_6_토픽_모델링(Topic_Modeling)_ipynb의_사본.ipynb

{
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  "metadata": {
    "colab": {
      "name": "_6 토픽 모델링(Topic Modeling).ipynb의 사본",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sqJ4qz2uwHHH"
      },
      "source": [
        "# 토픽 모델링(Topic Modeling)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YwtnnVf3DcnI"
      },
      "source": [
        "* 토픽 모델링은 문서 집합에서 주제를 찾아내기 위한 기술\n",
        "* 토픽 모델링은 '특정 주제에 관한 문서에서는 특정 단어가 자주 등장할 것이다'라는 직관을 기반\n",
        "* 예를 들어, 주제가 '개'인 문서에서는 개의 품종, 개의 특성을 나타내는 단어가 다른 문서에 비해 많이 등장\n",
        "* 주로 사용되는 토픽 모델링 방법은 잠재 의미 분석과 잠재 디리클레 할당 기법이 있음"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_DR7Dg5XLn-"
      },
      "source": [
        "## 잠재 의미 분석(Latent Semantic Analysis)\n",
        "\n",
        "* 잠재 의미 분석(LSA)은 주로 문서 색인의 의미 검색에 사용\n",
        "* 잠재 의미 인덱싱(Latent Semantic Indexing, LSI)로도 알려져 있음\n",
        "* LSA의 목표는 문서와 단어의 기반이 되는 잠재적인 토픽을 발견하는 것\n",
        "* 잠재적인 토픽은 문서에 있는 단어들의 분포를 주도한다고 가정\n",
        "\n",
        "* LSA 방법\n",
        "  + 문서 모음에서 생성한 문서-단어 행렬(Document Term Matrix)에서 단어-토픽 행렬(Term-Topic Matrix)과 토픽-중요도 행렬(Topic-Importance Matrix), 그리고 토픽-문서 행렬(Topic-Document Matrix)로 분해"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVQH1rH0bJLI"
      },
      "source": [
        "## 잠재 디리클레 할당(Latent Dirichlet Allocation)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ex3DjGiFKmk1"
      },
      "source": [
        "* 잠재 디레클레 할당(LDA)은 대표적인 토픽 모델링  알고리즘 중 하나\n",
        "\n",
        "* 잠재 디레클레 할당 방법\n",
        "  1. 사용자가 토픽이 개수를 지정해 알고리즘에 전달\n",
        "  2. 모든 단어들을 토픽 중 하나에 할당\n",
        "  3. 모든 문서의 모든 단어에 대해 단어 w가 가정에 의거, $p(t|d)$, $p(w|t)$에 따라 토픽을 재할당, 이를 반복, 이 때 가정은 자신만이 잘못된 토픽에 할당되어 있고 다른 모든 단어는 올바른 토픽에 할당된다는 것을 의미    \n",
        "\n",
        "* $p(t|d)$ - 문서 d의 단어들 중 토픽 t에 해당하는 비율\n",
        "* 해당 문서의 자주 등장하는 다른 단어의 토픽이 해당 단어의 토픽이 될 가능성이 높음을 의미    \n",
        "\n",
        "* $p(w|t)$- 단어 w를 가지고 있는 모든 문서들 중  토픽 t가 할당된 비율\n",
        "* 다른 문서에서 단어 w에 많이 할당된 토픽이 해당 단어의 토픽이 될 가능성이 높음을 의미"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1RxDTpTts3ma"
      },
      "source": [
        "## 데이터 준비"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z1yVomvgs6l3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "outputId": "5299e893-683a-4151-f288-c9eada3f29ed"
      },
      "source": [
        "from sklearn.datasets import fetch_20newsgroups\n",
        "\n",
        "dataset = fetch_20newsgroups(shuffle = True, random_state=1,\n",
        "                             remove = ('headers','footers','quotes'))\n",
        "documents = dataset.data\n",
        "\n",
        "print(len(documents))\n",
        "documents[3]"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11314\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Notwithstanding all the legitimate fuss about this proposal, how much\\nof a change is it?  ATT\\'s last product in this area (a) was priced over\\n$1000, as I suspect \\'clipper\\' phones will be; (b) came to the customer \\nwith the key automatically preregistered with government authorities. Thus,\\naside from attempting to further legitimize and solidify the fed\\'s posture,\\nClipper seems to be \"more of the same\", rather than a new direction.\\n   Yes, technology will eventually drive the cost down and thereby promote\\nmore widespread use- but at present, the man on the street is not going\\nto purchase a $1000 crypto telephone, especially when the guy on the other\\nend probably doesn\\'t have one anyway.  Am I missing something?\\n   The real question is what the gov will do in a year or two when air-\\ntight voice privacy on a phone line is as close as your nearest pc.  That\\nhas got to a problematic scenario for them, even if the extent of usage\\nnever surpasses the \\'underground\\' stature of PGP.'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E56d3KXroenQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7dfa647d-11a1-456d-efa0-db29a583413e"
      },
      "source": [
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from gensim.parsing.preprocessing import preprocess_string\n",
        "\n",
        "nltk.download('stopwords')\n",
        "\n",
        "def clean_text(d):\n",
        "  pattern = r'[^a-zA-Z\\s]'\n",
        "  text = re.sub(pattern, '', d)\n",
        "  return d\n",
        "\n",
        "def clean_stopword(d):\n",
        "  stop_words = stopwords.words('english')\n",
        "  return ' '.join([w.lower() for w in d.split() if w not in stop_words and len(w) > 3])\n",
        "\n",
        "def preprocessing(d):\n",
        "  return preprocess_string(d)"
      ],
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCo9S9RlrfQg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "906b7132-a327-4d1e-94cd-7716d3e15066"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "news_df = pd.DataFrame({'article' : documents})\n",
        "len(news_df)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11314"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9xzZu6Iprj5B",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "138a33de-e6ad-4a21-ff66-1ab6ac5113b5"
      },
      "source": [
        "news_df.replace(\"\",float(\"NaN\"), inplace=True)\n",
        "news_df.dropna(inplace=True)\n",
        "print(len(news_df))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "11096\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oi3PJ7dMsF8K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e59d9c8-0d0a-4283-c2fe-7a5fde62df92"
      },
      "source": [
        "news_df['article'] = news_df['article'].apply(clean_text)\n",
        "news_df['article']"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        Well i'm not sure about the story nad it did s...\n",
              "1        \\n\\n\\n\\n\\n\\n\\nYeah, do you expect people to re...\n",
              "2        Although I realize that principle is not one o...\n",
              "3        Notwithstanding all the legitimate fuss about ...\n",
              "4        Well, I will have to change the scoring on my ...\n",
              "                               ...                        \n",
              "11309    Danny Rubenstein, an Israeli journalist, will ...\n",
              "11310                                                   \\n\n",
              "11311    \\nI agree.  Home runs off Clemens are always m...\n",
              "11312    I used HP DeskJet with Orange Micros Grappler ...\n",
              "11313                                          ^^^^^^\\n...\n",
              "Name: article, Length: 11096, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W6BleIc4Jwcs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "037e5f28-5285-4df9-f70c-19fd2cd92a2d"
      },
      "source": [
        "news_df['article'] = news_df['article'].apply(clean_stopword)\n",
        "news_df['article']"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        well sure story seem biased. what disagree sta...\n",
              "1        yeah, expect people read faq, etc. actually ac...\n",
              "2        although realize principle strongest points, w...\n",
              "3        notwithstanding legitimate fuss proposal, much...\n",
              "4        well, change scoring playoff pool. unfortunate...\n",
              "                               ...                        \n",
              "11309    danny rubenstein, israeli journalist, speaking...\n",
              "11310                                                     \n",
              "11311    agree. home runs clemens always memorable. kin...\n",
              "11312    used deskjet orange micros grappler system6.0....\n",
              "11313    ^^^^^^ argument murphy. scared hell came last ...\n",
              "Name: article, Length: 11096, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenized_news = news_df['article'].apply(preprocessing)\n",
        "tokenized_news = tokenized_news.to_list()"
      ],
      "metadata": {
        "id": "Hx6WPWB-SM-P"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rKywEnyAJxhc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8de22023-79ca-4788-8cdc-89c135fc8985"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "drop_news = [index for index, sentence in enumerate(tokenized_news) if len(sentence) <= 1]\n",
        "news_texts = np.delete(tokenized_news,drop_news, axis=0)\n",
        "print(len(news_texts))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10936\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/numpy/lib/function_base.py:4454: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  arr = asarray(arr)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IJ8Zkrar1umI"
      },
      "source": [
        "## Gensim을 이용한 토픽 모델링"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q7g3xjKA0WI-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81cb8895-9a3d-4d54-8754-24449c30e404"
      },
      "source": [
        "from gensim import corpora\n",
        "\n",
        "dictionary = corpora.Dictionary(news_texts)\n",
        "corpus = [dictionary.doc2bow(text) for text in news_texts]\n",
        "print(corpus[1])"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(51, 1), (52, 1), (53, 1), (54, 1), (55, 1), (56, 1), (57, 1), (58, 2), (59, 1), (60, 1), (61, 1), (62, 2), (63, 1), (64, 1), (65, 1), (66, 1), (67, 1), (68, 2), (69, 3), (70, 1), (71, 1), (72, 1), (73, 1), (74, 1), (75, 2), (76, 1), (77, 1), (78, 1), (79, 1), (80, 1), (81, 1), (82, 2), (83, 1), (84, 1), (85, 1), (86, 1)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zhM28edm261U"
      },
      "source": [
        "### 잠재 의미 분석을 위한 `LsiModel`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pqz7Xmp63LPa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "66bece4e-6bad-4438-cbd7-f0a8cc0d7a23"
      },
      "source": [
        "from gensim.models import LsiModel\n",
        "\n",
        "lsi_model = LsiModel(corpus, num_topics=20, id2word=dictionary)\n",
        "topics = lsi_model.print_topics()\n",
        "topics"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0,\n",
              "  '-0.994*\"max\" + -0.069*\"giz\" + -0.068*\"bhj\" + -0.025*\"qax\" + -0.015*\"biz\" + -0.014*\"nrhj\" + -0.014*\"bxn\" + -0.012*\"nui\" + -0.011*\"ghj\" + -0.011*\"zei\"'),\n",
              " (1,\n",
              "  '0.381*\"file\" + 0.193*\"program\" + 0.169*\"edu\" + 0.162*\"imag\" + 0.130*\"avail\" + 0.126*\"output\" + 0.119*\"includ\" + 0.115*\"inform\" + 0.101*\"pub\" + 0.100*\"time\"'),\n",
              " (2,\n",
              "  '0.408*\"file\" + 0.335*\"output\" + 0.216*\"entri\" + -0.171*\"peopl\" + -0.153*\"know\" + 0.137*\"onam\" + 0.134*\"program\" + -0.131*\"said\" + 0.129*\"printf\" + 0.115*\"char\"'),\n",
              " (3,\n",
              "  '0.249*\"imag\" + 0.226*\"edu\" + -0.214*\"output\" + -0.165*\"peopl\" + -0.157*\"know\" + -0.155*\"entri\" + -0.153*\"said\" + 0.153*\"avail\" + 0.142*\"jpeg\" + 0.124*\"pub\"'),\n",
              " (4,\n",
              "  '-0.549*\"wire\" + -0.223*\"ground\" + 0.214*\"jpeg\" + 0.213*\"file\" + 0.169*\"imag\" + -0.164*\"circuit\" + -0.157*\"outlet\" + -0.139*\"connect\" + -0.129*\"subject\" + -0.126*\"neutral\"'),\n",
              " (5,\n",
              "  '0.400*\"jpeg\" + 0.345*\"imag\" + -0.276*\"anonym\" + 0.246*\"wire\" + -0.160*\"privaci\" + -0.156*\"internet\" + 0.151*\"color\" + -0.144*\"post\" + -0.125*\"inform\" + -0.123*\"mail\"'),\n",
              " (6,\n",
              "  '0.460*\"file\" + 0.215*\"wire\" + -0.169*\"output\" + -0.158*\"program\" + -0.154*\"edu\" + 0.144*\"anonym\" + 0.141*\"firearm\" + -0.137*\"widget\" + 0.123*\"jpeg\" + -0.120*\"entri\"'),\n",
              " (7,\n",
              "  '-0.298*\"anonym\" + 0.286*\"file\" + -0.247*\"imag\" + -0.246*\"jpeg\" + 0.178*\"widget\" + -0.170*\"post\" + -0.166*\"internet\" + -0.155*\"privaci\" + 0.138*\"control\" + 0.127*\"drive\"'),\n",
              " (8,\n",
              "  '0.295*\"hockei\" + 0.279*\"team\" + 0.235*\"game\" + 0.233*\"leagu\" + 0.188*\"season\" + 0.163*\"year\" + -0.136*\"widget\" + 0.129*\"plai\" + 0.128*\"space\" + -0.122*\"anonym\"'),\n",
              " (9,\n",
              "  '0.500*\"drive\" + 0.275*\"disk\" + 0.202*\"control\" + 0.185*\"hard\" + 0.168*\"bio\" + -0.158*\"edu\" + 0.155*\"support\" + 0.138*\"card\" + 0.134*\"featur\" + 0.119*\"scsi\"'),\n",
              " (10,\n",
              "  '-0.480*\"stephanopoulo\" + -0.329*\"presid\" + 0.238*\"drive\" + 0.187*\"armenian\" + 0.183*\"peopl\" + 0.125*\"disk\" + -0.123*\"packag\" + -0.120*\"go\" + -0.120*\"think\" + 0.102*\"control\"'),\n",
              " (11,\n",
              "  '0.286*\"space\" + -0.266*\"stephanopoulo\" + 0.254*\"launch\" + -0.213*\"drive\" + -0.209*\"edu\" + 0.172*\"satellit\" + -0.123*\"presid\" + 0.114*\"widget\" + -0.113*\"disk\" + -0.109*\"com\"'),\n",
              " (12,\n",
              "  '-0.248*\"edu\" + 0.232*\"widget\" + 0.200*\"jpeg\" + 0.173*\"window\" + -0.170*\"graphic\" + 0.161*\"convert\" + -0.148*\"space\" + -0.147*\"pub\" + 0.136*\"applic\" + 0.133*\"hockei\"'),\n",
              " (13,\n",
              "  '0.265*\"jesu\" + 0.196*\"atheist\" + 0.189*\"edu\" + 0.184*\"christian\" + 0.165*\"believ\" + 0.154*\"god\" + 0.145*\"exist\" + -0.139*\"launch\" + -0.133*\"armenian\" + -0.126*\"space\"'),\n",
              " (14,\n",
              "  '0.290*\"space\" + 0.217*\"launch\" + 0.175*\"stephanopoulo\" + -0.166*\"program\" + -0.162*\"russian\" + -0.159*\"govern\" + 0.153*\"satellit\" + -0.147*\"administr\" + 0.128*\"jesu\" + 0.123*\"post\"'),\n",
              " (15,\n",
              "  '0.248*\"imag\" + -0.236*\"com\" + -0.235*\"jpeg\" + -0.233*\"edu\" + 0.224*\"data\" + -0.218*\"launch\" + -0.168*\"space\" + -0.164*\"post\" + 0.162*\"graphic\" + -0.146*\"anonym\"'),\n",
              " (16,\n",
              "  '0.238*\"south\" + 0.219*\"rockefel\" + 0.204*\"secret\" + 0.198*\"island\" + 0.182*\"nuclear\" + 0.166*\"bolshevik\" + -0.157*\"health\" + 0.153*\"militari\" + 0.143*\"ship\" + 0.134*\"georgia\"'),\n",
              " (17,\n",
              "  '-0.255*\"anonym\" + -0.222*\"edu\" + -0.197*\"post\" + 0.180*\"jesu\" + 0.170*\"pub\" + 0.153*\"privaci\" + -0.150*\"imag\" + -0.135*\"health\" + 0.128*\"window\" + 0.125*\"inform\"'),\n",
              " (18,\n",
              "  '0.290*\"stephanopoulo\" + 0.180*\"health\" + -0.179*\"administr\" + -0.161*\"govern\" + -0.155*\"senior\" + -0.147*\"offici\" + -0.147*\"russia\" + -0.143*\"fund\" + -0.143*\"russian\" + -0.122*\"think\"'),\n",
              " (19,\n",
              "  '0.386*\"turkish\" + 0.315*\"jew\" + 0.224*\"armenian\" + 0.202*\"jpeg\" + 0.197*\"turkei\" + 0.154*\"nazi\" + -0.132*\"entri\" + -0.129*\"imag\" + 0.129*\"drive\" + 0.121*\"edu\"')]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWoLB_a164Gq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19236e6b-4b0f-44cb-9a9c-0ae1db9bff73"
      },
      "source": [
        "from gensim.models.coherencemodel import CoherenceModel\n",
        "\n",
        "min_topics, max_topics = 20,25\n",
        "coherence_scores = []\n",
        "\n",
        "for num_topics in range(min_topics,max_topics):\n",
        "  model = LsiModel(corpus, num_topics=num_topics,id2word=dictionary)\n",
        "  coherence = CoherenceModel(model = model,\n",
        "                             texts=news_texts,\n",
        "                             dictionary=dictionary)\n",
        "  coherence_scores.append(coherence.get_coherence())\n",
        "print(coherence_scores)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.48517508480238486, 0.46404079570973733, 0.49710601712103125, 0.46734614209458203, 0.4543798580646386]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JVPXSgtK97Ez",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "outputId": "dea8722a-8cc0-4360-e038-fd124a721b44"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('seaborn-white')\n",
        "\n",
        "x = [int(i) for i in range(min_topics,max_topics)]\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(x, coherence_scores)\n",
        "plt.xlabel('Number of Topics')\n",
        "plt.ylabel('Coherence Scores')\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zbI05R9O3Kc4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0e75bdf7-f6cd-4a80-fa3f-d959c8c329f3"
      },
      "source": [
        "lsi_model = LsiModel(corpus, num_topics=24, id2word=dictionary)\n",
        "topics = lsi_model.print_topics(num_topics=24)\n",
        "topics"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0,\n",
              "  '0.994*\"max\" + 0.069*\"giz\" + 0.068*\"bhj\" + 0.025*\"qax\" + 0.015*\"biz\" + 0.014*\"nrhj\" + 0.014*\"bxn\" + 0.012*\"nui\" + 0.011*\"ghj\" + 0.011*\"zei\"'),\n",
              " (1,\n",
              "  '0.381*\"file\" + 0.193*\"program\" + 0.169*\"edu\" + 0.162*\"imag\" + 0.130*\"avail\" + 0.126*\"output\" + 0.119*\"includ\" + 0.115*\"inform\" + 0.101*\"pub\" + 0.100*\"time\"'),\n",
              " (2,\n",
              "  '-0.408*\"file\" + -0.335*\"output\" + -0.216*\"entri\" + 0.171*\"peopl\" + 0.153*\"know\" + -0.137*\"onam\" + -0.134*\"program\" + 0.131*\"said\" + -0.129*\"printf\" + -0.115*\"char\"'),\n",
              " (3,\n",
              "  '-0.249*\"imag\" + -0.226*\"edu\" + 0.214*\"output\" + 0.165*\"peopl\" + 0.157*\"know\" + 0.155*\"entri\" + 0.153*\"said\" + -0.153*\"avail\" + -0.142*\"jpeg\" + -0.124*\"pub\"'),\n",
              " (4,\n",
              "  '-0.549*\"wire\" + -0.223*\"ground\" + 0.214*\"jpeg\" + 0.213*\"file\" + 0.169*\"imag\" + -0.164*\"circuit\" + -0.157*\"outlet\" + -0.139*\"connect\" + -0.129*\"subject\" + -0.126*\"neutral\"'),\n",
              " (5,\n",
              "  '-0.400*\"jpeg\" + -0.345*\"imag\" + 0.276*\"anonym\" + -0.246*\"wire\" + 0.160*\"privaci\" + 0.156*\"internet\" + -0.151*\"color\" + 0.144*\"post\" + 0.125*\"inform\" + 0.123*\"mail\"'),\n",
              " (6,\n",
              "  '-0.460*\"file\" + -0.215*\"wire\" + 0.169*\"output\" + 0.158*\"program\" + 0.154*\"edu\" + -0.144*\"anonym\" + -0.141*\"firearm\" + 0.137*\"widget\" + -0.123*\"jpeg\" + 0.120*\"entri\"'),\n",
              " (7,\n",
              "  '-0.298*\"anonym\" + 0.286*\"file\" + -0.247*\"imag\" + -0.246*\"jpeg\" + 0.178*\"widget\" + -0.170*\"post\" + -0.166*\"internet\" + -0.155*\"privaci\" + 0.138*\"control\" + 0.127*\"drive\"'),\n",
              " (8,\n",
              "  '0.295*\"hockei\" + 0.279*\"team\" + 0.235*\"game\" + 0.233*\"leagu\" + 0.188*\"season\" + 0.163*\"year\" + -0.136*\"widget\" + 0.129*\"plai\" + 0.128*\"space\" + -0.122*\"anonym\"'),\n",
              " (9,\n",
              "  '-0.500*\"drive\" + -0.275*\"disk\" + -0.202*\"control\" + -0.185*\"hard\" + -0.168*\"bio\" + 0.158*\"edu\" + -0.155*\"support\" + -0.138*\"card\" + -0.134*\"featur\" + -0.119*\"scsi\"'),\n",
              " (10,\n",
              "  '-0.480*\"stephanopoulo\" + -0.329*\"presid\" + 0.238*\"drive\" + 0.187*\"armenian\" + 0.183*\"peopl\" + 0.125*\"disk\" + -0.123*\"packag\" + -0.120*\"go\" + -0.120*\"think\" + 0.102*\"control\"'),\n",
              " (11,\n",
              "  '0.286*\"space\" + -0.266*\"stephanopoulo\" + 0.254*\"launch\" + -0.213*\"drive\" + -0.209*\"edu\" + 0.172*\"satellit\" + -0.123*\"presid\" + 0.114*\"widget\" + -0.113*\"disk\" + -0.109*\"com\"'),\n",
              " (12,\n",
              "  '0.248*\"edu\" + -0.232*\"widget\" + -0.200*\"jpeg\" + -0.173*\"window\" + 0.170*\"graphic\" + -0.161*\"convert\" + 0.148*\"space\" + 0.147*\"pub\" + -0.136*\"applic\" + -0.133*\"hockei\"'),\n",
              " (13,\n",
              "  '-0.265*\"jesu\" + -0.196*\"atheist\" + -0.189*\"edu\" + -0.184*\"christian\" + -0.165*\"believ\" + -0.154*\"god\" + -0.145*\"exist\" + 0.140*\"launch\" + 0.133*\"armenian\" + 0.126*\"space\"'),\n",
              " (14,\n",
              "  '0.290*\"space\" + 0.218*\"launch\" + 0.175*\"stephanopoulo\" + -0.165*\"program\" + -0.162*\"russian\" + -0.159*\"govern\" + 0.153*\"satellit\" + -0.147*\"administr\" + 0.128*\"jesu\" + 0.123*\"post\"'),\n",
              " (15,\n",
              "  '-0.248*\"imag\" + 0.236*\"com\" + 0.235*\"jpeg\" + 0.233*\"edu\" + -0.225*\"data\" + 0.218*\"launch\" + 0.168*\"space\" + 0.164*\"post\" + -0.162*\"graphic\" + 0.146*\"anonym\"'),\n",
              " (16,\n",
              "  '-0.238*\"south\" + -0.219*\"rockefel\" + -0.204*\"secret\" + -0.198*\"island\" + -0.182*\"nuclear\" + -0.166*\"bolshevik\" + 0.157*\"health\" + -0.153*\"militari\" + -0.144*\"ship\" + -0.134*\"georgia\"'),\n",
              " (17,\n",
              "  '0.255*\"anonym\" + 0.222*\"edu\" + 0.197*\"post\" + -0.180*\"jesu\" + -0.170*\"pub\" + -0.153*\"privaci\" + 0.150*\"imag\" + 0.135*\"health\" + -0.127*\"window\" + -0.125*\"inform\"'),\n",
              " (18,\n",
              "  '-0.290*\"stephanopoulo\" + -0.180*\"health\" + 0.179*\"administr\" + 0.161*\"govern\" + 0.155*\"senior\" + 0.147*\"offici\" + 0.147*\"russia\" + 0.143*\"fund\" + 0.143*\"russian\" + 0.123*\"think\"'),\n",
              " (19,\n",
              "  '-0.386*\"turkish\" + -0.315*\"jew\" + -0.225*\"armenian\" + -0.202*\"jpeg\" + -0.197*\"turkei\" + -0.154*\"nazi\" + 0.132*\"entri\" + 0.129*\"imag\" + -0.129*\"drive\" + -0.121*\"edu\"'),\n",
              " (20,\n",
              "  '-0.265*\"turkish\" + -0.230*\"entri\" + 0.228*\"edu\" + -0.218*\"jew\" + -0.159*\"anonym\" + 0.157*\"jpeg\" + -0.155*\"imag\" + -0.140*\"armenian\" + 0.139*\"com\" + -0.136*\"turkei\"'),\n",
              " (21,\n",
              "  '-0.474*\"entri\" + 0.236*\"output\" + -0.169*\"rule\" + 0.143*\"jesu\" + 0.132*\"anonym\" + 0.125*\"imag\" + -0.119*\"follow\" + 0.118*\"avail\" + 0.110*\"scx\" + -0.107*\"com\"'),\n",
              " (22,\n",
              "  '0.816*\"scx\" + 0.277*\"gcx\" + 0.183*\"syx\" + 0.179*\"mcx\" + 0.099*\"gyx\" + 0.098*\"xxi\" + 0.096*\"uc\" + 0.091*\"bhj\" + 0.091*\"chz\" + 0.086*\"cub\"'),\n",
              " (23,\n",
              "  '0.251*\"bit\" + 0.243*\"scsi\" + 0.207*\"edu\" + 0.186*\"chip\" + 0.180*\"encrypt\" + 0.175*\"com\" + 0.147*\"turkish\" + -0.144*\"drive\" + -0.143*\"entri\" + 0.140*\"kei\"')]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RxSqZmtV2QzN"
      },
      "source": [
        "### 잠재 디리클레 할당을 위한 `LdaModel`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2X_cCIRJbe1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1e239a1-594c-44ba-87bf-0f2c013722e1"
      },
      "source": [
        "from gensim.models import LdaModel\n",
        "lda_model = LdaModel(corpus,num_topics=20, id2word=dictionary)\n",
        "topics = lda_model.print_topics()\n",
        "topics"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0,\n",
              "  '0.011*\"govern\" + 0.008*\"peopl\" + 0.008*\"encrypt\" + 0.008*\"right\" + 0.008*\"chip\" + 0.008*\"clipper\" + 0.007*\"secur\" + 0.007*\"state\" + 0.005*\"law\" + 0.005*\"public\"'),\n",
              " (1,\n",
              "  '0.007*\"state\" + 0.006*\"year\" + 0.006*\"rate\" + 0.006*\"time\" + 0.006*\"health\" + 0.005*\"report\" + 0.005*\"like\" + 0.004*\"tobacco\" + 0.004*\"point\" + 0.004*\"peopl\"'),\n",
              " (2,\n",
              "  '0.009*\"card\" + 0.009*\"work\" + 0.008*\"disk\" + 0.007*\"need\" + 0.007*\"drive\" + 0.007*\"softwar\" + 0.006*\"bit\" + 0.006*\"problem\" + 0.006*\"like\" + 0.006*\"thank\"'),\n",
              " (3,\n",
              "  '0.209*\"max\" + 0.023*\"giz\" + 0.019*\"bhj\" + 0.016*\"bxn\" + 0.008*\"qax\" + 0.005*\"nrhj\" + 0.005*\"nui\" + 0.005*\"chz\" + 0.004*\"fij\" + 0.004*\"biz\"'),\n",
              " (4,\n",
              "  '0.011*\"mail\" + 0.010*\"post\" + 0.010*\"edu\" + 0.009*\"kei\" + 0.007*\"inform\" + 0.006*\"messag\" + 0.006*\"ripem\" + 0.006*\"com\" + 0.006*\"public\" + 0.005*\"user\"'),\n",
              " (5,\n",
              "  '0.014*\"peopl\" + 0.010*\"think\" + 0.007*\"like\" + 0.006*\"know\" + 0.005*\"time\" + 0.004*\"want\" + 0.004*\"come\" + 0.004*\"thing\" + 0.004*\"year\" + 0.004*\"govern\"'),\n",
              " (6,\n",
              "  '0.008*\"water\" + 0.006*\"condit\" + 0.005*\"electr\" + 0.004*\"decenso\" + 0.004*\"plant\" + 0.004*\"nuclear\" + 0.003*\"heat\" + 0.003*\"sale\" + 0.003*\"cover\" + 0.003*\"temperatur\"'),\n",
              " (7,\n",
              "  '0.016*\"drive\" + 0.010*\"know\" + 0.010*\"price\" + 0.009*\"problem\" + 0.009*\"like\" + 0.007*\"good\" + 0.006*\"work\" + 0.006*\"time\" + 0.005*\"think\" + 0.005*\"car\"'),\n",
              " (8,\n",
              "  '0.013*\"wire\" + 0.009*\"ground\" + 0.006*\"pitt\" + 0.006*\"edu\" + 0.005*\"gordon\" + 0.005*\"bank\" + 0.005*\"surrend\" + 0.005*\"skeptic\" + 0.004*\"outlet\" + 0.004*\"soon\"'),\n",
              " (9,\n",
              "  '0.010*\"israel\" + 0.008*\"know\" + 0.008*\"like\" + 0.007*\"isra\" + 0.007*\"exist\" + 0.007*\"think\" + 0.006*\"question\" + 0.005*\"problem\" + 0.005*\"peopl\" + 0.005*\"thing\"'),\n",
              " (10,\n",
              "  '0.040*\"window\" + 0.010*\"file\" + 0.009*\"program\" + 0.006*\"like\" + 0.006*\"space\" + 0.005*\"manag\" + 0.005*\"col\" + 0.005*\"event\" + 0.005*\"version\" + 0.005*\"work\"'),\n",
              " (11,\n",
              "  '0.008*\"problem\" + 0.005*\"food\" + 0.005*\"time\" + 0.005*\"book\" + 0.005*\"line\" + 0.004*\"power\" + 0.004*\"look\" + 0.004*\"know\" + 0.004*\"work\" + 0.003*\"color\"'),\n",
              " (12,\n",
              "  '0.007*\"kill\" + 0.005*\"peopl\" + 0.004*\"year\" + 0.004*\"time\" + 0.004*\"think\" + 0.003*\"gun\" + 0.003*\"good\" + 0.003*\"king\" + 0.003*\"like\" + 0.003*\"cluster\"'),\n",
              " (13,\n",
              "  '0.016*\"armenian\" + 0.010*\"said\" + 0.008*\"peopl\" + 0.007*\"islam\" + 0.007*\"muslim\" + 0.005*\"time\" + 0.005*\"know\" + 0.005*\"russian\" + 0.004*\"go\" + 0.004*\"sai\"'),\n",
              " (14,\n",
              "  '0.013*\"edu\" + 0.012*\"com\" + 0.004*\"univers\" + 0.003*\"dragon\" + 0.003*\"quantiti\" + 0.003*\"center\" + 0.003*\"washington\" + 0.002*\"order\" + 0.002*\"number\" + 0.002*\"texa\"'),\n",
              " (15,\n",
              "  '0.009*\"like\" + 0.007*\"time\" + 0.006*\"bike\" + 0.005*\"know\" + 0.005*\"peopl\" + 0.005*\"right\" + 0.004*\"want\" + 0.004*\"ride\" + 0.004*\"diseas\" + 0.004*\"need\"'),\n",
              " (16,\n",
              "  '0.026*\"game\" + 0.018*\"team\" + 0.015*\"plai\" + 0.013*\"year\" + 0.012*\"player\" + 0.008*\"season\" + 0.007*\"score\" + 0.007*\"leagu\" + 0.007*\"good\" + 0.006*\"hockei\"'),\n",
              " (17,\n",
              "  '0.014*\"space\" + 0.009*\"year\" + 0.007*\"program\" + 0.006*\"file\" + 0.006*\"contest\" + 0.005*\"post\" + 0.005*\"time\" + 0.004*\"like\" + 0.004*\"filenam\" + 0.004*\"work\"'),\n",
              " (18,\n",
              "  '0.018*\"christian\" + 0.011*\"jesu\" + 0.009*\"church\" + 0.008*\"bibl\" + 0.008*\"believ\" + 0.008*\"god\" + 0.007*\"word\" + 0.006*\"religion\" + 0.005*\"belief\" + 0.005*\"christ\"'),\n",
              " (19,\n",
              "  '0.023*\"file\" + 0.015*\"program\" + 0.014*\"entri\" + 0.008*\"output\" + 0.008*\"edu\" + 0.008*\"avail\" + 0.007*\"includ\" + 0.007*\"line\" + 0.006*\"imag\" + 0.006*\"sourc\"')]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2Yn8BwIJqwt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad6fc37b-a4d9-4659-bc6b-3d4651aeb1f0"
      },
      "source": [
        "from gensim.models.coherencemodel import CoherenceModel\n",
        "\n",
        "min_topics, max_topics = 20,25\n",
        "coherence_scores = []\n",
        "\n",
        "for num_topics in range(min_topics,max_topics):\n",
        "  model = LsiModel(corpus, num_topics=num_topics,id2word=dictionary)\n",
        "  coherence = CoherenceModel(model = model,\n",
        "                             texts=news_texts,\n",
        "                             dictionary=dictionary)\n",
        "  coherence_scores.append(coherence.get_coherence())\n",
        "print(coherence_scores)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.46327266306460907, 0.48015402938867635, 0.4620045023134507, 0.4710512948566233, 0.47481278827897255]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pD8aNQIK2q-p",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "outputId": "0eaad23e-261c-412a-c2a5-184eecbbc5ee"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('seaborn-white')\n",
        "\n",
        "x = [int(i) for i in range(min_topics,max_topics)]\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(x, coherence_scores)\n",
        "plt.xlabel('Number of Topics')\n",
        "plt.ylabel('Coherence Scores')\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eziNq5h54tzF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e41f59b-14b2-4ff9-b5d4-51dd93425185"
      },
      "source": [
        "lda_model = LdaModel(corpus,num_topics=23,id2word=dictionary)\n",
        "topics = lda_model.print_topics(num_topics)\n",
        "topics"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0,\n",
              "  '0.174*\"max\" + 0.019*\"giz\" + 0.015*\"bhj\" + 0.013*\"bxn\" + 0.006*\"qax\" + 0.005*\"lib\" + 0.004*\"nrhj\" + 0.004*\"color\" + 0.004*\"nui\" + 0.004*\"biz\"'),\n",
              " (1,\n",
              "  '0.016*\"greek\" + 0.009*\"univers\" + 0.008*\"turkish\" + 0.007*\"turk\" + 0.005*\"nazi\" + 0.005*\"german\" + 0.005*\"histori\" + 0.005*\"state\" + 0.005*\"greec\" + 0.004*\"year\"'),\n",
              " (2,\n",
              "  '0.014*\"israel\" + 0.013*\"state\" + 0.013*\"jew\" + 0.010*\"isra\" + 0.008*\"arab\" + 0.006*\"islam\" + 0.005*\"jewish\" + 0.005*\"peopl\" + 0.005*\"countri\" + 0.005*\"palestinian\"'),\n",
              " (3,\n",
              "  '0.005*\"plastic\" + 0.004*\"space\" + 0.004*\"launch\" + 0.003*\"motorcycl\" + 0.003*\"beam\" + 0.002*\"nasa\" + 0.002*\"kent\" + 0.002*\"appletalk\" + 0.002*\"satellit\" + 0.002*\"tonga\"'),\n",
              " (4,\n",
              "  '0.029*\"drive\" + 0.010*\"col\" + 0.010*\"disk\" + 0.008*\"food\" + 0.006*\"like\" + 0.006*\"hard\" + 0.005*\"look\" + 0.004*\"plane\" + 0.004*\"auto\" + 0.004*\"know\"'),\n",
              " (5,\n",
              "  '0.012*\"peopl\" + 0.010*\"christian\" + 0.008*\"think\" + 0.008*\"believ\" + 0.008*\"know\" + 0.007*\"jesu\" + 0.006*\"mean\" + 0.006*\"god\" + 0.005*\"time\" + 0.005*\"thing\"'),\n",
              " (6,\n",
              "  '0.033*\"armenian\" + 0.009*\"edu\" + 0.008*\"turkish\" + 0.007*\"peopl\" + 0.007*\"said\" + 0.006*\"armenia\" + 0.006*\"turkei\" + 0.006*\"azerbaijan\" + 0.005*\"genocid\" + 0.005*\"com\"'),\n",
              " (7,\n",
              "  '0.012*\"year\" + 0.010*\"like\" + 0.009*\"good\" + 0.008*\"look\" + 0.008*\"player\" + 0.007*\"think\" + 0.007*\"time\" + 0.006*\"know\" + 0.006*\"bike\" + 0.005*\"team\"'),\n",
              " (8,\n",
              "  '0.013*\"ground\" + 0.013*\"koresh\" + 0.012*\"tobacco\" + 0.007*\"outlet\" + 0.006*\"group\" + 0.005*\"wire\" + 0.005*\"peopl\" + 0.005*\"like\" + 0.005*\"black\" + 0.005*\"church\"'),\n",
              " (9,\n",
              "  '0.012*\"widget\" + 0.006*\"valu\" + 0.006*\"plai\" + 0.005*\"team\" + 0.004*\"event\" + 0.004*\"game\" + 0.004*\"season\" + 0.004*\"goal\" + 0.004*\"resourc\" + 0.004*\"edu\"'),\n",
              " (10,\n",
              "  '0.018*\"file\" + 0.014*\"program\" + 0.011*\"window\" + 0.007*\"avail\" + 0.006*\"includ\" + 0.006*\"user\" + 0.006*\"softwar\" + 0.006*\"inform\" + 0.006*\"entri\" + 0.006*\"edu\"'),\n",
              " (11,\n",
              "  '0.012*\"encrypt\" + 0.011*\"govern\" + 0.011*\"chip\" + 0.011*\"kei\" + 0.009*\"secur\" + 0.008*\"clipper\" + 0.008*\"public\" + 0.006*\"peopl\" + 0.006*\"like\" + 0.006*\"gun\"'),\n",
              " (12,\n",
              "  '0.009*\"program\" + 0.008*\"avail\" + 0.007*\"imag\" + 0.007*\"edu\" + 0.007*\"space\" + 0.006*\"data\" + 0.005*\"request\" + 0.005*\"inform\" + 0.005*\"includ\" + 0.005*\"distribut\"'),\n",
              " (13,\n",
              "  '0.011*\"time\" + 0.009*\"peopl\" + 0.008*\"know\" + 0.008*\"work\" + 0.007*\"think\" + 0.007*\"thing\" + 0.007*\"like\" + 0.005*\"go\" + 0.004*\"year\" + 0.004*\"want\"'),\n",
              " (14,\n",
              "  '0.012*\"window\" + 0.009*\"work\" + 0.008*\"like\" + 0.008*\"problem\" + 0.008*\"need\" + 0.006*\"price\" + 0.006*\"know\" + 0.006*\"keyboard\" + 0.006*\"thank\" + 0.006*\"driver\"'),\n",
              " (15,\n",
              "  '0.013*\"health\" + 0.010*\"patient\" + 0.008*\"medic\" + 0.008*\"edu\" + 0.007*\"doctor\" + 0.006*\"coli\" + 0.005*\"infect\" + 0.005*\"soon\" + 0.005*\"diseas\" + 0.005*\"caus\"'),\n",
              " (16,\n",
              "  '0.008*\"govern\" + 0.006*\"peopl\" + 0.006*\"file\" + 0.006*\"problem\" + 0.006*\"control\" + 0.005*\"right\" + 0.005*\"gun\" + 0.005*\"time\" + 0.005*\"state\" + 0.005*\"crime\"'),\n",
              " (17,\n",
              "  '0.015*\"space\" + 0.007*\"nasa\" + 0.006*\"report\" + 0.006*\"station\" + 0.005*\"fund\" + 0.005*\"billion\" + 0.005*\"center\" + 0.005*\"year\" + 0.004*\"cost\" + 0.004*\"mission\"'),\n",
              " (18,\n",
              "  '0.029*\"game\" + 0.013*\"team\" + 0.011*\"plai\" + 0.008*\"score\" + 0.008*\"year\" + 0.007*\"season\" + 0.007*\"period\" + 0.007*\"buf\" + 0.006*\"king\" + 0.005*\"power\"'),\n",
              " (19,\n",
              "  '0.020*\"entri\" + 0.010*\"rule\" + 0.009*\"obfusc\" + 0.009*\"author\" + 0.008*\"file\" + 0.007*\"section\" + 0.007*\"filenam\" + 0.006*\"remark\" + 0.006*\"book\" + 0.005*\"mormon\"'),\n",
              " (20,\n",
              "  '0.009*\"pain\" + 0.008*\"diseas\" + 0.005*\"year\" + 0.004*\"know\" + 0.004*\"time\" + 0.004*\"like\" + 0.004*\"surgeri\" + 0.003*\"newslett\" + 0.003*\"hockei\" + 0.003*\"spacewalk\"'),\n",
              " (21,\n",
              "  '0.020*\"output\" + 0.011*\"card\" + 0.010*\"scsi\" + 0.010*\"file\" + 0.009*\"line\" + 0.009*\"bit\" + 0.008*\"problem\" + 0.008*\"memori\" + 0.007*\"printf\" + 0.007*\"error\"'),\n",
              " (22,\n",
              "  '0.007*\"post\" + 0.005*\"peopl\" + 0.005*\"univers\" + 0.005*\"moral\" + 0.005*\"inform\" + 0.004*\"john\" + 0.004*\"servic\" + 0.004*\"group\" + 0.004*\"school\" + 0.004*\"provid\"')]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vLljp8WR3qHB"
      },
      "source": [
        "## 토픽 모델링 시각화"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z6lhKMEwJqAd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36ae5de3-0128-4e8d-8de8-8039cae4c242"
      },
      "source": [
        "!pip install pyLDAvis"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pyLDAvis\n",
            "  Downloading pyLDAvis-3.3.1.tar.gz (1.7 MB)\n",
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
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            "Requirement already satisfied: numexpr in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (2.8.1)\n",
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            "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (0.16.0)\n",
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            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.2.0->pyLDAvis) (2018.9)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.2.0->pyLDAvis) (1.15.0)\n",
            "Requirement already satisfied: smart-open>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from gensim->pyLDAvis) (5.2.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->pyLDAvis) (2.0.1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from numexpr->pyLDAvis) (21.3)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->numexpr->pyLDAvis) (3.0.7)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->pyLDAvis) (3.1.0)\n",
            "Building wheels for collected packages: pyLDAvis\n",
            "  Building wheel for pyLDAvis (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyLDAvis: filename=pyLDAvis-3.3.1-py2.py3-none-any.whl size=136898 sha256=32421a863664d45fb17dc195517bb62169c51ce4997b838982cc6c8a1c252f9f\n",
            "  Stored in directory: /root/.cache/pip/wheels/c9/21/f6/17bcf2667e8a68532ba2fbf6d5c72fdf4c7f7d9abfa4852d2f\n",
            "Successfully built pyLDAvis\n",
            "Installing collected packages: funcy, pyLDAvis\n",
            "Successfully installed funcy-1.17 pyLDAvis-3.3.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FRYHzP_9JuC3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 916
        },
        "outputId": "5d5119a8-6fd0-4fae-8228-b5dc86d4b2b5"
      },
      "source": [
        "import pyLDAvis.gensim_models\n",
        "\n",
        "pyLDAvis.enable_notebook()\n",
        "vis = pyLDAvis.gensim_models.prepare(lda_model, corpus,dictionary)\n",
        "pyLDAvis.display(vis)"
      ],
      "execution_count": 21,
      "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_el591404323995408162427846003\"></div>\n",
              "<script type=\"text/javascript\">\n",
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              "\n",
              "function LDAvis_load_lib(url, callback){\n",
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              "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
              "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
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              "       new LDAvis(\"#\" + \"ldavis_el591404323995408162427846003\", ldavis_el591404323995408162427846003_data);\n",
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              "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
              "   require([\"d3\"], function(d3){\n",
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              "        new LDAvis(\"#\" + \"ldavis_el591404323995408162427846003\", ldavis_el591404323995408162427846003_data);\n",
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              "    });\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_el591404323995408162427846003\", ldavis_el591404323995408162427846003_data);\n",
              "            })\n",
              "         });\n",
              "}\n",
              "</script>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "eP68pwFaXwLu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}