LDA_EN.ipynb

{
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  "metadata": {
    "colab": {
      "name": "LDA_EN.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "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_EN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NyyHgWiHMCeI"
      },
      "source": [
        "# NLTK Corpus Analysis with Gensim's LDA Model "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U4k7IBGTN0yE"
      },
      "source": [
        "## Preparation\n",
        "First of all, you need to import necessary libraries (with pip command).\n",
        "* nltk\n",
        "* gensim\n",
        "* pyLDAvis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_V4GY1vBSq6"
      },
      "source": [
        "!pip install nltk\n",
        "!pip install gensim\n",
        "!pip install pyLDAvis"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kDFneCwVTfg1"
      },
      "source": [
        "After installing the dependencies, you need to download the following datasets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oAE0HUrXQ0Ly",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0cd3267f-0e0f-4de3-9dd4-715e72dc7754"
      },
      "source": [
        "import nltk\n",
        "nltk.download(\"stopwords\")\n",
        "nltk.download(\"wordnet\")\n",
        "nltk.download(\"reuters\")\n",
        "nltk.download(\"punkt\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n",
            "[nltk_data] Downloading package reuters to /root/nltk_data...\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54jPC3VDV1u6"
      },
      "source": [
        "## Datasets\n",
        "Load the corpus from NLTK package."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFm1nHQGT1Xa"
      },
      "source": [
        "from nltk.corpus import reuters as corpus"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DJnGuTjraiHB"
      },
      "source": [
        "Let us check out the content of the corpus."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aFTjsUd5vvlF"
      },
      "source": [
        "# In some cases, you may need to run the code as follows.\n",
        "!unzip /root/nltk_data/corpora/reuters.zip -d /root/nltk_data/corpora"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQ3Um_aDXbmX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa68fd37-7305-4036-80ba-897000be4236"
      },
      "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": 6,
      "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": "markdown",
      "metadata": {
        "id": "iqdcPZVIgQbr"
      },
      "source": [
        "The total number of documents."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iIH5fH-pai_r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3bc9cc64-36eb-4625-9d7c-d80b48c2d8f1"
      },
      "source": [
        "len(corpus.fileids())"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10788"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DtuDx7O7hGn5"
      },
      "source": [
        "You can train the model with first K number of documents or all documents."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DvcLk_jWgeI8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ddd3216-5810-4641-925c-a49a0c8d3cfe"
      },
      "source": [
        "# First K documents\n",
        "# K=1000\n",
        "# docs=[corpus.words(fileid) for fileid in corpus.fileids()[:K]]\n",
        "\n",
        "# All documents\n",
        "docs=[corpus.words(fileid) for fileid in corpus.fileids()]\n",
        "\n",
        "print(docs[:5])\n",
        "print(\"num of docs:\", len(docs))"
      ],
      "execution_count": 8,
      "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": "QpSrSF8Wi28F"
      },
      "source": [
        "## Data preprocessing\n",
        "First, let us define some stopwords. Here we consider English stopwords from the NLTK package and some noises that may affect our LDA analysis result.  \n",
        "(Optional) Try to ignore numbers and words through regular expression."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y_UwWcRAidvN"
      },
      "source": [
        "# English stopwords defined by the NLTK package.\n",
        "en_stop = nltk.corpus.stopwords.words('english')\n",
        "\n",
        "# Ignore noises that might affect our result.\n",
        "en_stop = [\"``\",\"/\",\",.\",\".,\",\";\",\"--\",\":\",\")\",\"(\",'\"','&',\"'\",'),',',\"','-','.,','.,\"','.-',\"?\",\">\",\"<\"]                  \\\n",
        "         +[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"86\",\"1986\",\"1987\",\"000\"]                                                      \\\n",
        "         +[\"said\",\"say\",\"u\",\"v\",\"mln\",\"ct\",\"net\",\"dlrs\",\"tonne\",\"pct\",\"shr\",\"nil\",\"company\",\"lt\",\"share\",\"year\",\"billion\",\"price\"]          \\\n",
        "         +en_stop"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bUbX_gfXnOnk"
      },
      "source": [
        "Next, let us define several preprocessing functions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZo1muSJm-4w"
      },
      "source": [
        "from nltk.corpus import wordnet as wn # import for lemmatize\n",
        "\n",
        "def preprocess_word(word, stopwordset):\n",
        "    \n",
        "    #1.convert words to lowercase (e.g., Python =>python)\n",
        "    word=word.lower()\n",
        "    \n",
        "    #2.remove \",\" and \".\"\n",
        "    if word in [\",\",\".\"]:\n",
        "        return None\n",
        "    \n",
        "    #3.remove stopwords  (e.g., the => (None)) \n",
        "    if word in stopwordset:\n",
        "        return None\n",
        "    \n",
        "    #4.lemmatize  (e.g., cooked=>cook)\n",
        "    lemma = wn.morphy(word)\n",
        "    if lemma is None:\n",
        "        return word\n",
        "\n",
        "    # lemmatized words could be in the stopwords set\n",
        "    elif lemma in stopwordset: \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": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12xcYitq5uJC"
      },
      "source": [
        "Let us check out the preprocessing result."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x_JATbtL5ptG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e069f30c-aae6-40d7-b160-12bfcb90cb44"
      },
      "source": [
        "# before\n",
        "print(docs[0][:25]) \n",
        "\n",
        "# after\n",
        "print(preprocess_documents(docs)[0][:25])"
      ],
      "execution_count": 11,
      "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": "JAUm_-Ic6B_G"
      },
      "source": [
        "Next, we need to reshape our documents with the available format for the gensim LDA model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tycOho5e580L"
      },
      "source": [
        "import gensim\n",
        "from gensim import corpora"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uoGp2okR6MVJ"
      },
      "source": [
        "# build the dictionary\n",
        "dictionary = corpora.Dictionary(preprocess_documents(docs))\n",
        "# construct the \n",
        "corpus_ = [dictionary.doc2bow(doc) for doc in preprocess_documents(docs)]"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fjneHSfW-LgD"
      },
      "source": [
        "Let us check out the contents of the built dictionary and corpus."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIcDqzWY9eqj"
      },
      "source": [
        "# token2id is the attribute which indicates the mapping between words and dictionary ID\n",
        "\n",
        "print(dictionary.token2id)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PfjZmBAY_Rky",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1838a395-f3f6-46ab-b7e3-a7adc8d5af4e"
      },
      "source": [
        "# corpus_ contains words of each document with a list (ID, appear frequency)\n",
        "\n",
        "# note that there is not the appearing order in the documents, but the order of the dictionary\n",
        "print(corpus_[0][:10]) "
      ],
      "execution_count": 15,
      "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": "markdown",
      "metadata": {
        "id": "g1cC6QA2XBEv"
      },
      "source": [
        "Let us compare the original document with our preprocessing result that is available for the LDA model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQ6n6jH5WxFX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20b0db60-081c-42d9-b230-c6e33efd5ab8"
      },
      "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]]))"
      ],
      "execution_count": 16,
      "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": "GOrrQe5nB84o"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8M-MoIdKB0UD"
      },
      "source": [
        "ldamodel = gensim.models.ldamodel.LdaModel(corpus=corpus_,\n",
        "                                           num_topics=20,\n",
        "                                           id2word=dictionary,\n",
        "                                           alpha=0.1,                 # optional LDA hyperparameter alpha\n",
        "                                           eta=0.1,                   # optional LDA hyperparameter beta\n",
        "                                           #minimum_probability=0.0    # optional the lower bound of the topic/word generative probability\n",
        "                                          )"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RRaPtc9PEpVO"
      },
      "source": [
        "Check out the learned parameters."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GksWhDmLEmLp"
      },
      "source": [
        "# the top num_words of words for each topic (topic ID, the word generative probability for the topic).\n",
        "\n",
        "topics = ldamodel.print_topics(num_words=15)\n",
        "for topic in topics:\n",
        "    print(topic)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QV-llIYuEvTf"
      },
      "source": [
        "# for each document, show the probabilities of topics which beyond the minimum_probability [(topic ID, probability)]\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": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2A-Iyb1GuWm"
      },
      "source": [
        "# the categories of documents\n",
        "categories = [corpus.categories(fileid) for fileid in corpus.fileids()]"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PQuKOtaLI2yW"
      },
      "source": [
        "Let us check out the ```nth``` document in the result."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InaDZANTIaPr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "74884ae6-182e-4c45-ac55-01f8cd8d9d38"
      },
      "source": [
        "n=0\n",
        "\n",
        "# nth document's topic distribution\n",
        "print(ldamodel.get_document_topics(corpus_[n]))\n",
        "\n",
        "# nth document's category\n",
        "print(categories[n])\n",
        "\n",
        "# show the original document\n",
        "print(\" \".join(docs[n]))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(4, 0.20491518), (5, 0.0385313), (10, 0.5366431), (11, 0.1513758), (17, 0.055325456)]\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": "g0CJNkqCJUkp"
      },
      "source": [
        "## Visualization\n",
        "We can further analyze our result through visualization."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhvfbEGhJSRI",
        "outputId": "ff2a934d-3b11-421f-e96a-35161bfb895e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "import pyLDAvis.gensim_models\n",
        "pyLDAvis.enable_notebook()"
      ],
      "execution_count": 22,
      "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": "P-5FnEzeJa0P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 898
        },
        "outputId": "fb53d8bf-c7fd-47bb-afa5-8831d2e83a02"
      },
      "source": [
        "# it will spend about 20 minutes to visualize the result if you train the model with all documents\n",
        "# please note that gensim start topics with index 0 to K-1, but pyLDAvis start the index with 1 to K\n",
        "\n",
        "\n",
        "lda_display = pyLDAvis.gensim_models.prepare(ldamodel, corpus_, dictionary, sort_topics=False)\n",
        "pyLDAvis.display(lda_display)"
      ],
      "execution_count": 23,
      "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_el591405058646536481545539724\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el591405058646536481545539724_data = {\"mdsDat\": {\"x\": [0.0785763565017738, 0.004561897800710155, 0.1334746365945185, 0.2850291464723994, -0.167823213669321, 0.049551103259793276, 0.017325251688484214, -0.09683968593061085, -0.07290525364451836, -0.04407302740032473, -0.13037560247751034, -0.17075449814295085, 0.22811997427440964, -0.02801192299955605, -0.013784253032523942, 0.020713415110286768, -0.11449059127111116, -0.14571361823118556, 0.11870289730305064, 0.048716987794186345], \"y\": [-0.008695969899266543, 0.014157756919484992, 0.03422757764177569, 0.1272506462338463, 0.02984219487581131, -0.18918368363909635, -0.0417809077606478, 0.08313389878384539, 0.13240140487569327, 0.03900169054972806, 0.04933663574185903, 0.11025834665957862, 0.07210019974896355, -0.06958546893576058, -0.08827792426703443, -0.19577631200375753, 0.026216901321337863, -0.06779647740003716, -0.0014131651891084581, -0.05541734425721523], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 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