Ch 07. 토픽모델링.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8dda7647",
   "metadata": {},
   "source": [
    "# Chapter 7. 토픽 모델링으로 주제 찾기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e8fc0c1",
   "metadata": {},
   "source": [
    "## 7.2 사이킷런을 이용한 토픽 모델링\n",
    "\n",
    "### 7.2.1 데이터 준비"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0bdc3f72",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Train set size: 3219\n",
      "#Selected categories: ['alt.atheism', 'comp.graphics', 'comp.sys.ibm.pc.hardware', 'sci.crypt', 'sci.space', 'talk.religion.misc']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "\n",
    "categories = ['alt.atheism', 'talk.religion.misc', 'comp.graphics', 'sci.space', \n",
    "              'comp.sys.ibm.pc.hardware', 'sci.crypt']\n",
    "\n",
    "#학습 데이터셋을 가져옴\n",
    "newsgroups_train = fetch_20newsgroups(subset='train', categories=categories)\n",
    "\n",
    "print('#Train set size:', len(newsgroups_train.data))\n",
    "print('#Selected categories:', newsgroups_train.target_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0f7a323",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "cv = CountVectorizer(token_pattern=\"[\\w']{3,}\", stop_words='english', \n",
    "                     max_features=2000, min_df=5, max_df=0.5)\n",
    "review_cv = cv.fit_transform(newsgroups_train.data) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "117b78a8",
   "metadata": {},
   "source": [
    "### 7.2.2 LDA 토픽 모델링 실행"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da26c423",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#shape of review_topics: (3219, 10)\n",
      "#Sample of review_topics: [0.901 0.007 0.011 0.009 0.007 0.008 0.022 0.008 0.007 0.022]\n",
      "#Sum of topic weights of documents: [0.091 0.082 0.079 0.114 0.114 0.144 0.078 0.072 0.075 0.151]\n",
      "#shape of topic word distribution: (10, 2000)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "import numpy as np\n",
    "np.set_printoptions(precision=3)\n",
    "\n",
    "lda = LatentDirichletAllocation(n_components = 10, #추출할 topic의 수\n",
    "                                max_iter=5, \n",
    "                                topic_word_prior=0.1, doc_topic_prior=1.0,\n",
    "                                learning_method='online', \n",
    "                                n_jobs= -1, #사용 processor 수\n",
    "                                random_state=0)\n",
    "\n",
    "review_topics = lda.fit_transform(review_cv)\n",
    "print('#shape of review_topics:', review_topics.shape)\n",
    "print('#Sample of review_topics:', review_topics[0])\n",
    "\n",
    "gross_topic_weights = np.mean(review_topics, axis=0)\n",
    "print('#Sum of topic weights of documents:', gross_topic_weights)\n",
    "\n",
    "print('#shape of topic word distribution:', lda.components_.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3662b1ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #0: com, article, keith, morality, sgi, think, sandvik, caltech, objective, moral\n",
      "Topic #1: image, graphics, file, files, ftp, software, available, use, data, pub\n",
      "Topic #2: space, access, com, net, digex, lunar, pat, science, posting, new\n",
      "Topic #3: article, just, com, like, don't, university, i'm, henry, nntp, host\n",
      "Topic #4: key, clipper, chip, encryption, com, government, keys, law, use, escrow\n",
      "Topic #5: scsi, com, host, nntp, university, posting, thanks, know, bit, bus\n",
      "Topic #6: nasa, space, gov, center, ___, orbit, washington, jpl, shuttle, earth\n",
      "Topic #7: drive, com, disk, hard, controller, drives, dos, problem, tape, floppy\n",
      "Topic #8: key, public, mail, university, message, faq, posting, pgp, des, group\n",
      "Topic #9: god, people, don't, jesus, just, believe, say, does, think, know\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def print_top_words(model, feature_names, n_top_words):\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        print(\"Topic #%d: \" % topic_idx, end='')\n",
    "        print(\", \".join([feature_names[i] for i in topic.argsort()[:-n_top_words - 1:-1]]))\n",
    "        #print(\", \".join([feature_names[i]+'('+str(topic[i])+')' for i in topic.argsort()[:-n_top_words - 1:-1]]))\n",
    "        # 위 slicing에서 맨 뒤 -1은 역순을 의미, 역순으로 했을 때 처음부터 n_top_words까지\n",
    "    print()\n",
    "\n",
    "print_top_words(lda,cv.get_feature_names_out(), 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b79db881",
   "metadata": {},
   "source": [
    "### 2.3 최적의 토픽 수 선택하기\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "967db96b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_components: 6, perplexity: 1060.370\n",
      "n_components: 7, perplexity: 1061.761\n",
      "n_components: 8, perplexity: 1016.386\n",
      "n_components: 9, perplexity: 1015.849\n",
      "n_components: 10, perplexity: 1030.698\n",
      "n_components: 11, perplexity: 1028.851\n",
      "n_components: 12, perplexity: 1026.104\n",
      "n_components: 13, perplexity: 1037.293\n",
      "n_components: 14, perplexity: 1039.847\n",
      "n_components: 15, perplexity: 1050.649\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_components with minimum perplexity: 9\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "def show_perplexity(cv, start=10, end=30, max_iter=5, topic_word_prior= 0.1, \n",
    "                    doc_topic_prior=1.0):\n",
    "    iter_num = []\n",
    "    per_value = []\n",
    "\n",
    "    for i in range(start, end + 1):\n",
    "        lda = LatentDirichletAllocation(n_components = i, max_iter=max_iter, \n",
    "                                        topic_word_prior= topic_word_prior, \n",
    "                                        doc_topic_prior=doc_topic_prior,\n",
    "                                        learning_method='batch', n_jobs= -1,\n",
    "                                        random_state=7)    \n",
    "        lda.fit(cv)\n",
    "        iter_num.append(i)\n",
    "        pv = lda.perplexity(cv)\n",
    "        per_value.append(pv)\n",
    "        print(f'n_components: {i}, perplexity: {pv:0.3f}')\n",
    "\n",
    "    plt.plot(iter_num, per_value, 'g-')\n",
    "    plt.show()\n",
    "    return start + per_value.index(min(per_value))\n",
    "\n",
    "print(\"n_components with minimum perplexity:\",\n",
    "      show_perplexity(review_cv, start=6, end=15))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e30ace14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Topic #0: image, graphics, mail, available, file, ftp, data, files, software, information\n",
      "Topic #1: nasa, gov, posting, university, host, nntp, space, ___, center, distribution\n",
      "Topic #2: com, keith, article, morality, posting, nntp, host, think, caltech, sgi\n",
      "Topic #3: com, article, jesus, know, just, don't, posting, god, host, i'm\n",
      "Topic #4: people, god, does, don't, think, say, believe, just, way, like\n",
      "Topic #5: drive, scsi, card, com, disk, thanks, ide, controller, bus, hard\n",
      "Topic #6: space, access, article, launch, year, just, like, digex, com, moon\n",
      "Topic #7: key, encryption, clipper, chip, com, government, keys, use, security, public\n",
      "\n"
     ]
    }
   ],
   "source": [
    "lda = LatentDirichletAllocation(n_components = 8, #추출할 topic의 수를 지정\n",
    "                                max_iter=20, \n",
    "                                topic_word_prior= 0.1, \n",
    "                                doc_topic_prior=1.0,\n",
    "                                learning_method='batch',\n",
    "                                n_jobs= -1, \n",
    "                                random_state=7)\n",
    "\n",
    "review_topics = lda.fit_transform(review_cv)\n",
    "\n",
    "print_top_words(lda, cv.get_feature_names_out(), 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c521513b",
   "metadata": {},
   "source": [
    "## 7.3 Gensim을 이용한 토픽 모델링\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47e1acaf",
   "metadata": {},
   "source": [
    "### 7.3.1 Gensim 사용법과 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e387f2f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install --upgrade gensim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5efd2068",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 필요한 library들을 import\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "\n",
    "cachedStopWords = stopwords.words(\"english\")\n",
    "\n",
    "RegTok = RegexpTokenizer(\"[\\w']{3,}\") # 정규포현식으로 토크나이저를 정의\n",
    "english_stops = set(stopwords.words('english')) #영어 불용어를 가져옴\n",
    "\n",
    "def tokenizer(text):\n",
    "    tokens = RegTok.tokenize(text.lower()) \n",
    "    # stopwords 제외\n",
    "    words = [word for word in tokens if (word not in english_stops) and len(word) > 2]\n",
    "    return words\n",
    "\n",
    "texts = [tokenizer(news) for news in newsgroups_train.data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "990c82b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Number of initial unique words in documents: 46466\n",
      "#Number of unique words after removing rare and common words: 2000\n",
      "#Number of unique tokens: 2000\n",
      "#Number of documents: 3219\n"
     ]
    }
   ],
   "source": [
    "from gensim.corpora.dictionary import Dictionary\n",
    "\n",
    "# 토큰화 결과로부터 dictionay 생성\n",
    "dictionary = Dictionary(texts)\n",
    "print('#Number of initial unique words in documents:', len(dictionary))\n",
    "\n",
    "# 문서 빈도수가 너무 적거나 높은 단어를 필터링하고 특성을 단어의 빈도 순으로 선택\n",
    "dictionary.filter_extremes(keep_n=2000, no_below=5, no_above=0.5)\n",
    "print('#Number of unique words after removing rare and common words:', len(dictionary))\n",
    "\n",
    "# 카운트 벡터로 변환\n",
    "corpus = [dictionary.doc2bow(text) for text in texts]\n",
    "print('#Number of unique tokens: %d' % len(dictionary))\n",
    "print('#Number of documents: %d' % len(corpus))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0a0b12a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 10.9 s\n"
     ]
    }
   ],
   "source": [
    "from gensim.models import LdaModel\n",
    "\n",
    "num_topics = 10\n",
    "passes = 5\n",
    "%time model = LdaModel(corpus=corpus, id2word=dictionary,\\\n",
    "                       passes=passes, num_topics=num_topics, \\\n",
    "                       random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c01b0331",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.023*\"com\" + 0.018*\"keith\" + 0.016*\"caltech\" + 0.013*\"sgi\" + 0.013*\"nntp\" + 0.013*\"posting\" + 0.013*\"host\" + 0.012*\"would\" + 0.012*\"system\" + 0.011*\"livesey\"'),\n",
       " (1,\n",
       "  '0.020*\"morality\" + 0.018*\"objective\" + 0.015*\"one\" + 0.015*\"say\" + 0.014*\"uiuc\" + 0.012*\"frank\" + 0.012*\"values\" + 0.010*\"faq\" + 0.010*\"article\" + 0.008*\"cso\"'),\n",
       " (2,\n",
       "  '0.026*\"com\" + 0.025*\"access\" + 0.025*\"posting\" + 0.023*\"host\" + 0.023*\"nntp\" + 0.017*\"digex\" + 0.015*\"article\" + 0.013*\"cwru\" + 0.013*\"___\" + 0.013*\"net\"'),\n",
       " (3,\n",
       "  '0.021*\"university\" + 0.017*\"posting\" + 0.015*\"host\" + 0.015*\"nntp\" + 0.013*\"article\" + 0.010*\"com\" + 0.009*\"know\" + 0.009*\"i\\'m\" + 0.009*\"would\" + 0.009*\"thanks\"'),\n",
       " (4,\n",
       "  '0.032*\"com\" + 0.015*\"would\" + 0.011*\"article\" + 0.010*\"one\" + 0.010*\"get\" + 0.009*\"people\" + 0.009*\"ibm\" + 0.008*\"government\" + 0.007*\"good\" + 0.007*\"i\\'m\"'),\n",
       " (5,\n",
       "  '0.025*\"key\" + 0.017*\"encryption\" + 0.014*\"clipper\" + 0.014*\"chip\" + 0.009*\"keys\" + 0.009*\"use\" + 0.008*\"security\" + 0.008*\"government\" + 0.008*\"public\" + 0.007*\"escrow\"'),\n",
       " (6,\n",
       "  '0.024*\"scsi\" + 0.024*\"drive\" + 0.013*\"com\" + 0.012*\"ide\" + 0.011*\"controller\" + 0.010*\"bus\" + 0.010*\"card\" + 0.010*\"disk\" + 0.009*\"one\" + 0.009*\"drives\"'),\n",
       " (7,\n",
       "  '0.017*\"graphics\" + 0.012*\"image\" + 0.012*\"ftp\" + 0.011*\"file\" + 0.010*\"files\" + 0.009*\"available\" + 0.009*\"data\" + 0.009*\"pub\" + 0.008*\"software\" + 0.008*\"use\"'),\n",
       " (8,\n",
       "  '0.014*\"god\" + 0.013*\"people\" + 0.012*\"one\" + 0.009*\"would\" + 0.007*\"jesus\" + 0.007*\"com\" + 0.007*\"think\" + 0.006*\"many\" + 0.006*\"even\" + 0.006*\"say\"'),\n",
       " (9,\n",
       "  '0.033*\"space\" + 0.019*\"nasa\" + 0.009*\"gov\" + 0.007*\"first\" + 0.007*\"launch\" + 0.006*\"moon\" + 0.006*\"earth\" + 0.006*\"orbit\" + 0.006*\"shuttle\" + 0.006*\"would\"')]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.print_topics(num_words=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3c612bb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#topic distribution of the first document:  [(0, 0.72576934), (8, 0.2699505)]\n"
     ]
    }
   ],
   "source": [
    "print(\"#topic distribution of the first document: \", model.get_document_topics(corpus)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f91053c8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sk8er\\anaconda3\\envs\\textmining\\lib\\site-packages\\pyLDAvis\\_prepare.py:246: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only.\n",
      "  default_term_info = default_term_info.sort_values(\n"
     ]
    },
    {
     "data": {
      "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_el1265218249447240003587681495\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "\n",
       "var ldavis_el1265218249447240003587681495_data = {\"mdsDat\": {\"x\": [-0.1372003241521423, 0.06598951251212938, 0.07213262449923208, 0.13831991361221696, -0.021828434431548603, 0.0727832623398145, 0.027374250322706427, -0.04078630480591686, 0.0053594934988313615, -0.18214399339532314], \"y\": [0.04024886602320191, 0.12361980426193493, 0.09287188530211987, 0.04360855923958373, 0.00749451553911701, -0.09470905073392978, -0.054949133198954656, -0.12997446722166495, -0.09197214681650659, 0.06376116760509863], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [22.655416242418433, 14.522776134324907, 12.98743719933912, 11.02043717187974, 9.29836472992791, 9.118868849514786, 8.494164914699162, 4.524070752392074, 4.051826626501038, 3.3266373790028454]}, \"tinfo\": {\"Term\": [\"space\", \"com\", \"key\", \"scsi\", \"posting\", \"drive\", \"nntp\", \"host\", \"nasa\", \"encryption\", \"access\", \"god\", \"article\", \"university\", 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      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "8     -0.137200  0.040249       1        1  22.655416\n",
       "5      0.065990  0.123620       2        1  14.522776\n",
       "9      0.072133  0.092872       3        1  12.987437\n",
       "7      0.138320  0.043609       4        1  11.020437\n",
       "4     -0.021828  0.007495       5        1   9.298365\n",
       "6      0.072783 -0.094709       6        1   9.118869\n",
       "3      0.027374 -0.054949       7        1   8.494165\n",
       "0     -0.040786 -0.129974       8        1   4.524071\n",
       "2      0.005359 -0.091972       9        1   4.051827\n",
       "1     -0.182144  0.063761      10        1   3.326637, topic_info=            Term         Freq        Total Category  logprob  loglift\n",
       "538        space  1487.000000  1487.000000  Default  30.0000  30.0000\n",
       "20           com  3752.000000  3752.000000  Default  29.0000  29.0000\n",
       "834          key  1316.000000  1316.000000  Default  28.0000  28.0000\n",
       "1551        scsi   741.000000   741.000000  Default  27.0000  27.0000\n",
       "70       posting  1723.000000  1723.000000  Default  26.0000  26.0000\n",
       "...          ...          ...          ...      ...      ...      ...\n",
       "65        people    64.445774  1672.571326  Topic10  -5.1558   0.1469\n",
       "789          sci    53.265337   309.474053  Topic10  -5.3463   1.6436\n",
       "602   university    56.862740  1487.218103  Topic10  -5.2810   0.1392\n",
       "294         time    55.767882  1187.715297  Topic10  -5.3004   0.3446\n",
       "315       better    54.193100   564.543676  Topic10  -5.3290   1.0597\n",
       "\n",
       "[592 rows x 6 columns], token_table=      Topic      Freq     Term\n",
       "term                          \n",
       "1359      1  0.006274      128\n",
       "1359      2  0.069013      128\n",
       "1359      3  0.037643      128\n",
       "1359      4  0.878342      128\n",
       "153       1  0.057463     1993\n",
       "...     ...       ...      ...\n",
       "343       8  0.022394    years\n",
       "343       9  0.005599    years\n",
       "343      10  0.121303    years\n",
       "704       3  0.991487      zoo\n",
       "705       3  0.984036  zoology\n",
       "\n",
       "[2804 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[9, 6, 10, 8, 5, 7, 4, 1, 3, 2])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyLDAvis\n",
    "import pyLDAvis.gensim_models as gensimvis\n",
    "pyLDAvis.enable_notebook()\n",
    "\n",
    "# feed the LDA model into the pyLDAvis instance\n",
    "lda_viz = gensimvis.prepare(model, corpus, dictionary)\n",
    "lda_viz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b25285f",
   "metadata": {},
   "source": [
    "### 7.3.2 혼란도와 토픽 응집도를 이용한 최적값 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7a6d982d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.7493528544065975\n"
     ]
    }
   ],
   "source": [
    "from gensim.models import CoherenceModel\n",
    "\n",
    "cm = CoherenceModel(model=model, corpus=corpus, coherence='u_mass')\n",
    "coherence = cm.get_coherence()\n",
    "print(coherence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "748ab7e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "num_topics: 6, perplexity: -7.035, coherence: -1.701\n",
      "num_topics: 7, perplexity: -7.023, coherence: -1.735\n",
      "num_topics: 8, perplexity: -7.023, coherence: -1.547\n",
      "num_topics: 9, perplexity: -7.007, coherence: -1.891\n",
      "num_topics: 10, perplexity: -6.996, coherence: -1.888\n",
      "num_topics: 11, perplexity: -7.027, coherence: -2.164\n",
      "num_topics: 12, perplexity: -7.019, coherence: -2.018\n",
      "num_topics: 13, perplexity: -7.025, coherence: -2.255\n",
      "num_topics: 14, perplexity: -7.020, coherence: -2.082\n",
      "num_topics: 15, perplexity: -7.019, coherence: -2.521\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def show_coherence(corpus, dictionary, start=6, end=15):\n",
    "    iter_num = []\n",
    "    per_value = []\n",
    "    coh_value = []\n",
    "\n",
    "    for i in range(start, end + 1):\n",
    "        model = LdaModel(corpus=corpus, id2word=dictionary,\n",
    "                 chunksize=1000, num_topics=i,\n",
    "                 random_state=7)\n",
    "        iter_num.append(i)\n",
    "        pv = model.log_perplexity(corpus)\n",
    "        per_value.append(pv)\n",
    "        \n",
    "        cm = CoherenceModel(model=model, corpus=corpus, \n",
    "                            coherence='u_mass')\n",
    "        cv = cm.get_coherence()\n",
    "        coh_value.append(cv)\n",
    "        print(f'num_topics: {i}, perplexity: {pv:0.3f}, coherence: {cv:0.3f}')\n",
    "\n",
    "    plt.plot(iter_num, per_value, 'g-')\n",
    "    plt.xlabel(\"num_topics\")\n",
    "    plt.ylabel(\"perplexity\")\n",
    "    plt.show()\n",
    "\n",
    "    plt.plot(iter_num, coh_value, 'r--')\n",
    "    plt.xlabel(\"num_topics\")\n",
    "    plt.ylabel(\"coherence\")\n",
    "    plt.show()\n",
    "\n",
    "show_coherence(corpus, dictionary, start=6, end=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40a66a0f",
   "metadata": {},
   "source": [
    "## 7.4 토픽트렌드로 시간에 따른 주제의 변화 알아내기"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bb896d4",
   "metadata": {},
   "source": [
    "1: https://github.com/akngs/petitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3c8d675a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: wget in c:\\users\\sk8er\\anaconda3\\envs\\textmining\\lib\\site-packages (3.2)\n",
      "100% [........................................................................] 24480914 / 24480914"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'data/petition_sampled.csv'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!pip install wget\n",
    "\n",
    "import os\n",
    "import wget\n",
    "import ssl\n",
    "\n",
    "data_dir = 'data'\n",
    "url = 'https://s3.ap-northeast-2.amazonaws.com/data10902/petition/petition_sampled.csv'\n",
    "ssl._create_default_https_context = ssl._create_unverified_context\n",
    "\n",
    "if not os.path.exists(data_dir):\n",
    "    os.mkdir(data_dir)\n",
    "\n",
    "wget.download(url, data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f7f16a98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>article_id</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>answered</th>\n",
       "      <th>votes</th>\n",
       "      <th>category</th>\n",
       "      <th>title</th>\n",
       "      <th>content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>136</td>\n",
       "      <td>2017-08-20</td>\n",
       "      <td>2017-11-18</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>육아/교육</td>\n",
       "      <td>고등학교 교육 내용 수준을 낮춰주시고 실용적인 내용을 담아주세요!</td>\n",
       "      <td>저는 광주에 사는 중3 학생입니다. 고등학교 가기 직전의 학년이라 어느 때보다 고등...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   article_id       start         end  answered  votes category  \\\n",
       "2         136  2017-08-20  2017-11-18         0      4    육아/교육   \n",
       "\n",
       "                                  title  \\\n",
       "2  고등학교 교육 내용 수준을 낮춰주시고 실용적인 내용을 담아주세요!   \n",
       "\n",
       "                                             content  \n",
       "2  저는 광주에 사는 중3 학생입니다. 고등학교 가기 직전의 학년이라 어느 때보다 고등...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"./data/petition_sampled.csv\") #read data\n",
    "df.iloc[[2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3701321d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(18077, 1000)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from konlpy.tag import Okt #konlpy에서 Twitter 형태소 분석기를 import\n",
    "twit = Okt()\n",
    "\n",
    "def tokenizer(doc):\n",
    "    return [token for token in twit.nouns(doc) if len(token) > 1]\n",
    "\n",
    "vec = CountVectorizer(tokenizer=tokenizer, #우선은 명사만 사용\n",
    "                      max_df=0.5, min_df = 5, \n",
    "                      max_features = 1000) #적당한 대상 단어 수를 선택\n",
    "\n",
    "pet_cv = vec.fit_transform(df.content)\n",
    "print(pet_cv.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d0e148bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 8.05 s\n",
      "Topic #0: 사람, 생각, 피해자, 사건, 병원, 대통령, 정말, 국민, 가족, 가해자, 환자, 나라, 자신, 처벌, 지금\n",
      "Topic #1: 북한, 우리, 한국, 대통령, 정부, 국민, 대한민국, 미국, 문재인, 일본, 국가, 나라, 생각, 문제, 우리나라\n",
      "Topic #2: 아이, 부모, 생각, 어린이집, 엄마, 교사, 우리, 유치원, 자녀, 가정, 동물, 출산, 가족, 결혼, 저희\n",
      "Topic #3: 청소년, 불법, 폐지, 보호, 장애인, 조직, 사찰, 게임, 생각, 외국인, 이용, 나이, 사회, 범죄, 축구\n",
      "Topic #4: 정부, 주택, 정책, 부동산, 서민, 아파트, 국민, 세금, 나라, 대통령, 지금, 사람, 투기, 집값, 임대\n",
      "Topic #5: 일자리, 기업, 생각, 근무, 정부, 지원, 근로자, 임금, 시간, 최저임금, 정책, 직원, 사람, 고용, 대기업\n",
      "Topic #6: 국민, 청원, 국회의원, 국회, 의원, 세금, 반대, 청와대, 생각, 대통령, 정치, 동의, 제도, 나라, 난민\n",
      "Topic #7: 내용, 대한, 조사, 관리, 판결, 관련, 사실, 공사, 업무, 판사, 확인, 사건, 기관, 해당, 결과\n",
      "Topic #8: 학생, 학교, 교육, 선수, 시험, 생각, 대학, 공부, 선생님, 교사, 문제, 시간, 고등학교, 제도, 위해\n",
      "Topic #9: 보험, 연금, 국민연금, 공무원, 국민, 방송, 지급, 소득, 가입, 국가, 군인, 보험료, 기초, 의무, 제도\n",
      "Topic #10: 여성, 사회, 공무원, 생각, 대한민국, 사람, 남성, 남자, 여자, 행정, 나라, 의무, 군대, 국민, 국가\n",
      "Topic #11: 경찰, 범죄, 국민, 사건, 위해, 대한민국, 조직, 불법, 수사, 만행, 경찰서, 범죄자, 부정부패, 증거, 살인마\n",
      "Topic #12: 처벌, 차량, 사고, 경우, 안전, 피해자, 피해, 범죄, 기사, 사건, 대한, 행위, 택시, 이명박, 운전\n",
      "Topic #13: 민원, 화폐, 가상, 정부, 투자, 시장, 계획, 거래, 규제, 주식, 주민, 흡연, 담배, 사업, 개인\n",
      "Topic #14: 사람, 회사, 저희, 생각, 대출, 사용, 지역, 미세먼지, 전화, 때문, 지금, 신고, 정말, 문제, 은행\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sk8er\\anaconda3\\envs\\textmining\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "\n",
    "lda = LatentDirichletAllocation(n_components = 15, \\\n",
    "                                n_jobs= -1, \\\n",
    "                                random_state=0)\n",
    "\n",
    "%time pet_topics = lda.fit_transform(pet_cv)\n",
    "\n",
    "def print_top_words(model, feature_names, n_top_words):\n",
    "    for topic_idx, topic in enumerate(model.components_):\n",
    "        print(\"Topic #%d: \" % topic_idx, end='')\n",
    "        print(\", \".join([feature_names[i] \n",
    "                         for i in topic.argsort()[:-n_top_words - 1:-1]]))\n",
    "print_top_words(lda,vec.get_feature_names(), 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dae03049",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Topic12</th>\n",
       "      <th>Topic13</th>\n",
       "      <th>Topic14</th>\n",
       "      <th>Topic15</th>\n",
       "      <th>start</th>\n",
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       "      <th>2</th>\n",
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       "      <td>0.000227</td>\n",
       "      <td>0.000227</td>\n",
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       "      <td>0.000227</td>\n",
       "      <td>2017-08</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000447</td>\n",
       "      <td>0.061049</td>\n",
       "      <td>0.000447</td>\n",
       "      <td>0.000447</td>\n",
       "      <td>2017-08</td>\n",
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      ],
      "text/plain": [
       "    Topic12   Topic13   Topic14   Topic15    start\n",
       "0  0.000307  0.000307  0.000307  0.000307  2017-08\n",
       "1  0.000813  0.000813  0.000813  0.495397  2017-08\n",
       "2  0.001449  0.001449  0.001449  0.001449  2017-08\n",
       "3  0.000227  0.000227  0.000227  0.000227  2017-08\n",
       "4  0.000447  0.061049  0.000447  0.000447  2017-08"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trend_data = pd.DataFrame(pet_topics, columns=['Topic'+str(i) for i in range(1, 16)])\n",
    "trend_data = pd.concat([trend_data, df.start.map(lambda x: x[:7])], axis=1)\n",
    "trend_data.iloc[:5, -5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fa9a487f",
   "metadata": {},
   "outputs": [
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       "      <th>Topic11</th>\n",
       "      <th>Topic12</th>\n",
       "      <th>Topic13</th>\n",
       "      <th>Topic14</th>\n",
       "      <th>Topic15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2017-08</th>\n",
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       "      <td>0.017952</td>\n",
       "      <td>0.024384</td>\n",
       "      <td>0.029802</td>\n",
       "      <td>0.101626</td>\n",
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       "    <tr>\n",
       "      <th>2017-09</th>\n",
       "      <td>0.045371</td>\n",
       "      <td>0.023622</td>\n",
       "      <td>0.072277</td>\n",
       "      <td>0.020161</td>\n",
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       "      <th>2017-10</th>\n",
       "      <td>0.088079</td>\n",
       "      <td>0.055008</td>\n",
       "      <td>0.058133</td>\n",
       "      <td>0.032353</td>\n",
       "      <td>0.096818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11</th>\n",
       "      <td>0.046716</td>\n",
       "      <td>0.044052</td>\n",
       "      <td>0.224816</td>\n",
       "      <td>0.040936</td>\n",
       "      <td>0.048950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-12</th>\n",
       "      <td>0.061058</td>\n",
       "      <td>0.028460</td>\n",
       "      <td>0.071439</td>\n",
       "      <td>0.054063</td>\n",
       "      <td>0.079466</td>\n",
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      ],
      "text/plain": [
       "          Topic11   Topic12   Topic13   Topic14   Topic15\n",
       "start                                                    \n",
       "2017-08  0.033960  0.017952  0.024384  0.029802  0.101626\n",
       "2017-09  0.045371  0.023622  0.072277  0.020161  0.040465\n",
       "2017-10  0.088079  0.055008  0.058133  0.032353  0.096818\n",
       "2017-11  0.046716  0.044052  0.224816  0.040936  0.048950\n",
       "2017-12  0.061058  0.028460  0.071439  0.054063  0.079466"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trend = trend_data.groupby(['start']).mean()\n",
    "trend.iloc[:5, -5:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f4919c1f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1152 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "fig, axes = plt.subplots(5, 3, sharex='col', figsize=(12, 16)) \n",
    "for col, ax in zip(trend.columns.tolist(), axes.ravel()): \n",
    "    ax.set_title(col)\n",
    "    ax.axes.xaxis.set_visible(False)\n",
    "    ax.plot(trend[col])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e472ecb4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": true,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "165px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}