスト\n",
        "en_stop = nltk.corpus.stopwords.words('english')\n",
        "\n",
        "# 一度LDAしてみる等して，適宜ノイズになってそうな記号等を見つけて，ストップワードリストに新たに加える\n",
        "#【発展】記号や数字は正規表現で消してみましょう\n",
        "en_stop= [\"``\", \"/\", \";\", \"--\", \":\", \")\", \"(\", '\"', '&', \"'\", '-', \"?\", \">\", \"<\"]                  \\\n",
        "         +[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\"]                                                      \\\n",
        "         +[\"said\",\"say\",\"u\",\"v\",\"mln\",\"ct\",\"net\",\"dlrs\",\"tonne\",\"pct\",\"shr\",\"nil\",\"company\",\"lt\",\"share\",\"year\",\"billion\",\"price\"]          \\\n",
        "         +en_stop"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_bXxDfxm5R50"
      },
      "source": [
        "#前処理関数の作成\n",
        "\n",
        "from nltk.corpus import wordnet as wn #lemmatize関数のためのimport\n",
        "\n",
        "def preprocess_word(word, stopwordset):\n",
        "    \n",
        "    #1.make words lower  example: Python =>python\n",
        "    word=word.lower()\n",
        "    \n",
        "    #2.remove \",\" and \".\"\n",
        "    if word in [\",\",\".\"]:\n",
        "        return None\n",
        "    \n",
        "    #3.remove stopword  example: the => (None) \n",
        "    if word in stopwordset:\n",
        "        return None\n",
        "    \n",
        "    #4.lemmatize  example: cooked=>cook\n",
        "    lemma = wn.morphy(word)\n",
        "    if lemma is None:\n",
        "        return word\n",
        "\n",
        "    elif lemma in stopwordset: #lemmatizeしたものがstopwordである可能性がある\n",
        "        return None\n",
        "    else:\n",
        "        return lemma\n",
        "    \n",
        "\n",
        "def preprocess_document(document):\n",
        "    document=[preprocess_word(w, en_stop) for w in document]\n",
        "    document=[w for w in document if w is not None]\n",
        "    return document\n",
        "\n",
        "def preprocess_documents(documents):\n",
        "    return [preprocess_document(document) for document in documents]"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZeKhuyY5R52",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07f33d04-d865-4025-bfca-540de7fd939f"
      },
      "source": [
        "#before\n",
        "print(docs[0][:25]) \n",
        "\n",
        "#after\n",
        "print(preprocess_documents(docs)[0][:25])"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['ASIAN', 'EXPORTERS', 'FEAR', 'DAMAGE', 'FROM', 'U', '.', 'S', '.-', 'JAPAN', 'RIFT', 'Mounting', 'trade', 'friction', 'between', 'the', 'U', '.', 'S', '.', 'And', 'Japan', 'has', 'raised', 'fears']\n",
            "['asian', 'exporter', 'fear', 'damage', 'japan', 'rift', 'mounting', 'trade', 'friction', 'japan', 'raise', 'fear', 'among', 'many', 'asia', 'exporting', 'nation', 'row', 'could', 'inflict', 'far', 'reaching', 'economic', 'damage', 'businessmen']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rinjeFsB5R55"
      },
      "source": [
        "## LDA準備"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qSPGvpTw5R55"
      },
      "source": [
        "import gensim\n",
        "from gensim import corpora"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7an_Ksq5R58"
      },
      "source": [
        "#documentを，gensim LDAが読み込めるデータ構造にする\n",
        "\n",
        "#辞書の作成\n",
        "dictionary = corpora.Dictionary(preprocess_documents(docs))\n",
        "#コーパスの作成\n",
        "corpus_ = [dictionary.doc2bow(doc) for doc in preprocess_documents(docs)]"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nTtB7gMP5R59"
      },
      "source": [
        "#Dictionary:gensimにおける辞書クラス\n",
        "#token2id属性には単語と辞書IDとの対応が格納される\n",
        "\n",
        "print(dictionary.token2id)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "erKJROI55R6A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32684eff-b3a1-459c-f8e3-2f8aba691c5d"
      },
      "source": [
        "#corpusにはdocumentごとに単語の(ID、出現回数)のリストが得られる\n",
        "\n",
        "print(corpus_[0][:10]) #文章での出現順でなく辞書IDの若い順なことに注意"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(0, 1), (1, 1), (2, 1), (3, 1), (4, 1), (5, 1), (6, 1), (7, 3), (8, 1), (9, 1)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WfygvWyF5R6G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "874f95b2-6378-4feb-e9b9-610161b9516c"
      },
      "source": [
        "#before\n",
        "print([w.lower() for w in corpus.sents(corpus.fileids()[0])[0]])\n",
        "\n",
        "#after\n",
        "print(dictionary.doc2bow([w.lower() for w in corpus.sents(corpus.fileids()[0])[0]]))\n",
        "\n",
        "#これを全文書の全文に適用したのがcorpus_"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['asian', 'exporters', 'fear', 'damage', 'from', 'u', '.', 's', '.-', 'japan', 'rift', 'mounting', 'trade', 'friction', 'between', 'the', 'u', '.', 's', '.', 'and', 'japan', 'has', 'raised', 'fears', 'among', 'many', 'of', 'asia', \"'\", 's', 'exporting', 'nations', 'that', 'the', 'row', 'could', 'inflict', 'far', '-', 'reaching', 'economic', 'damage', ',', 'businessmen', 'and', 'officials', 'said', '.']\n",
            "[(16, 1), (19, 1), (20, 1), (37, 1), (55, 1), (59, 2), (72, 1), (85, 1), (88, 1), (89, 1), (96, 1), (116, 1), (120, 2), (142, 1), (157, 1), (198, 1), (209, 1), (210, 1), (256, 1)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tYjpaw2J-GS0"
      },
      "source": [
        "## LDA学習"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZW_kyeXS5R6I"
      },
      "source": [
        "ldamodel = gensim.models.ldamodel.LdaModel(corpus=corpus_,\n",
        "                                           num_topics=20,\n",
        "                                           id2word=dictionary,\n",
        "                                           alpha=0.1,                             #optional LDAのハイパーパラメータalpha\n",
        "                                           eta=0.1,                                 #optional LDAのハイパーパラメータbeta\n",
        "                                           #minimum_probability=0.0    #optional 学習結果に格納するトピック・単語の生起確率の下限\n",
        "                                          )"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rc2GDhf2-PiQ"
      },
      "source": [
        "## パラメータの確認"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BoEJnDoY5R6K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a0c5474-abb8-4994-9fba-75dd2200e3e3"
      },
      "source": [
        "#(トピックID, 当該トピックにおける単語とそのprobability)  ※　のうち、上位num_words位 \n",
        "\n",
        "topics = ldamodel.print_topics(num_words=15)\n",
        "for topic in topics:\n",
        "    print(topic)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0, '0.021*\"sales\" + 0.018*\"qtr\" + 0.014*\"4th\" + 0.013*\"note\" + 0.010*\"rev\" + 0.009*\"1985\" + 0.009*\"include\" + 0.009*\"loss\" + 0.008*\"january\" + 0.007*\"inc\" + 0.007*\"gain\" + 0.007*\"end\" + 0.007*\"avg\" + 0.007*\"shrs\" + 0.007*\"28\"')\n",
            "(1, '0.014*\"would\" + 0.010*\"ec\" + 0.006*\"commission\" + 0.006*\"gencorp\" + 0.005*\"plan\" + 0.005*\"european\" + 0.005*\"market\" + 0.005*\"tender\" + 0.005*\"export\" + 0.004*\"analyst\" + 0.004*\"one\" + 0.004*\"last\" + 0.004*\"also\" + 0.004*\"tax\" + 0.004*\"per\"')\n",
            "(2, '0.049*\"stg\" + 0.023*\"bank\" + 0.021*\"rev\" + 0.020*\"shrs\" + 0.019*\"qtr\" + 0.019*\"avg\" + 0.015*\"market\" + 0.014*\"jan\" + 0.012*\"1st\" + 0.011*\"k\" + 0.011*\"money\" + 0.010*\"31\" + 0.009*\"16\" + 0.009*\"corp\" + 0.009*\"bill\"')\n",
            "(3, '0.012*\"gulf\" + 0.011*\"corp\" + 0.008*\"common\" + 0.008*\"merger\" + 0.007*\"inc\" + 0.007*\"iran\" + 0.006*\"stock\" + 0.006*\"oil\" + 0.005*\"pacific\" + 0.005*\"missile\" + 0.005*\"ship\" + 0.005*\"pipeline\" + 0.005*\"shareholder\" + 0.005*\"shipping\" + 0.004*\"would\"')\n",
            "(4, '0.020*\"oper\" + 0.018*\"april\" + 0.018*\"record\" + 0.017*\"prior\" + 0.017*\"inc\" + 0.017*\"qtr\" + 0.017*\"div\" + 0.016*\"pay\" + 0.015*\"rev\" + 0.014*\"qtly\" + 0.013*\"dividend\" + 0.013*\"15\" + 0.012*\"corp\" + 0.012*\"note\" + 0.011*\"31\"')\n",
            "(5, '0.015*\"savings\" + 0.011*\"acres\" + 0.009*\"charter\" + 0.007*\"loan\" + 0.006*\"employer\" + 0.006*\"incoming\" + 0.006*\"woolworth\" + 0.006*\"federal\" + 0.005*\"area\" + 0.005*\"linotype\" + 0.004*\"acre\" + 0.004*\"writedown\" + 0.004*\"association\" + 0.004*\"tire\" + 0.004*\"report\"')\n",
            "(6, '0.026*\"offer\" + 0.012*\"inc\" + 0.012*\"tender\" + 0.009*\"corp\" + 0.008*\"would\" + 0.008*\"merger\" + 0.008*\"stock\" + 0.007*\"bid\" + 0.007*\"usair\" + 0.006*\"per\" + 0.006*\"shareholder\" + 0.006*\"group\" + 0.006*\"board\" + 0.005*\"acquisition\" + 0.005*\"make\"')\n",
            "(7, '0.011*\"ltd\" + 0.011*\"gold\" + 0.011*\"mine\" + 0.009*\"saudi\" + 0.008*\"exploration\" + 0.007*\"would\" + 0.006*\"south\" + 0.005*\"rubber\" + 0.005*\"resource\" + 0.005*\"mining\" + 0.004*\"production\" + 0.004*\"project\" + 0.004*\"arabia\" + 0.004*\"last\" + 0.004*\"ore\"')\n",
            "(8, '0.012*\"would\" + 0.008*\"market\" + 0.008*\"sugar\" + 0.006*\"trade\" + 0.006*\"last\" + 0.006*\"import\" + 0.006*\"export\" + 0.006*\"country\" + 0.005*\"oil\" + 0.005*\"producer\" + 0.005*\"official\" + 0.005*\"future\" + 0.005*\"could\" + 0.004*\"state\" + 0.004*\"world\"')\n",
            "(9, '0.036*\"marks\" + 0.009*\"new\" + 0.009*\"ag\" + 0.006*\"spokesman\" + 0.006*\"shell\" + 0.006*\"dutch\" + 0.006*\"turnover\" + 0.006*\"pound\" + 0.005*\"bell\" + 0.005*\"rio\" + 0.004*\"guilder\" + 0.004*\"trust\" + 0.004*\"german\" + 0.004*\"day\" + 0.004*\"two\"')\n",
            "(10, '0.014*\"inc\" + 0.014*\"group\" + 0.013*\"stock\" + 0.010*\"corp\" + 0.009*\"unit\" + 0.009*\"sell\" + 0.008*\"buy\" + 0.008*\"stake\" + 0.007*\"sale\" + 0.007*\"earnings\" + 0.007*\"business\" + 0.006*\"first\" + 0.006*\"acquire\" + 0.006*\"expect\" + 0.006*\"acquisition\"')\n",
            "(11, '0.108*\"loss\" + 0.069*\"profit\" + 0.014*\"rev\" + 0.013*\"tax\" + 0.010*\"dollar\" + 0.008*\"yen\" + 0.007*\"4th\" + 0.007*\"qtr\" + 0.006*\"note\" + 0.006*\"include\" + 0.005*\"one\" + 0.005*\"group\" + 0.005*\"1985\" + 0.004*\"dealer\" + 0.004*\"losses\"')\n",
            "(12, '0.018*\"trade\" + 0.015*\"japan\" + 0.011*\"dollar\" + 0.010*\"would\" + 0.009*\"market\" + 0.007*\"currency\" + 0.007*\"agreement\" + 0.007*\"japanese\" + 0.007*\"bank\" + 0.007*\"yen\" + 0.006*\".\"\" + 0.006*\"exchange\" + 0.006*\"official\" + 0.006*\"foreign\" + 0.006*\"government\"')\n",
            "(13, '0.027*\"quarter\" + 0.012*\"loss\" + 0.010*\"corn\" + 0.010*\"sales\" + 0.009*\"report\" + 0.009*\"fourth\" + 0.009*\"income\" + 0.008*\"expect\" + 0.007*\"result\" + 0.007*\"first\" + 0.007*\"wheat\" + 0.006*\"program\" + 0.006*\"earnings\" + 0.006*\"operations\" + 0.005*\"fiscal\"')\n",
            "(14, '0.021*\"gas\" + 0.020*\"coffee\" + 0.014*\"oil\" + 0.012*\"brazil\" + 0.010*\"energy\" + 0.009*\"export\" + 0.009*\"natural\" + 0.008*\"new\" + 0.006*\"producer\" + 0.005*\"herrington\" + 0.005*\"cent\" + 0.005*\"last\" + 0.004*\"quota\" + 0.004*\"colombia\" + 0.004*\"would\"')\n",
            "(15, '0.025*\"split\" + 0.023*\"stock\" + 0.020*\"dividend\" + 0.013*\"two\" + 0.011*\"record\" + 0.011*\"april\" + 0.011*\"one\" + 0.010*\"declare\" + 0.009*\"board\" + 0.009*\"payable\" + 0.009*\"shareholder\" + 0.007*\"set\" + 0.007*\"corp\" + 0.006*\"inc\" + 0.006*\"increase\"')\n",
            "(16, '0.020*\"oil\" + 0.010*\"production\" + 0.009*\"export\" + 0.009*\"bpd\" + 0.008*\"1985\" + 0.008*\"week\" + 0.008*\"estimate\" + 0.007*\"last\" + 0.007*\"barrels\" + 0.007*\"total\" + 0.006*\"output\" + 0.006*\"report\" + 0.006*\"opec\" + 0.006*\"87\" + 0.006*\"crude\"')\n",
            "(17, '0.021*\"january\" + 0.021*\"february\" + 0.015*\"rise\" + 0.012*\"rose\" + 0.011*\"surplus\" + 0.011*\"increase\" + 0.010*\"trade\" + 0.010*\"export\" + 0.009*\"month\" + 0.009*\"figure\" + 0.008*\"deficit\" + 0.008*\"import\" + 0.008*\"growth\" + 0.008*\"last\" + 0.008*\"fell\"')\n",
            "(18, '0.034*\"bank\" + 0.015*\"market\" + 0.014*\"money\" + 0.013*\"rate\" + 0.013*\"banks\" + 0.012*\"rates\" + 0.011*\"reserves\" + 0.011*\"fed\" + 0.009*\"dealer\" + 0.008*\"week\" + 0.008*\"rise\" + 0.007*\"january\" + 0.007*\"gold\" + 0.007*\"month\" + 0.006*\"february\"')\n",
            "(19, '0.058*\"oil\" + 0.022*\"crude\" + 0.019*\"delegate\" + 0.012*\"barrel\" + 0.012*\"50\" + 0.011*\"raise\" + 0.010*\"loss\" + 0.009*\"petroleum\" + 0.009*\"crown\" + 0.006*\"texas\" + 0.006*\"today\" + 0.006*\"barrels\" + 0.006*\"bbl\" + 0.005*\"post\" + 0.005*\"17\"')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6-HD9v_65R6N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03c166cd-9e2b-45e8-e8e6-f1ee02cec9fd"
      },
      "source": [
        "#[(当該documentにおけるトピックIDとそのprobability　)]　 ※　のうち、minimum_probabilityの値を超えるもの\n",
        "\n",
        "for n,item in enumerate(corpus_[:10]):\n",
        "    print(\"document ID \"+str(n)+\":\" ,end=\"\")\n",
        "    print(ldamodel.get_document_topics(item))"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "document ID 0:[(1, 0.026368447), (6, 0.07723138), (7, 0.021549119), (8, 0.12972574), (10, 0.02202973), (12, 0.6120421), (17, 0.10790494)]\n",
            "document ID 1:[(5, 0.20143344), (6, 0.07478321), (8, 0.162274), (16, 0.53141564)]\n",
            "document ID 2:[(12, 0.19364278), (14, 0.7057467), (16, 0.0545798), (17, 0.03174169)]\n",
            "document ID 3:[(7, 0.046529885), (13, 0.12958449), (16, 0.46027732), (17, 0.34658366)]\n",
            "document ID 4:[(1, 0.02952471), (8, 0.122700825), (9, 0.015160659), (16, 0.8166106)]\n",
            "document ID 5:[(7, 0.017792517), (8, 0.45537633), (14, 0.51178426)]\n",
            "document ID 6:[(0, 0.013757105), (1, 0.05697171), (7, 0.0660678), (8, 0.6776655), (14, 0.018788645), (16, 0.12739612), (17, 0.033866905)]\n",
            "document ID 7:[(5, 0.14274766), (6, 0.29586512), (7, 0.1046729), (13, 0.13140224), (16, 0.27842695)]\n",
            "document ID 8:[(1, 0.109378986), (4, 0.05232196), (5, 0.019391814), (7, 0.7005131), (9, 0.07516294), (11, 0.02322761)]\n",
            "document ID 9:[(1, 0.13863876), (6, 0.018394459), (8, 0.015829975), (10, 0.23320936), (12, 0.5241451), (15, 0.010524957), (18, 0.051127873)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7AJ_6rZWTUnC"
      },
      "source": [
        "#documentのcategory\n",
        "categories=[corpus.categories(fileid) for fileid in corpus.fileids()]"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q7-m6_X-TdgP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70442854-7c3e-46d2-b675-8894dec11845"
      },
      "source": [
        "n=0\n",
        "\n",
        "#n番目のdocumentのトピック分布\n",
        "print(ldamodel.get_document_topics(corpus_[n]))\n",
        "\n",
        "#n番目のdocumentのcategory\n",
        "print(categories[n])\n",
        "\n",
        "#n番目のdocumentの生の文章\n",
        "print(\" \".join(docs[n]))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(1, 0.026358789), (6, 0.07723607), (7, 0.021553531), (8, 0.1297238), (10, 0.022026742), (12, 0.6120462), (17, 0.107906334)]\n",
            "['trade']\n",
            "ASIAN EXPORTERS FEAR DAMAGE FROM U . S .- JAPAN RIFT Mounting trade friction between the U . S . And Japan has raised fears among many of Asia ' s exporting nations that the row could inflict far - reaching economic damage , businessmen and officials said . They told Reuter correspondents in Asian capitals a U . S . Move against Japan might boost protectionist sentiment in the U . S . And lead to curbs on American imports of their products . But some exporters said that while the conflict would hurt them in the long - run , in the short - term Tokyo ' s loss might be their gain . The U . S . Has said it will impose 300 mln dlrs of tariffs on imports of Japanese electronics goods on April 17 , in retaliation for Japan ' s alleged failure to stick to a pact not to sell semiconductors on world markets at below cost . Unofficial Japanese estimates put the impact of the tariffs at 10 billion dlrs and spokesmen for major electronics firms said they would virtually halt exports of products hit by the new taxes . \" We wouldn ' t be able to do business ,\" said a spokesman for leading Japanese electronics firm Matsushita Electric Industrial Co Ltd & lt ; MC . T >. \" If the tariffs remain in place for any length of time beyond a few months it will mean the complete erosion of exports ( of goods subject to tariffs ) to the U . S .,\" said Tom Murtha , a stock analyst at the Tokyo office of broker & lt ; James Capel and Co >. In Taiwan , businessmen and officials are also worried . \" We are aware of the seriousness of the U . S . Threat against Japan because it serves as a warning to us ,\" said a senior Taiwanese trade official who asked not to be named . Taiwan had a trade trade surplus of 15 . 6 billion dlrs last year , 95 pct of it with the U . S . The surplus helped swell Taiwan ' s foreign exchange reserves to 53 billion dlrs , among the world ' s largest . \" We must quickly open our markets , remove trade barriers and cut import tariffs to allow imports of U . S . Products , if we want to defuse problems from possible U . S . Retaliation ,\" said Paul Sheen , chairman of textile exporters & lt ; Taiwan Safe Group >. A senior official of South Korea ' s trade promotion association said the trade dispute between the U . S . And Japan might also lead to pressure on South Korea , whose chief exports are similar to those of Japan . Last year South Korea had a trade surplus of 7 . 1 billion dlrs with the U . S ., Up from 4 . 9 billion dlrs in 1985 . In Malaysia , trade officers and businessmen said tough curbs against Japan might allow hard - hit producers of semiconductors in third countries to expand their sales to the U . S . In Hong Kong , where newspapers have alleged Japan has been selling below - cost semiconductors , some electronics manufacturers share that view . But other businessmen said such a short - term commercial advantage would be outweighed by further U . S . Pressure to block imports . \" That is a very short - term view ,\" said Lawrence Mills , director - general of the Federation of Hong Kong Industry . \" If the whole purpose is to prevent imports , one day it will be extended to other sources . Much more serious for Hong Kong is the disadvantage of action restraining trade ,\" he said . The U . S . Last year was Hong Kong ' s biggest export market , accounting for over 30 pct of domestically produced exports . The Australian government is awaiting the outcome of trade talks between the U . S . And Japan with interest and concern , Industry Minister John Button said in Canberra last Friday . \" This kind of deterioration in trade relations between two countries which are major trading partners of ours is a very serious matter ,\" Button said . He said Australia ' s concerns centred on coal and beef , Australia ' s two largest exports to Japan and also significant U . S . Exports to that country . Meanwhile U . S .- Japanese diplomatic manoeuvres to solve the trade stand - off continue . Japan ' s ruling Liberal Democratic Party yesterday outlined a package of economic measures to boost the Japanese economy . The measures proposed include a large supplementary budget and record public works spending in the first half of the financial year . They also call for stepped - up spending as an emergency measure to stimulate the economy despite Prime Minister Yasuhiro Nakasone ' s avowed fiscal reform program . Deputy U . S . Trade Representative Michael Smith and Makoto Kuroda , Japan ' s deputy minister of International Trade and Industry ( MITI ), are due to meet in Washington this week in an effort to end the dispute .\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8p2Z1Q05R6Q"
      },
      "source": [
        "## 可視化"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWdya0ec5R6R",
        "outputId": "eda6ee7a-7b4b-4c03-96a5-153ca2497def",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#import pyLDAvis.gensim\n",
        "import pyLDAvis.gensim_models\n",
        "pyLDAvis.enable_notebook()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/past/types/oldstr.py:5: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated since Python 3.3,and in 3.9 it will stop working\n",
            "  from collections import Iterable\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4xEypPsL5R6T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 898
        },
        "outputId": "6d0d9d13-4508-478a-e76a-9e9101e87c0d"
      },
      "source": [
        "#全documentを学習に用いた場合結構時間がかかる(20min~)\n",
        "#gensimではK個のトピックに0~K-1のidが割り振られていたのに対し，pyLDAvisでは1~Kのidが割り振られていることに注意\n",
        "\n",
        "lda_display = pyLDAvis.gensim_models.prepare(ldamodel, corpus_, dictionary, sort_topics=False)\n",
        "pyLDAvis.display(lda_display)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyLDAvis/_prepare.py:247: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
            "  by='saliency', ascending=False).head(R).drop('saliency', 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.3.1/pyLDAvis/js/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el66139924297626192901645296\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el66139924297626192901645296_data = {\"mdsDat\": {\"x\": [0.2563314784164687, -0.09966894249977619, 0.2045734249620567, -0.0670840059355476, 0.20277258399979176, 0.059224819836456326, -0.0803390745082541, -0.07735179838771722, -0.18000986947220304, -0.048809273486366066, -0.012555217757252154, 0.09470685682245775, -0.18878607663407462, 0.047580547935262396, -0.08246894722630231, 0.006318387870088593, 0.03630157089081006, -0.07932237645812185, -0.036120296495532424, 0.044706208127755505], \"y\": [0.055848416769868126, -0.025960536137443587, 0.029181186732314915, -0.1526389688008608, -0.07771974979224437, -0.023259041115585712, -0.15659482099679345, -0.017928381466233618, 0.05659875069397861, -0.03649151279240362, -0.11798793374440861, 0.034248173682607526, 0.05498096933444443, 0.020679465502420325, 0.038456058301583454, -0.13994854122230566, 0.12086386406309722, 0.174185189040698, 0.13224624329053888, 0.031241168656728043], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [5.795745604025357, 5.847270075940892, 1.7682946451003556, 2.9851900704756407, 4.7509240448754975, 1.4181239268331975, 6.106225873210129, 3.318560385682126, 11.275097877047715, 1.7064113604930105, 9.579329552816823, 3.4535092060855686, 12.565519303477101, 3.6849539790758814, 2.6975080041429464, 2.0727128197222755, 5.8192639086292415, 6.472485016741096, 6.664560422790572, 2.018313922834579]}, \"tinfo\": {\"Term\": [\"loss\", \"profit\", \"oil\", \"bank\", \"stg\", \"qtr\", \"rev\", \"stock\", \"offer\", \"marks\", \"quarter\", \"january\", \"inc\", \"dividend\", \"money\", \"february\", \"market\", \"oper\", \"corp\", \"shrs\", \"avg\", \"record\", \"april\", \"sales\", \"crude\", \"trade\", \"split\", \"dollar\", \"div\", \"4th\", \"m3\", \"cra\", \"m0\", \"538\", \"presently\", \"mart\", \"922\", \"761\", \"517\", \"newmont\", \"046\", \"979\", \"cwe\", 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\"alaskan\", \"collins\", \"perception\", \"reichhold\", \"bread\", \"licence\", \"edelman\", \"mobil\", \"mechanism\", \"peso\", \"philippine\", \"ec\", \"chrysler\", \"shearson\", \"maximum\", \"commission\", \"brown\", \"motor\", \"european\", \"block\", \"maize\", \"express\", \"would\", \"certificate\", \"court\", \"grant\", \"tender\", \"award\", \"plan\", \"intervention\", \"analyst\", \"payment\", \"trader\", \"community\", \"export\", \"general\", \"source\", \"market\", \"tax\", \"today\", \"one\", \"per\", \"also\", \"last\", \"make\", \"could\", \"industry\", \"propose\", \"new\", \"government\", \"offer\", \"week\", \"alcan\", \"755\", \"764\", \"morse\", \"princeville\", \"sceptre\", \"seaxe\", \"pillsbury\", \"timminco\", \"billet\", \"666\", \"776\", \"hadson\", \"band\", \"409\", \"qtrs\", \"valtek\", \"965\", \"mar\", \"570\", \"extrusion\", \"subscription\", \"329\", \"trustco\", \"ingot\", \"021\", \"216\", \"wei\", \"carney\", \"067\", \"labatt\", \"stg\", \"assistance\", \"852\", \"1st\", \"jan\", \"shrs\", \"avg\", \"dilute\", \"england\", \"shortage\", \"541\", \"rev\", \"qtr\", \"k\", \"bank\", \"revise\", \"money\", \"bill\", \"feb\", \"31\", \"16\", \"market\", \"help\", \"13\", \"given\", \"today\", \"corp\", \"two\", \"one\", \"forecast\", \"estimate\", \"inc\", \"utilicorp\", \"hormuz\", \"occidental\", \"weinberger\", \"escort\", \"card\", \"graphics\", \"minstar\", \"cr\", \"bound\", \"installation\", \"raid\", \"ocean\", \"army\", \"weapon\", \"mich\", \"oilfield\", \"caspar\", \"cards\", \"ross\", \"healthcare\", \"ami\", \"schneider\", \"mousavi\", \"modulaire\", \"ferc\", \"oxy\", \"reinforce\", \"missile\", \"silkworm\", \"tanker\", \"rospatch\", \"iran\", \"bahrain\", \"kuwaiti\", \"iranian\", \"gulf\", \"pesch\", \"rainbow\", \"attack\", \"medical\", \"mile\", \"pipeline\", \"military\", \"shipping\", \"traffic\", \"strait\", \"tehran\", \"ship\", \"borg\", \"pacific\", \"merger\", \"kuwait\", \"common\", \"seaman\", \"corp\", \"inc\", \"computer\", \"stock\", \"shareholder\", \"system\", \"oil\", \"acquire\", \"approve\", \"would\", \"one\", \"state\", \"co\", \"group\", \"new\", \"sell\", \"also\", \"chemlawn\", \"qtly\", \"hoechst\", \"calny\", \"omit\", \"platform\", \"equatorial\", \"186\", \"saunders\", \"div\", \"polyester\", \"820\", \"oxford\", \"val\", \"alumina\", \"contel\", \"mickelberry\", \"echlin\", \"hovnanian\", \"amt\", \"ps\", \"coke\", \"349\", \"mthly\", \"ecolab\", \"719\", \"798\", \"hf\", \"824\", \"503\", \"qtrly\", \"prior\", \"nippon\", \"oper\", \"payout\", \"quarterly\", \"pay\", \"record\", \"dividend\", \"restate\", \"april\", \"exclude\", \"regular\", \"qtr\", \"mths\", \"rev\", \"feb\", \"15\", \"discontinue\", \"nine\", \"31\", \"note\", \"inc\", \"march\", \"set\", \"co\", \"corp\", \"may\", \"gain\", \"include\", \"30\", \"28\", \"20\", \"one\", \"woolworth\", \"incoming\", \"acre\", \"fnv\", \"combustion\", \"accuray\", \"267\", \"writing\", \"czechoslovakia\", \"redundancy\", \"quaker\", \"turkish\", \"colorado\", \"sigma\", \"underwood\", \"narrowly\", \"mississippi\", \"frequent\", \"aegean\", \"836\", \"schoufour\", \"unfavourable\", \"rainy\", \"linotype\", \"encompass\", \"goodyear\", \"virginia\", \"brine\", \"acquisiton\", \"kloeckner\", \"charter\", \"sgl\", \"acres\", \"hospital\", \"writedown\", \"turkey\", \"shelf\", \"employer\", \"savings\", \"tire\", \"crossland\", \"loan\", \"federal\", \"weather\", \"johnson\", \"home\", \"area\", \"b\", \"association\", \"western\", \"c\", \"general\", \"report\", \"days\", \"corp\", \"new\", \"last\", \"bank\", \"usair\", \"piedmont\", \"eep\", \"hk\", \"thermo\", \"dynamics\", \"hongkong\", \"owens\", \"traditionally\", \"expiration\", \"denver\", \"dot\", \"mca\", \"amusement\", \"oak\", \"wmx\", \"pie\", \"oii\", \"delaware\", \"resign\", \"914\", \"rescind\", \"midnight\", \"airway\", \"judgement\", \"permission\", \"suitor\", \"arbitrageur\", \"viacom\", \"banner\", \"nabisco\", \"hotel\", \"conrac\", \"airline\", \"illinois\", \"dart\", \"enhancement\", \"twa\", \"resort\", \"waste\", \"klm\", \"offer\", \"bp\", \"amstutz\", \"suit\", \"tender\", \"bonus\", \"supermarket\", \"transportation\", \"bid\", \"franchise\", \"merger\", \"standard\", \"voting\", \"takeover\", \"buyout\", \"management\", \"shareholder\", \"inc\", \"acquisition\", \"board\", \"director\", \"corp\", \"stock\", \"proposal\", \"propose\", \"per\", \"would\", \"approval\", \"group\", \"cash\", \"make\", \"dlr\", \"also\", \"agreement\", \"new\", \"common\", \"buy\", \"co\", \"april\", \"placer\", \"png\", \"secondary\", \"accident\", \"wickes\", \"pty\", \"gram\", \"pioneer\", \"nazer\", \"mcfarland\", \"exploit\", \"geological\", \"utah\", \"barely\", \"bhp\", \"finnish\", \"smelt\", \"grey\", \"johannesburg\", \"dakota\", \"gramm\", \"throughput\", \"miner\", \"markka\", \"sprinkel\", \"ste\", \"guy\", \"architect\", \"hear\", \"cole\", \"santos\", \"assay\", \"coal\", \"cooper\", 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\"spokesman\", \"spokesman\", \"spokesman\", \"spokesman\", \"spokesman\", \"spokesman\", \"spokesman\", \"spokesman\", \"sport\", \"sport\", \"sport\", \"sport\", \"sprinkel\", \"sprinkel\", \"stability\", \"stability\", \"stability\", \"stability\", \"stability\", \"stability\", \"stability\", \"stability\", \"stability\", \"stability\", \"stability\", \"stability\", \"stability\", \"stabilize\", \"stabilize\", \"stabilize\", \"stabilize\", \"stabilize\", \"stabilize\", \"stabilize\", \"stabilize\", \"stabilize\", \"stabilize\", \"stabilize\", \"stabilize\", \"stabilize\", \"stake\", \"stake\", \"stake\", \"stake\", \"stake\", \"stake\", \"stake\", \"stake\", \"stake\", \"stake\", \"stake\", \"stake\", \"stake\", \"stake\", \"stake\", \"stake\", \"stake\", \"stake\", \"stake\", \"standard\", \"standard\", \"standard\", \"standard\", \"standard\", \"standard\", \"standard\", \"standard\", \"standard\", \"standard\", \"standard\", \"standard\", \"standard\", \"standard\", \"staple\", \"staple\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"state\", \"statistics\", \"statistics\", \"statistics\", \"statistics\", \"statistics\", \"statistics\", \"statistics\", \"statistics\", \"statistics\", \"statistics\", \"statistics\", \"statoil\", \"statoil\", \"statoil\", \"stc\", \"stch\", \"ste\", \"steam\", \"steel\", \"steel\", \"steel\", \"steel\", \"steel\", \"steel\", \"steel\", \"steel\", \"steel\", \"steel\", \"steel\", \"steel\", \"steel\", \"steel\", \"steel\", \"stg\", \"stg\", \"stg\", \"stg\", \"stg\", \"stg\", \"stg\", \"stg\", \"stg\", \"stg\", \"stg\", \"stg\", \"stg\", \"stg\", \"stg\", \"stg\", \"stick\", \"stick\", \"stick\", \"stick\", \"stick\", \"stipulate\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stock\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"stocks\", \"store\", \"store\", \"store\", \"store\", \"store\", \"store\", \"store\", \"store\", \"store\", \"store\", \"store\", \"store\", \"straight\", \"strait\", \"strait\", \"strait\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"strike\", \"subscription\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"subsidiary\", \"sudan\", \"sudan\", \"sufficiently\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"sugar\", \"suit\", \"suit\", \"suit\", \"suit\", \"suit\", \"suit\", \"suit\", \"suit\", \"suit\", \"suit\", \"suitor\", \"sul\", \"sulphur\", \"sulphur\", \"sulphur\", \"sulphur\", \"sulphur\", \"sumita\", \"sumita\", \"sumita\", \"sumita\", \"sumita\", \"sumita\", \"sun\", \"sun\", \"sun\", \"sun\", \"sun\", \"supercomputer\", \"supermarket\", \"supermarket\", \"supermarket\", \"supermarket\", \"supermarket\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"supply\", \"surface\", \"surface\", \"surface\", \"surplus\", \"surplus\", \"surplus\", \"surplus\", \"surplus\", \"surplus\", \"surplus\", \"surplus\", \"surplus\", \"surplus\", \"surplus\", \"surplus\", \"surplus\", \"surplus\", \"surplus\", \"surplus\", \"surplus\", \"surplus\", \"sweetener\", \"sweetener\", \"switch\", \"switch\", \"switch\", \"switch\", \"switch\", \"switch\", \"switch\", \"symbol\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"system\", \"taft\", \"taft\", \"taiwanese\", \"taiwanese\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"takeover\", \"tanker\", \"tanker\", \"tanker\", \"tanker\", \"tanker\", \"tanker\", \"tape\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tariff\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"tax\", \"taylor\", \"teacher\", \"tehran\", \"tehran\", \"tehran\", \"tehran\", \"tehran\", \"telecom\", \"telecom\", \"telecom\", \"telegraph\", \"telegraph\", \"telegraph\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tell\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tender\", \"tertiary\", \"tertiary\", \"texaco\", \"texaco\", \"texaco\", \"texaco\", \"texaco\", \"texaco\", \"texaco\", \"texaco\", \"texaco\", \"texas\", \"texas\", \"texas\", \"texas\", \"texas\", \"texas\", \"texas\", \"texas\", \"texas\", \"texas\", \"texas\", \"texas\", \"texas\", \"texas\", \"texas\", \"texas\", \"texas\", \"texas\", \"thatcher\", \"thatcher\", \"thatcher\", \"thermo\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"third\", \"thomson\", \"thomson\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"three\", \"throughput\", \"timminco\", \"timminco\", \"tinto\", \"tinto\", \"tire\", \"tire\", \"tire\", \"tire\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"today\", \"todd\", \"ton\", \"ton\", \"ton\", \"ton\", \"ton\", \"ton\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"total\", \"toy\", \"tractor\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trade\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"trader\", \"traditionally\", \"traffic\", \"traffic\", \"traffic\", \"traffic\", \"transamerica\", \"transamerica\", \"transamerica\", \"transportation\", \"transportation\", \"transportation\", \"transportation\", \"transportation\", \"transportation\", \"transportation\", \"transportation\", \"trap\", \"traveler\", \"treasurer\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"treasury\", \"trinidad\", \"triton\", \"trump\", \"trump\", \"trump\", \"trump\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trust\", \"trustco\", \"turkey\", \"turkey\", \"turkey\", \"turkey\", \"turkey\", \"turkish\", \"turkish\", \"turnover\", \"turnover\", \"turnover\", \"turnover\", \"turnover\", \"turnover\", \"turnover\", \"turnover\", \"turnover\", \"turnover\", \"turnover\", \"turnover\", \"turnover\", \"tv\", \"tv\", \"twa\", \"twa\", \"twa\", \"twa\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"two\", \"uganda\", \"uganda\", \"ultramar\", \"ultramar\", \"ultramar\", \"ultramar\", \"unclear\", \"und\", \"underwood\", \"unemployment\", \"unemployment\", \"unemployment\", \"unemployment\", \"unemployment\", \"unemployment\", \"unemployment\", \"unemployment\", \"unemployment\", \"unemployment\", \"unfavourable\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"union\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unit\", \"unknown\", \"unknown\", \"unknown\", \"unocal\", \"unocal\", \"unrealistic\", \"unrest\", \"unrest\", \"unstable\", \"unsure\", \"uranium\", \"usair\", \"usda\", \"usda\", \"usda\", \"usda\", \"usda\", \"usda\", \"usda\", \"usda\", \"usda\", \"usda\", \"usda\", \"usda\", \"utah\", \"utilicorp\", \"val\", \"valtek\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"venture\", \"viacom\", \"viacom\", \"viacom\", \"victor\", \"video\", \"video\", \"video\", \"video\", \"video\", \"video\", \"video\", \"vietnam\", \"virginia\", \"virginia\", \"virginia\", \"virginia\", \"volcker\", \"volcker\", \"volcker\", \"volcker\", \"volcker\", \"volcker\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"vote\", \"voting\", \"voting\", \"voting\", \"voting\", \"voting\", \"voting\", \"voting\", \"voting\", \"voting\", \"wagner\", \"wagner\", \"waive\", \"waiver\", \"walker\", \"walker\", \"walker\", \"walker\", \"wallis\", \"wallis\", \"ward\", \"ward\", \"ward\", \"ward\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"warrant\", \"waste\", \"waste\", \"waste\", \"waste\", \"waste\", \"waste\", \"waste\", \"watching\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"water\", \"weapon\", \"weather\", \"weather\", \"weather\", \"weather\", \"weather\", \"weather\", \"weather\", \"weather\", \"weather\", \"weather\", \"weather\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"week\", \"wei\", \"weinberger\", \"weinberger\", \"welsh\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"west\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"western\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"wheat\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"white\", \"wickes\", \"widen\", \"widen\", \"widen\", \"widen\", \"widen\", \"widen\", \"widen\", \"winding\", \"withhold\", \"wmx\", \"woolworth\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"world\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"would\", \"wright\", \"writedown\", \"writedown\", \"writedown\", \"writedown\", \"writedowns\", \"writedowns\", \"writedowns\", \"writeoff\", \"writeoff\", \"writing\", \"wti\", \"yemen\", \"yemen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yen\", \"yeo\", \"yeo\", \"yeutter\", \"yeutter\", \"yeutter\", \"yeutter\", \"yr\", \"yr\", \"zaire\", \"zaire\", \"zaire\", \"zaire\", \"zambia\", \"zambia\", \"zambia\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]};\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_el66139924297626192901645296\", ldavis_el66139924297626192901645296_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@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
              "        new LDAvis(\"#\" + \"ldavis_el66139924297626192901645296\", ldavis_el66139924297626192901645296_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@3.3.1/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
              "                 new LDAvis(\"#\" + \"ldavis_el66139924297626192901645296\", ldavis_el66139924297626192901645296_data);\n",
              "            })\n",
              "         });\n",
              "}\n",
              "</script>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uHgNg6pU5R6W"
      },
      "source": [
        "#上で可視化したモデルをgoogle drive上にsaveできる\n",
        "\n",
        "pyLDAvis.save_html(lda_display,'vis.html')"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bU7HOzkKM-f4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c353b68a-b6de-4161-bf2d-e1daa1075831"
      },
      "source": [
        "!ls"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sample_data  vis.html\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WawrLdG1NAaq"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}