Topic_Modeling_Word2Vec.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from konlpy.corpus import kobill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs_ko = [kobill.open(i).read() for i in kobill.fileids()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim import corpora"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import csv\n",
    "targetpath = \"topic_modeling/cbt5_all_chatting/\"\n",
    "texts_ko = []\n",
    "stoppos = ['Noun', 'Adjective', 'Adverb', 'KoreanParticle']\n",
    "user_id = []\n",
    "for (path, dir, files) in os.walk(targetpath):\n",
    "    for filename in files:\n",
    "        #print(filename)\n",
    "        user_id.append(filename[:-4])\n",
    "        with open(targetpath + filename, 'r', encoding = 'utf-8') as readfile:\n",
    "            reader = csv.reader(readfile)\n",
    "            temp_list = []\n",
    "            for r in reader:\n",
    "                if len(str(r[0])) == 1:\n",
    "                    continue\n",
    "                if str(r[1]) not in stoppos:\n",
    "                    continue\n",
    "                temp = str(r[0]) + \"/\" + str(r[1])\n",
    "                temp_list.append(temp)\n",
    "        texts_ko.append(temp_list)\n",
    "                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['외식/Noun', '시로/Noun', '얼마/Noun', '언니/Noun', '버그/Noun', '샛길/Noun', '있었/Adjective', '타고/Noun', '있는/Adjective', 'ㅋㅋ/KoreanParticle', '이렇게/Adverb', '날자/Noun', '언니/Noun', '머행/Noun', '여기/Noun', '어렵/Adjective', '하나/Noun', '어디/Noun', '저번/Noun', '내일/Noun', '언니/Noun', '타지/Noun', '팔지/Noun', '약탈/Noun', '웃후/Noun', '이제/Noun', '골드/Noun', '가야/Noun', '바닥/Noun', '있던/Adjective', '미안/Noun', '세수/Noun', '클릭/Noun', '기사/Noun', '있지/Adjective', '바로/Noun', '대박/Noun', '당신/Noun', '누구/Noun', '룰루랄라/Adverb', '사자/Noun', '궁디/Noun', '무것/Noun', '안남/Noun', '언니/Noun', '속도/Noun', '빠른/Adjective', '느낌/Noun', '이지/Noun', '영숙/Noun', '소진/Noun', '이제/Noun', '어떻/Adjective', '팔자/Noun', '아이템/Noun', '획득/Noun', '녀석/Noun', '여기/Noun', '계속/Noun', '묶인/Noun', '저런/Noun', '매너/Noun', '없는/Adjective', '하얀/Adjective', '글씨/Noun', '일단/Noun', '여기/Noun', '이의/Noun', '여신/Noun', '아까/Adjective', '무기/Noun', '타고/Noun', '밨어/Noun', '그래도/Adverb', '언니/Noun', 'ㅡㅡ/KoreanParticle', '요기/Noun', '많다/Adjective', '짬뽕/Noun', '상자/Noun', '언니/Noun', '있으/Adjective', '시치미/Noun', '약탈/Noun', '리타/Noun', '대화/Noun', '일도/Noun', '나쁜/Adjective', '쉐리/Noun', '붉은/Adjective', '얼룩/Noun', '라임/Noun', 'ㄱㄱ/KoreanParticle', '근데/Adverb', '어떻/Adjective', '씨앗/Noun', '사서/Noun', '바람/Noun', '골드/Noun', '어디서/Adverb', '이상해/Adjective', '기억/Noun', '생각/Noun', '매사/Noun', '고원/Noun', '길이/Noun', '어디/Noun', '저기/Noun', '어떻/Adjective', '엄청/Adverb', '쎄졌어/Noun', '씨앗/Noun', '있었/Adjective', '산삼/Noun', '경매/Noun', '어떻/Adjective', '배구/Noun', '무스/Noun', '대화/Noun', '어떻/Adjective', '주더/Noun', '타고/Noun', '단축키/Noun', '언니/Noun', '궁디/Noun', '리타/Noun', '언니/Noun', '어디가/Noun', '야함/Adjective', '돈내/Noun', '발톱/Noun', 'ㅇㅇ/KoreanParticle', '우히히/Noun', '방아/Noun', '무역/Noun', '야한/Adjective', '채팅/Noun', '헬로/Noun', '원인/Noun', '파악/Noun', '어떻/Adjective', '어서/Noun', '주술사/Noun', '그게/Noun', '관심/Noun', '없어/Adjective', '바보/Noun', '피슝/Noun', '어디서/Adverb', '불꽃/Noun', '정령/Noun', '시험/Noun', 'ㅋㅋ/KoreanParticle', '언니/Noun', '궁디/Noun', 'ㅋㅋ/KoreanParticle', '무서운/Adjective', '갸르릉/Noun', '이제/Noun', '넘사벽/Noun', '이상/Noun', '저번/Noun', '불쌍해/Adjective', '언니/Noun', '사자/Noun', '입수/Noun', '자꾸/Noun', '절루/Noun', 'ㅋㅋ/KoreanParticle', '목화/Noun', '종자/Noun', '빨리/Adverb', '언니/Noun', '가얌/Noun', '답답해서/Adjective', '그렇/Adjective', '편하긴/Adjective', '날틀/Noun', '불편해/Adjective', '언니/Noun', '무서워/Adjective', '바람/Noun', '이런/Adjective', '토끼/Noun', '배고프/Adjective', 'ㅇㅇ/KoreanParticle', '마차/Noun', '오디/Noun', '언니/Noun', '어디가/Noun', '한번/Noun', '과켝/Noun', '어떻/Adjective', '가장/Noun', '오늘/Noun', '아니/Adjective', '우리/Noun', '사이트/Noun', '그럼/Adjective', '이제/Noun', '언니/Noun', '같아/Adjective', '우리/Noun', '매사/Noun', '고원/Noun', '어니/Noun', '인터넷/Noun', '언니/Noun', '있어/Adjective', '에이/Noun', '팔고/Noun', '허름한거/Noun', '있지/Adjective', '일단/Noun', '고고/Noun', '같은/Adjective', '날틀/Noun', '개인/Noun', '거래/Noun', '메뉴/Noun', '아무래도/Adverb', '지금/Noun', '근데/Adverb', '공격/Noun', '비싸네/Adjective', '세수/Noun', '하디/Noun', '대화/Noun', '귀찮/Adjective', 'ㅋㅋ/KoreanParticle', '시치키/Noun', '기차/Noun', '같은/Adjective', '왼쪽/Noun', '어딘가/Noun', '있었/Adjective', '우리/Noun', '어그/Noun', '무지/Noun', '강함/Adjective', '역시/Noun', '보스/Noun', '출근/Noun', '안해/Noun', '그러/Adjective', 'ㅐㅇ/KoreanParticle', '버그/Noun', '고고/Noun', '아기/Noun', '사자/Noun', '거래/Noun', '릴리/Noun', '아니었/Adjective', '얼마나/Noun', '서브/Noun', '아침/Noun', '조조/Noun', '영화/Noun', 'ㅠㅠ/KoreanParticle', '무것/Noun', '뭐하셩/Noun', '언니/Noun', '머행/Noun', '일단/Noun', '고고/Noun', '엘렉/Noun', '타문/Noun', '신기루/Noun', '릴리/Noun', '시치미/Noun', '딸아/Noun', '쟜디/Noun', '산삼/Noun', '사과/Noun', '어디서/Adverb', '채집/Noun', '한지/Noun', '이제/Noun', '길드워/Noun', '마리/Noun', '채짖/Noun', '가까운/Adjective', '먼저/Noun', '헤헤/Noun', '언니/Noun', '지역/Noun', '특산/Noun', '있네/Adjective', '그냥/Noun', '가까/Adjective', '으윽/Adverb', '누구/Noun', '하트/Noun', '대화/Noun', '덴장/Noun', '델피/Noun', '방법/Noun', 'ㅡㅡ/KoreanParticle', '먼저/Noun', '떠블류/Noun', '자꾸/Noun', '애드냄/Noun', '다리/Noun', '건너/Noun', '우물/Noun', '일찍/Noun', '갈꿰/Noun', '이의/Noun', '눈물/Noun', '구래/Noun', '텃밭/Noun', '첼훙/Noun', '만남/Noun', '여기/Noun', '마나/Noun', '아니었/Adjective', '어쩐지/Noun', '멀리/Noun', '아아/Noun', '나나/Noun', 'ㅠㅠ/KoreanParticle', '힐러/Noun', '은화/Noun', '얼마/Noun', '있어/Adjective', '수도/Noun', '이제/Noun', '너멌당/Noun', '내일/Noun', '우리/Noun', '근데/Adverb', '세금/Noun', '빨리/Adverb', '어디/Noun', '동그랗/Adjective', '표시/Noun', '뭐하셩/Noun', '첼흉/Noun', '대화/Noun', '은화/Noun', '비싸길/Adjective', '있겠/Adjective', '우물/Noun', '오빠/Noun', '관계/Noun', '날틀/Noun', '운전/Noun', '어려워/Adjective', '산삼/Noun', '팔고/Noun', '휙휙/Noun', '테스트/Noun', '델피/Noun', '녀석/Noun', '덤비/Noun', '어떻/Adjective', '무것/Noun', '없어/Adjective', '목말/Noun', '리타/Noun', '여기/Noun', '다시/Noun', '아이/Noun', '대루/Noun', '진짜/Noun', '시간/Noun', '기념/Noun', '비슷한/Adjective', '하꾸/Noun', '어디/Noun', '오전/Noun', 'ㅋㅋ/KoreanParticle', '어떻/Adjective', '여기/Noun', '방사/Noun', '오염/Noun', '지역/Noun', '깃털/Noun', '돼지/Noun', '번호/Noun', '순서/Noun', '아니얌/Noun', '미투/Noun', '요거/Noun', '많이/Adverb', '토끼/Noun', '언니/Noun', '랄랄라/Adverb', '방금/Noun', '어랏/Noun', '높다/Adjective', '사람/Noun', '머리/Noun', '하나/Noun', '윤땡/Noun', '오디/Noun', '여기/Noun', '어댜/Noun', '그렁/Adverb', '여신/Noun', 'ㄱㄱ/KoreanParticle', '언니/Noun', '것좀/Noun', '아하/Noun', '어디/Noun', '설정/Noun', '리드/Noun', '이렇게/Adverb', '그런/Adjective', 'ㅠㅠ/KoreanParticle', '쟈갸/Noun', '그럼/Adjective', '언니/Noun', '완전/Noun', '가방/Noun', '있자/Adjective', '하나/Noun', '언니/Noun', '오케/Noun', '완료/Noun', '아직/Noun', '스페이스바/Noun', '날틀/Noun', '여기/Noun', '골드/Noun', '언니/Noun', '때문/Noun', '야함/Adjective', '나봐/Noun', 'ㅏㅈ/KoreanParticle', '그건/Adverb', '헤헤/Noun', '과녘/Noun', '마지막/Noun', '누가/Noun', '여기/Noun', '내용/Noun', '역시/Noun', '운전/Noun', '체질/Noun', '아녀/Adjective', '미국/Noun', '한국/Noun', '양손/Noun', '둔기/Noun', '언니/Noun', '찜닭/Noun', '가야/Noun', '술술/Noun', '눈누난나/Adverb', '유쭈주/Noun', '고고씽/Noun', '가까운/Adjective', '아까/Adjective', '미리/Noun', '그러/Adjective', '가까워/Adjective', '점프/Noun', '분만/Noun', '특산/Noun', '무역/Noun', 'ㅋㅋ/KoreanParticle', '완쥰/Noun', '아니/Adjective', '토끼풀/Noun', '없어/Adjective', '배고파/Adjective', '어떻/Adjective', '아이디/Noun', '굴욕/Noun', '내용/Noun', '이제/Noun', '똑똑하다/Adjective']\n"
     ]
    }
   ],
   "source": [
    "print(texts_ko[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "textset = defaultdict(lambda: 0)\n",
    "for text in texts_ko:\n",
    "    for t in text:\n",
    "        textset[t] +=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2190\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topic Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dictionary(85416 unique tokens: ['ㄱㄱ/KoreanParticle', 'ㅇㅇ/KoreanParticle', 'ㅋㅋ/KoreanParticle', 'ㅏㅈ/KoreanParticle', 'ㅐㅇ/KoreanParticle']...)\n"
     ]
    }
   ],
   "source": [
    "from gensim import corpora\n",
    "dictionary_ko = corpora.Dictionary(texts_ko)\n",
    "print(dictionary_ko)\n",
    "dictionary_ko.save('ko.dict')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 2), (1, 2), (2, 7), (3, 1), (4, 1), (5, 3), (6, 2), (7, 1), (8, 2), (9, 1), (10, 1), (11, 2), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 2), (18, 1), (19, 1), (20, 2), (21, 1), (22, 1), (23, 1), (24, 1), (25, 3), (26, 1), (27, 2), (28, 3), (29, 1), (30, 1), (31, 1), (32, 1), (33, 1), (34, 1), (35, 1), (36, 3), (37, 1), (38, 1), (39, 1), (40, 1), (41, 1), (42, 2), (43, 1), (44, 2), (45, 1), (46, 1), (47, 3), (48, 1), (49, 1), (50, 1), (51, 1), (52, 1), (53, 1), (54, 1), (55, 1), (56, 1), (57, 1), (58, 1), (59, 1), (60, 4), (61, 2), (62, 2), (63, 1), (64, 1), (65, 2), (66, 1), (67, 1), (68, 2), (69, 1), (70, 1), (71, 1), (72, 1), (73, 1), (74, 1), (75, 1), (76, 1), (77, 1), (78, 1), (79, 5), (80, 1), (81, 1), (82, 2), (83, 1), (84, 1), (85, 1), (86, 1), (87, 1), (88, 1), (89, 1), (90, 1), (91, 1), (92, 1), (93, 1), (94, 1), (95, 3), (96, 2), (97, 1), (98, 1), (99, 1), (100, 1), (101, 1), (102, 1), (103, 1), (104, 1), (105, 2), (106, 1), (107, 2), (108, 2), (109, 1), (110, 1), (111, 1), (112, 1), (113, 3), (114, 1), (115, 1), (116, 1), (117, 1), (118, 2), (119, 1), (120, 1), (121, 2), (122, 1), (123, 1), (124, 1), (125, 1), (126, 1), (127, 2), (128, 1), (129, 1), (130, 1), (131, 1), (132, 1), (133, 1), (134, 1), (135, 1), (136, 1), (137, 1), (138, 1), (139, 2), (140, 1), (141, 1), (142, 1), (143, 1), (144, 1), (145, 1), (146, 1), (147, 1), (148, 1), (149, 1), (150, 1), (151, 2), (152, 1), (153, 1), (154, 1), (155, 1), (156, 3), (157, 3), (158, 1), (159, 1), (160, 1), (161, 1), (162, 1), (163, 1), (164, 2), (165, 1), (166, 1), (167, 1), (168, 1), (169, 1), (170, 1), (171, 1), (172, 1), (173, 1), (174, 2), (175, 1), (176, 1), (177, 1), (178, 1), (179, 2), (180, 1), (181, 2), (182, 1), (183, 2), (184, 1), (185, 2), (186, 1), (187, 1), (188, 1), (189, 1), (190, 1), (191, 1), (192, 1), (193, 1), (194, 1), (195, 1), (196, 1), (197, 1), (198, 2), (199, 2), (200, 1), (201, 1), (202, 1), (203, 1), (204, 5), (205, 2), (206, 3), (207, 1), (208, 10), (209, 1), (210, 1), (211, 1), (212, 1), (213, 1), (214, 23), (215, 1), (216, 2), (217, 1), (218, 1), (219, 1), (220, 3), (221, 1), (222, 1), (223, 9), (224, 2), (225, 2), (226, 1), (227, 1), (228, 1), (229, 2), (230, 1), (231, 1), (232, 1), (233, 1), (234, 1), (235, 1), (236, 1), (237, 1), (238, 1), (239, 1), (240, 1), (241, 4), (242, 2), (243, 1), (244, 2), (245, 1), (246, 1), (247, 1), (248, 1), (249, 1), (250, 2), (251, 1), (252, 2), (253, 1), (254, 1), (255, 2), (256, 7), (257, 1), (258, 1), (259, 3), (260, 1), (261, 1), (262, 1), (263, 1), (264, 1), (265, 1), (266, 1), (267, 2), (268, 3), (269, 1), (270, 1), (271, 2), (272, 2), (273, 1), (274, 1), (275, 1), (276, 1), (277, 2), (278, 1), (279, 1), (280, 1), (281, 1), (282, 1), (283, 1), (284, 1), (285, 1), (286, 2), (287, 1), (288, 1), (289, 1), (290, 1), (291, 1), (292, 1), (293, 1), (294, 1), (295, 1), (296, 1), (297, 1), (298, 3), (299, 1), (300, 1), (301, 1), (302, 1), (303, 2), (304, 1), (305, 2), (306, 1), (307, 2), (308, 1), (309, 1), (310, 1), (311, 1), (312, 1), (313, 1), (314, 3), (315, 1), (316, 1), (317, 1), (318, 1), (319, 1), (320, 1), (321, 1), (322, 2), (323, 1), (324, 1), (325, 1), (326, 1)]\n"
     ]
    }
   ],
   "source": [
    "from gensim import models\n",
    "tf_ko = [dictionary_ko.doc2bow(text) for text in texts_ko]\n",
    "print (tf_ko[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_model_ko = models.TfidfModel(tf_ko)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, 2), (1, 2), (2, 7), (3, 1), (4, 1), (5, 3), (6, 2), (7, 1), (8, 2), (9, 1)]\n",
      "[(214, 23), (208, 10), (223, 9), (2, 7), (256, 7), (79, 5), (204, 5), (60, 4), (241, 4), (5, 3)]\n"
     ]
    }
   ],
   "source": [
    "tfidf_ko = tfidf_model_ko[tf_ko]\n",
    "corpora.MmCorpus.serialize('ko.mm', tfidf_ko) # save corpus to file for future use\n",
    "\n",
    "# print first 10 elements of first document's tf-idf vector\n",
    "print(tfidf_ko.corpus[0][:10])\n",
    "# print top 10 elements of first document's tf-idf vector\n",
    "print(sorted(tfidf_ko.corpus[0], key=lambda x: x[1], reverse=True)[:10])\n",
    "# print token of most frequent element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, '0.153*\"ㅎㅎ/KoreanParticle\" + 0.138*\"ㅠㅠ/KoreanParticle\" + 0.114*\"왕궁/Noun\" + 0.089*\"ㅋㅋ/KoreanParticle\" + 0.087*\"ㄷㄷ/KoreanParticle\"'), (1, '-0.417*\"왕궁/Noun\" + -0.223*\"ㅎㅎ/KoreanParticle\" + -0.218*\"힐러/Noun\" + -0.174*\"지하/Noun\" + -0.172*\"보스/Noun\"'), (2, '0.378*\"ㅎㅎ/KoreanParticle\" + -0.207*\"그래픽/Noun\" + 0.174*\"무역/Noun\" + -0.156*\"블소/Noun\" + -0.154*\"시발/Noun\"')]\n"
     ]
    }
   ],
   "source": [
    "ntopics, nwords = 3, 5\n",
    "lsi_ko = models.lsimodel.LsiModel(tfidf_ko, id2word=dictionary_ko, num_topics=ntopics)\n",
    "print(lsi_ko.print_topics(num_topics=ntopics, num_words=nwords))\n",
    "\n",
    "topics = lsi_ko.print_topics(num_topics=ntopics, num_words=nwords)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "topic 1  (0, '0.153*\"ㅎㅎ/KoreanParticle\" + 0.138*\"ㅠㅠ/KoreanParticle\" + 0.114*\"왕궁/Noun\" + 0.089*\"ㅋㅋ/KoreanParticle\" + 0.087*\"ㄷㄷ/KoreanParticle\"')\n",
      "topic 2  (1, '-0.417*\"왕궁/Noun\" + -0.223*\"ㅎㅎ/KoreanParticle\" + -0.218*\"힐러/Noun\" + -0.174*\"지하/Noun\" + -0.172*\"보스/Noun\"')\n",
      "topic 3  (2, '0.378*\"ㅎㅎ/KoreanParticle\" + -0.207*\"그래픽/Noun\" + 0.174*\"무역/Noun\" + -0.156*\"블소/Noun\" + -0.154*\"시발/Noun\"')\n"
     ]
    }
   ],
   "source": [
    "count = 0\n",
    "for t in topics:\n",
    "    count = count + 1\n",
    "    print('topic ' +str(count) + ' ', t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(0, '0.001*\"ㅠㅠ/KoreanParticle\" + 0.001*\"ㅎㅎ/KoreanParticle\" + 0.001*\"ㅡㅡ/KoreanParticle\" + 0.001*\"노동/Noun\" + 0.001*\"ㅋㅋ/KoreanParticle\"'), (1, '0.000*\"ㅎㅎ/KoreanParticle\" + 0.000*\"ㄷㄷ/KoreanParticle\" + 0.000*\"ㅠㅠ/KoreanParticle\" + 0.000*\"대륙/Noun\" + 0.000*\"ㅋㅋ/KoreanParticle\"'), (2, '0.001*\"왕궁/Noun\" + 0.001*\"ㅎㅎ/KoreanParticle\" + 0.001*\"ㅠㅠ/KoreanParticle\" + 0.001*\"보스/Noun\" + 0.001*\"힐러/Noun\"')]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np; np.random.seed(42)  # optional\n",
    "lda_ko = models.ldamodel.LdaModel(tfidf_ko, id2word=dictionary_ko, num_topics=ntopics)\n",
    "print(lda_ko.print_topics(num_topics=ntopics, num_words=nwords))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-e2f8f0db6df0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m;\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mseed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m42\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# optional\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mhdp_ko\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhdpmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mHdpModel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtfidf_ko\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mid2word\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdictionary_ko\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhdp_ko\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprint_topics\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtopics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mntopics\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtopn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnwords\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\gensim\\models\\hdpmodel.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, corpus, id2word, max_chunks, max_time, chunksize, kappa, tau, K, T, alpha, gamma, eta, scale, var_converge, outputdir, random_state)\u001b[0m\n\u001b[0;32m    190\u001b[0m         \u001b[1;31m# if a training corpus was provided, start estimating the model right away\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    191\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcorpus\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 192\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcorpus\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    193\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    194\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0minference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchunk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\gensim\\models\\hdpmodel.py\u001b[0m in \u001b[0;36mupdate\u001b[1;34m(self, corpus)\u001b[0m\n\u001b[0;32m    224\u001b[0m         \u001b[1;32mwhile\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    225\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mchunk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcorpus\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchunksize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 226\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_chunk\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    227\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mm_num_docs_processed\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mchunk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    228\u001b[0m                 \u001b[0mchunks_processed\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\gensim\\models\\hdpmodel.py\u001b[0m in \u001b[0;36mupdate_chunk\u001b[1;34m(self, chunk, update, opt_o)\u001b[0m\n\u001b[0;32m    287\u001b[0m                     \u001b[0mss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mElogsticks_1st\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    288\u001b[0m                     \u001b[0munique_words\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdoc_word_ids\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 289\u001b[1;33m                     \u001b[0mdoc_word_counts\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mm_var_converge\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    290\u001b[0m                 )\n\u001b[0;32m    291\u001b[0m                 \u001b[0mcount\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdoc_word_counts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\gensim\\models\\hdpmodel.py\u001b[0m in \u001b[0;36mdoc_e_step\u001b[1;34m(self, ss, Elogsticks_1st, unique_words, doc_word_ids, doc_word_counts, var_converge)\u001b[0m\n\u001b[0;32m    365\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    366\u001b[0m             \u001b[1;31m# X part, the data part\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 367\u001b[1;33m             \u001b[0mlikelihood\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mphi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar_phi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mElogbeta_doc\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mdoc_word_counts\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    368\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    369\u001b[0m             \u001b[0mconverge\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mlikelihood\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mold_likelihood\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mold_likelihood\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import numpy as np; np.random.seed(42)  # optional\n",
    "hdp_ko = models.hdpmodel.HdpModel(tfidf_ko, id2word=dictionary_ko)\n",
    "print(hdp_ko.print_topics(topics=ntopics, topn=nwords))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# word 2 vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import word2vec\n",
    "wv_model_ko = word2vec.Word2Vec(texts_ko)\n",
    "wv_model_ko.init_sims(replace=True)\n",
    "wv_model_ko.save('ko_word2vec.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('재밌냐/Adjective', 0.9682403206825256),\n",
       " ('큰일/Noun', 0.9589104652404785),\n",
       " ('미성년/Noun', 0.9584646821022034),\n",
       " ('상식/Noun', 0.9578827619552612),\n",
       " ('부자/Noun', 0.9574700593948364),\n",
       " ('형량/Noun', 0.9571524858474731),\n",
       " ('언플/Noun', 0.9559289216995239),\n",
       " ('인물/Noun', 0.9558865427970886),\n",
       " ('어쨋/Noun', 0.9556173086166382),\n",
       " ('업뎃/Noun', 0.9551690816879272)]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv_model_ko.most_similar(['고려/Noun'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# word to vec visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1241\n"
     ]
    }
   ],
   "source": [
    "c=0\n",
    "word = []\n",
    "for k, v in enumerate(textset.keys()):\n",
    "    if textset[v] >= 200:\n",
    "        c+=1\n",
    "        word.append(str(v))\n",
    "print (c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "wv_texts_ko = []\n",
    "\n",
    "for text in texts_ko:\n",
    "    temp2= []\n",
    "    for t in text:\n",
    "        if t not in word:\n",
    "            continue\n",
    "        temp2.append(t)\n",
    "    wv_texts_ko.append(temp2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gensim.models import word2vec\n",
    "wv_model_ko = word2vec.Word2Vec(wv_texts_ko)\n",
    "wv_model_ko.init_sims(replace=True)\n",
    "wv_model_ko.save('ko_word2vec_morethan_200.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-246-73ab2f6bf71c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m \u001b[0mtsne\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTSNE\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_components\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 15\u001b[1;33m \u001b[0mX_tsne\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtsne\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_tsne\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvocab\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'x'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'y'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\manifold\\t_sne.py\u001b[0m in \u001b[0;36mfit_transform\u001b[1;34m(self, X, y)\u001b[0m\n\u001b[0;32m    856\u001b[0m             \u001b[0mEmbedding\u001b[0m \u001b[0mof\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mtraining\u001b[0m \u001b[0mdata\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mlow\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mdimensional\u001b[0m \u001b[0mspace\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    857\u001b[0m         \"\"\"\n\u001b[1;32m--> 858\u001b[1;33m         \u001b[0membedding\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    859\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0membedding_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0membedding\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    860\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0membedding_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\manifold\\t_sne.py\u001b[0m in \u001b[0;36m_fit\u001b[1;34m(self, X, skip_num_points)\u001b[0m\n\u001b[0;32m    768\u001b[0m                           \u001b[0mX_embedded\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mX_embedded\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    769\u001b[0m                           \u001b[0mneighbors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mneighbors_nn\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 770\u001b[1;33m                           skip_num_points=skip_num_points)\n\u001b[0m\u001b[0;32m    771\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    772\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\manifold\\t_sne.py\u001b[0m in \u001b[0;36m_tsne\u001b[1;34m(self, P, degrees_of_freedom, n_samples, X_embedded, neighbors, skip_num_points)\u001b[0m\n\u001b[0;32m    810\u001b[0m         \u001b[0mP\u001b[0m \u001b[1;33m*=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mearly_exaggeration\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    811\u001b[0m         params, kl_divergence, it = _gradient_descent(obj_func, params,\n\u001b[1;32m--> 812\u001b[1;33m                                                       **opt_args)\n\u001b[0m\u001b[0;32m    813\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    814\u001b[0m             print(\"[t-SNE] KL divergence after %d iterations with early \"\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\manifold\\t_sne.py\u001b[0m in \u001b[0;36m_gradient_descent\u001b[1;34m(objective, p0, it, n_iter, n_iter_check, n_iter_without_progress, momentum, learning_rate, min_gain, min_grad_norm, verbose, args, kwargs)\u001b[0m\n\u001b[0;32m    342\u001b[0m         \u001b[0minc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mupdate\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mgrad\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0.0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    343\u001b[0m         \u001b[0mdec\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 344\u001b[1;33m         \u001b[0mgains\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minc\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m0.2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    345\u001b[0m         \u001b[0mgains\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdec\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m*=\u001b[0m \u001b[1;36m0.8\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    346\u001b[0m         \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgains\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmin_gain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mgains\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import gensim\n",
    "import gensim.models as g\n",
    "from sklearn.manifold import TSNE\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "model = g.Doc2Vec.load('ko_word2vec_morethan_200.model')\n",
    "\n",
    "import matplotlib\n",
    "from matplotlib import font_manager, rc\n",
    "\n",
    "vocab = list(model.wv.vocab)\n",
    "X = model[vocab]\n",
    "\n",
    "tsne = TSNE(n_components=2)\n",
    "X_tsne = tsne.fit_transform(X)\n",
    "\n",
    "df = pd.DataFrame(X_tsne, index=vocab, columns=['x', 'y'])\n",
    "\n",
    "font_list = font_manager.findSystemFonts(fontpaths=None, fontext='ttf')\n",
    "\n",
    "font_name = font_manager.FontProperties(fname=\"c:/windows/fonts/ngulim.ttf\").get_name()\n",
    "matplotlib.rc('font', family=font_name, size=20)\n",
    "\n",
    "fig = plt.figure(figsize=(100, 100))\n",
    "\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.scatter(df['x'], df['y'], s=100)\n",
    "\n",
    "for word, pos in df.iterrows():\n",
    "    ax.annotate(word, pos)\n",
    "\n",
    "plt.savefig('more_than_200_size100_0611.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# word2vec clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: DeprecationWarning: Call to deprecated `__getitem__` (Method will be removed in 4.0.0, use self.wv.__getitem__() instead).\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "wordtovec = []\n",
    "for w in word:\n",
    "    wordtovec.append((str(w), wv_model_ko[w]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n",
      "\n",
      "Current values:\n",
      "NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)\n",
      "NotebookApp.rate_limit_window=3.0 (secs)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(wordtovec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "wordtovecDF = pd.DataFrame.from_items(wordtovec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "wordtovecDF = wordtovecDF.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    0         1         2         3         4         5   \\\n",
      "얼마/Noun      -0.001363  0.006044 -0.147057  0.003991 -0.135764 -0.128842   \n",
      "언니/Noun      -0.080264 -0.054018  0.074703  0.053057 -0.119738  0.164354   \n",
      "버그/Noun       0.170473 -0.142893  0.097201  0.095709 -0.000038 -0.104791   \n",
      "있었/Adjective  0.042356 -0.019842  0.242474 -0.031464 -0.090970 -0.082535   \n",
      "타고/Noun      -0.128507 -0.017313 -0.067460 -0.060048 -0.070833  0.245632   \n",
      "\n",
      "                    6         7         8         9     ...           90  \\\n",
      "얼마/Noun       0.004462  0.008435  0.093333  0.018758    ...    -0.250962   \n",
      "언니/Noun      -0.040345  0.183135 -0.043505 -0.184397    ...     0.104483   \n",
      "버그/Noun      -0.019098  0.161087 -0.228175 -0.099509    ...     0.039611   \n",
      "있었/Adjective  0.035881  0.221292 -0.135147 -0.209253    ...     0.004033   \n",
      "타고/Noun      -0.092574  0.133317  0.022753 -0.142898    ...    -0.068437   \n",
      "\n",
      "                    91        92        93        94        95        96  \\\n",
      "얼마/Noun      -0.005462  0.024633 -0.050680  0.042050 -0.206298  0.096333   \n",
      "언니/Noun       0.092778 -0.019538  0.013602  0.017358  0.232266 -0.063616   \n",
      "버그/Noun      -0.125942 -0.148904  0.042005 -0.182664  0.031624  0.100194   \n",
      "있었/Adjective  0.197215  0.095826  0.045402  0.079654  0.077587 -0.007405   \n",
      "타고/Noun       0.187223  0.142893  0.036908  0.203386  0.025416  0.009116   \n",
      "\n",
      "                    97        98        99  \n",
      "얼마/Noun       0.009311 -0.040713 -0.076031  \n",
      "언니/Noun       0.016077 -0.022269 -0.127338  \n",
      "버그/Noun      -0.046909  0.011268 -0.065578  \n",
      "있었/Adjective  0.033271  0.016615 -0.242554  \n",
      "타고/Noun      -0.133735 -0.061361 -0.030847  \n",
      "\n",
      "[5 rows x 100 columns]\n",
      "1241\n"
     ]
    }
   ],
   "source": [
    "print(wordtovecDF.head())\n",
    "print(len(wordtovecDF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "\n",
    "kmeans = KMeans(n_clusters=3)\n",
    "\n",
    "kmeans = kmeans.fit(wordtovecDF)\n",
    "\n",
    "labels = kmeans.predict(wordtovecDF)\n",
    "\n",
    "centroids = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27b4c44dc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ks = range(1, 30)\n",
    "inertias = []\n",
    "for k in ks:\n",
    "    model = KMeans(n_clusters=k)\n",
    "    model.fit(wordtovecDF)\n",
    "    inertias.append(model.inertia_)\n",
    "\n",
    "matplotlib.rc('font', family=font_name, size=10)\n",
    "plt.plot(ks, inertias, '-o')\n",
    "plt.xlabel('number of clusters, k')\n",
    "plt.ylabel('inertia')\n",
    "plt.xticks(ks)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=10)\n",
    "\n",
    "kmeans = kmeans.fit(wordtovecDF)\n",
    "\n",
    "labels = kmeans.predict(wordtovecDF)\n",
    "\n",
    "centroids = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "wordtovecDF['label'] = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                     0         1         2         3         4         5  \\\n",
      "얼마/Noun      -0.001363  0.006044 -0.147057  0.003991 -0.135764 -0.128842   \n",
      "언니/Noun      -0.080264 -0.054018  0.074703  0.053057 -0.119738  0.164354   \n",
      "버그/Noun       0.170473 -0.142893  0.097201  0.095709 -0.000038 -0.104791   \n",
      "있었/Adjective  0.042356 -0.019842  0.242474 -0.031464 -0.090970 -0.082535   \n",
      "타고/Noun      -0.128507 -0.017313 -0.067460 -0.060048 -0.070833  0.245632   \n",
      "\n",
      "                     6         7         8         9  ...          91  \\\n",
      "얼마/Noun       0.004462  0.008435  0.093333  0.018758  ...   -0.005462   \n",
      "언니/Noun      -0.040345  0.183135 -0.043505 -0.184397  ...    0.092778   \n",
      "버그/Noun      -0.019098  0.161087 -0.228175 -0.099509  ...   -0.125942   \n",
      "있었/Adjective  0.035881  0.221292 -0.135147 -0.209253  ...    0.197215   \n",
      "타고/Noun      -0.092574  0.133317  0.022753 -0.142898  ...    0.187223   \n",
      "\n",
      "                    92        93        94        95        96        97  \\\n",
      "얼마/Noun       0.024633 -0.050680  0.042050 -0.206298  0.096333  0.009311   \n",
      "언니/Noun      -0.019538  0.013602  0.017358  0.232266 -0.063616  0.016077   \n",
      "버그/Noun      -0.148904  0.042005 -0.182664  0.031624  0.100194 -0.046909   \n",
      "있었/Adjective  0.095826  0.045402  0.079654  0.077587 -0.007405  0.033271   \n",
      "타고/Noun       0.142893  0.036908  0.203386  0.025416  0.009116 -0.133735   \n",
      "\n",
      "                    98        99  label  \n",
      "얼마/Noun      -0.040713 -0.076031      9  \n",
      "언니/Noun      -0.022269 -0.127338      3  \n",
      "버그/Noun       0.011268 -0.065578      6  \n",
      "있었/Adjective  0.016615 -0.242554      6  \n",
      "타고/Noun      -0.061361 -0.030847      0  \n",
      "\n",
      "[5 rows x 101 columns]\n"
     ]
    }
   ],
   "source": [
    "print(wordtovecDF.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['어디/Noun', '사자/Noun', '빠른/Adjective', '어떻/Adjective', '아이템/Noun', '이의/Noun', '씨앗/Noun', '바람/Noun', '어디서/Adverb', '기억/Noun', '단축키/Noun', '입수/Noun', '날틀/Noun', '아기/Noun', '릴리/Noun', '신기루/Noun', '방법/Noun', '우물/Noun', '설정/Noun', '가방/Noun', '점프/Noun', '어딧어/Noun', '귓속말/Noun', '언제/Noun', '사용/Noun', '어딧/Noun', '찾기/Noun', '혹시/Noun', '마구간/Noun', '지기/Noun', '퀘스트/Noun', '망토/Noun', '안보/Noun', '기절/Noun', '엘프/Noun', '수리/Noun', '보리/Noun', '대체/Noun', '당나귀/Noun', '사나/Noun', '장소/Noun', '위치/Noun', '정보/Noun', '공격대/Noun', '어떤/Adjective', 'ㄳㄳ/KoreanParticle', '소환/Noun', '엔피씨/Noun', '각성/Noun', '신관/Noun', '반지/Noun', '살피/Noun', '주머니/Noun', '기계/Noun', '타레/Noun', '저장/Noun', '님들/Noun', 'ㅎㅇ/KoreanParticle', '호수/Noun', '하나요/Noun', '기록/Noun', '엔피시/Noun', '어캐/Noun', '아시/Noun', '어딧음/Noun', '성장/Noun', '물이/Noun', '무지개/Noun', '호랑이/Noun', '편지/Noun', '작은/Adjective', '상인/Noun', '늑대/Noun', '비밀/Noun', '왜안/Noun', '구함/Noun', '부품/Noun', '어딧는/Noun', '문서/Noun', '하만/Noun', '먹이/Noun', '검은/Adjective', '망아지/Noun', '엘크/Noun', 'ㅈㅈ/KoreanParticle', '발차기/Noun', '탈것/Noun', '안나/Noun', '레드/Noun', '코볼트/Noun', '공학자/Noun', '달리기/Noun', '사슴/Noun', '저승/Noun', '플로/Noun', '오이/Noun', '감자/Noun', '도대체/Noun', '메인/Noun', '협정/Noun', '수집/Noun', '비행선/Noun', '이동/Noun', '타요/Noun', '어찌/Noun', '대여/Noun', '돌이/Noun', '탈출/Noun', '아련한/Adjective', '신발/Noun', '공간/Noun', '없던/Adjective', '합죽이/Noun', '마리안/Noun', '우유/Noun', '잉크/Noun', '나스/Noun', '수도사/Noun']\n"
     ]
    }
   ],
   "source": [
    "print(list(wordtovecDF.loc[(wordtovecDF['label'])==2].T.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 = 137\n",
      "1 = 109\n",
      "2 = 118\n",
      "3 = 123\n",
      "4 = 140\n",
      "5 = 185\n",
      "6 = 127\n",
      "7 = 126\n",
      "8 = 64\n",
      "9 = 112\n",
      "10 = 0\n",
      "11 = 0\n",
      "12 = 0\n",
      "13 = 0\n",
      "14 = 0\n",
      "15 = 0\n",
      "16 = 0\n",
      "17 = 0\n",
      "18 = 0\n",
      "19 = 0\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, 20):\n",
    "    print(i, '=', len(wordtovecDF.loc[(wordtovecDF['label'])==int(i)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusterWordList = []\n",
    "for i in range(0, 20):\n",
    "    clusterWordList.append(list((wordtovecDF.loc[(wordtovecDF['label'])==int(i)].T.keys())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['어서/Noun', '그럼/Adjective', '아하/Noun', 'ㅎㅎ/KoreanParticle', '혹시/Noun', '가요/Noun', '초대/Noun', '많으/Adjective', '공격대/Noun', '중이/Noun', '확인/Noun', '서요/Noun', '안녕하세/Adjective', '잠시/Noun', '부탁드립니/Adjective', '설문/Noun', '안녕히/Adjective', '말씀/Noun', '계시/Noun', '해주시/Noun', '감사/Noun', '이만/Adverb', '주시/Noun', '나영/Noun', '번만/Noun', '부탁드려/Adjective', '하이/Noun', '금방/Noun', 'ㅅㅅ/KoreanParticle', '세영/Noun', '우선/Noun', '이요/Noun', '대요/Noun', '방가/Noun', '가나/Noun', '이용/Noun', '대장/Noun', '초보/Noun', '연락/Noun', '귓말/Noun', '죄송한/Adjective', '가보/Noun', 'ㅎㅇㅎㅇ/KoreanParticle', '이세/Noun', '이분/Noun', '반갑/Adjective', '탈퇴/Noun', '해체/Noun', '축하/Noun', '설치/Noun', '이따가/Adverb', '하심/Noun', '부탁/Noun', '척후/Noun', '가능할/Adjective', '폭탄/Noun', '마리아/Noun', '반가워/Adjective', '셀렌/Noun', '무릎/Noun', '풀이/Noun', '초소/Noun', '타임/Noun', '가능해/Adjective', '주심/Noun', '작업/Noun', '에드/Noun', '화이팅/Noun', '노노/Noun', '분도/Noun', '디오/Noun'], ['있었/Adjective', '대박/Noun', '무것/Noun', '없는/Adjective', '그래도/Adverb', '근데/Adverb', '생각/Noun', '엄청/Adverb', '그게/Noun', '없어/Adjective', '그렇/Adjective', '이런/Adjective', '아니/Adjective', '같아/Adjective', '같은/Adjective', '역시/Noun', '어려워/Adjective', '비슷한/Adjective', '많이/Adverb', '그런/Adjective', '완전/Noun', '때문/Noun', '그건/Adverb', '뭔가/Noun', '그렇게/Adverb', '괜찮/Adjective', '없고/Adjective', '보고/Noun', '아닌/Adjective', '서도/Noun', '없네/Adjective', '아쉽/Adjective', '약간/Noun', '왠지/Adverb', '활성화/Noun', '문제/Noun', '오래/Noun', '정말/Noun', '다른/Noun', '별거/Noun', '없었/Adjective', '별로/Noun', '자기/Noun', '원래/Noun', '숫자/Noun', '어려운/Adjective', '젠장/Noun', '그래/Adjective', '전문/Noun', '케릭/Noun', '그럴/Adjective', '된거/Noun', '같네/Adjective', '초반/Noun', '크게/Noun', '본적/Noun', '하니/Noun', '기도/Noun', '전혀/Noun', '딱히/Adjective', '확실히/Adjective', '같던/Adjective', '애매/Noun', '그랬/Adjective', '신경/Noun', '나름/Noun', '아닐/Adjective', '아예/Noun', '살이/Noun', '상관/Noun', '같어/Adjective'], ['무기/Noun', '이상/Noun', '양손/Noun', '모자/Noun', '지팡이/Noun', '투구/Noun', '가죽/Noun', '민첩/Noun', '수치/Noun', '크리/Noun', '음식/Noun', '단검/Noun', '방패/Noun', '바지/Noun', '판금/Noun', '어둠/Noun', '보호/Noun', '방어/Noun', '쌍수/Noun', '쓰시/Noun', '고대/Noun', '희귀/Noun', '장비/Noun', '허리/Noun', '영웅/Noun', '착용/Noun', '낮아/Adjective', '높으/Adjective', '금은/Noun', '렙제/Noun', '세트/Noun', '등급/Noun', '기능/Noun', '초록/Noun', '단계/Noun', '수습/Noun', '확률/Noun', '손검/Noun', '장갑/Noun', '장신구/Noun', '별루/Noun', '유랑/Noun', '제한/Noun', '현아/Noun'], ['얼마/Noun', '하나/Noun', '이의/Noun', '있으/Adjective', '경매/Noun', '비싸네/Adjective', '눈물/Noun', '햇빛/Noun', '가루/Noun', '시세/Noun', '정도/Noun', '달빛/Noun', '결정/Noun', '사실/Noun', '선물/Noun', '별빛/Noun', '지구/Noun', '가격/Noun', '조각/Noun', '비쌈/Adjective', '신분/Noun', '일반/Noun', '연마/Noun', '구리/Noun', '광석/Noun', '저승/Noun', '높은/Adjective', '상점/Noun', '분해/Noun', '조그만/Noun', '당근/Noun', '숙련/Noun', '제작/Noun', '잡화/Noun', '수정/Noun', '가가/Noun', '기름/Noun', 'ㅍㅍ/KoreanParticle', '돌이/Noun', '종류/Noun', '필요하/Adjective', '요리/Noun', '업글/Noun', '명예/Noun', '고급/Noun', '판매/Noun', '편린/Noun', '목공/Noun', '제시/Noun', '흔적/Noun', '금속/Noun', '귓주세/Noun', '파라/Noun', '교환/Noun', '광맥/Noun', '명인/Noun', '구매/Noun', '사지/Noun', '은광/Noun', '배하/Noun', '정수/Noun', '순도/Noun', '산호/Noun', '전문가/Noun', '장인/Noun', '정제/Noun', '명점/Noun', '지마도/Noun'], ['누구/Noun', '토끼/Noun', '오늘/Noun', '사람/Noun', '공유/Noun', '가능한/Adjective', '편하게/Adjective', '모두/Noun', '입니/Adjective', '연합/Noun', '살인/Noun', '세력/Noun', '무죄/Noun', '문의/Noun', '지코/Noun', '부대/Noun', '크레이지/Noun', '형님/Noun', '맨더/Noun', '쪼렙/Noun', '공대/Noun', '레이드/Noun', '고해/Noun', '이벤트/Noun', '사형/Noun', '길드/Noun', '끼리/Noun', '저희/Noun', '아프리카/Noun', '원정/Noun', '네이버/Noun', '방송/Noun', '나이/Noun', '동맹/Noun', '대가/Noun', '막피/Noun', '가족/Noun', '함께/Adverb', '현재/Noun', '드라이어/Noun', '우스/Noun', '가입/Noun', '치기/Noun', '조건/Noun', '아르/Noun', '반야심경/Noun', '우레/Noun', '당했/Adjective', '나이스/Noun', '오랜만/Noun', '카페/Noun', '서로/Noun', '피노키오/Noun', '대원/Noun', '바쁘/Adjective', '검색/Noun', '러너/Noun', '선포/Noun', '리하/Noun', '신청/Noun', '버스/Noun', '대도/Noun', '적대/Noun', '러시아/Noun', '길마/Noun', '투표/Noun', '우회/Noun', '호남/Noun'], ['어렵/Adjective', '바닥/Noun', '바로/Noun', '녀석/Noun', '계속/Noun', '일단/Noun', 'ㄱㄱ/KoreanParticle', '야함/Adjective', '정령/Noun', '무서운/Adjective', '한번/Noun', '아무래도/Adverb', '어그/Noun', '무지/Noun', '보스/Noun', '먼저/Noun', '마나/Noun', '힐러/Noun', '다시/Noun', '순서/Noun', '마지막/Noun', '분만/Noun', '모집/Noun', '어글/Noun', '아프/Adjective', '왕궁/Noun', '던전/Noun', '없으/Adjective', '파티/Noun', '지하/Noun', '외침/Noun', '도발/Noun', '힘들어/Adjective', '지하가/Noun', '갑옷/Noun', '물약/Noun', '초기/Noun', '오우거/Noun', '주네/Noun', '무적/Noun', '사제/Noun', '화가/Noun', '서방/Noun', '힘듦/Adjective', '없이/Adverb', '물속/Noun', '루팅/Noun', '힘들/Adjective', '시구/Noun', '물통/Noun', '천천히/Adverb', '처음/Noun', '부터/Noun', 'ㅈㅅ/KoreanParticle', '이서/Noun', '힘드/Noun', '클리/Noun', '죄송합/Adjective', 'ㅌㅌ/KoreanParticle', '야할/Adjective', '신가/Noun', '오시/Noun', '입장/Noun', '자리/Noun', '랜덤/Noun', '부활/Noun', '파티원/Noun', '구해/Noun', 'ㅇㅋ/KoreanParticle', '잡몹/Noun', '근위병/Noun', '리젠/Noun', '몸빵/Noun', '순삭/Noun', '건가/Noun', '화살/Noun', '잡음/Noun', '몇번/Noun', '신도/Noun', '인장/Noun', '피리/Noun', '왕국/Noun', '정원/Noun', '피해/Noun', 'ㅅㄱㅇ/KoreanParticle', '가시/Noun', '잡고/Noun', '잡기/Noun', '열쇠/Noun', '탱커/Noun', '가운데/Noun', '포식자/Noun', 'ㅜㅡ/KoreanParticle', '팟좀/Noun', '로만/Noun', '정리/Noun', '에고/Noun', '근딜/Noun', '딜러/Noun', '가능하/Adjective', '치면/Noun', '무조건/Noun', '반복/Noun', '슈바/Noun', '흐미/Noun', '이신/Noun', '아픔/Noun', '네임드/Noun', '파장/Noun', '특수/Noun', '도전/Noun', '불탄/Adjective', '탄성/Noun', '생기/Noun', '없죠/Adjective', '함정/Noun', '징표/Noun', '가용/Noun', '경험/Noun', '필드/Noun', '공략/Noun', '설명/Noun', '바퀴/Noun', '메즈/Noun', '고정/Noun', '보조/Noun', '차례/Noun', '어려움/Noun', '조심/Noun', '파초/Noun', '리셋/Noun', '인팟/Noun', '새로/Adjective', '뿌리/Noun', '패스/Noun', '애드/Noun', '범위/Noun', '원딜/Noun', '전멸/Noun', '파탈/Noun', '해제/Noun', '무리/Noun', '도실/Noun', '대기/Noun', '선빵/Noun', '힘드네/Noun', '정예/Noun', '매즈/Noun', '막판/Noun', '야해/Adjective', '사냥꾼/Noun', '이상하네/Adjective', '포션/Noun', '아크/Noun', '샬록/Noun', '힘든/Adjective', '고쉬/Noun', '팟점/Noun', '아파/Adjective', '피통/Noun', '얼음/Noun', '라서/Noun', '넴드/Noun', '오심/Noun', '처리/Noun', '주위/Noun', '빡시/Noun', '레버/Noun', '액체/Noun', '나갓/Noun', '방이/Noun', '화염/Noun', '둘이서/Noun', '두번째/Noun', '잡지/Noun', '도망/Noun', '패턴/Noun', '기둥/Noun', '빡세/Noun', '구도/Noun', '쉬워/Adjective', '토템/Noun', '광역/Noun', '주문/Noun', '공중/Noun', '오홍/Noun', '세명/Noun', '후딱/Adverb', '막보/Noun', '양동이/Noun', '무빙/Noun', '팟구해/Noun', '야하/Adjective', '불의/Noun', '물가/Noun', '로머/Noun', '유인/Noun', '타이밍/Noun', '리오/Noun', '딜로/Noun', '미아/Noun', '에밀/Noun', '선타/Noun', '로딩/Noun', '구슬/Noun', '왕실/Noun', '막넴/Noun', '점사/Noun'], ['ㅋㅋ/KoreanParticle', '이렇게/Adverb', '빠른/Adjective', '많다/Adjective', '라임/Noun', '이상해/Adjective', '주술사/Noun', '시험/Noun', '자꾸/Noun', '무서워/Adjective', '에이/Noun', '귀찮/Adjective', '하트/Noun', '아아/Noun', '오빠/Noun', '번호/Noun', '머리/Noun', '내용/Noun', '점프/Noun', '짜증/Noun', '너무/Noun', '더럽/Adjective', '딴거/Adjective', '가기/Noun', '이상한/Adjective', '느리/Adjective', '기절/Noun', '무엇/Noun', '엘프/Noun', '느려/Adjective', '많네/Adjective', '느림/Adjective', '초록색/Noun', '어이/Noun', '잘못/Noun', '삽질/Noun', '에프/Noun', '멀어/Adjective', '촌장/Noun', '약해/Adjective', '드럽/Noun', '무서움/Noun', '많아/Adjective', '아가/Noun', '하늘/Noun', '보드/Noun', '으악/Adverb', '괴물/Noun', '런가/Noun', '갑자기/Noun', '빨간/Adjective', '졸라/Noun', '엔피시/Noun', '페레/Noun', '말타/Noun', '빠르/Adjective', '개미/Noun', '경치/Noun', '오른/Noun', '심해/Noun', '회색/Noun', '몬스터/Noun', '되게/Adverb', '미치/Adjective', '이리/Adverb', '손가락/Noun', '다름/Adjective', '정화/Noun', '절벽/Noun', '화면/Noun', '질주/Noun', '반응/Noun', '가야대/Noun', '타가/Noun', '애가/Noun', '알트/Noun', '난왜/Noun', '아무리/Adverb', '안이/Noun', '멋진/Adjective', '이쁘/Adjective', '컨트롤/Noun', '가짐/Noun', '브이/Noun', '싫어/Adjective', '미니맵/Noun', '몇렙/Noun', 'ㅂㅂ/KoreanParticle', '히든/Noun', '물고기/Noun', '미안해/Adjective', '멋지/Adjective', '하래/Noun', '뻔했/Adjective', '어딜/Adverb', '고양이/Noun', '귀엽/Adjective', '없던/Adjective', '조작/Noun', '오우/Noun', '들소/Noun', 'ㅏㅏ/KoreanParticle', '모야/Noun', '야호/Noun', '이상하/Noun', '선공/Noun', '게뭐/Noun', '봐봐/Noun', '아이거/Noun', '어머/Noun', '많어/Adjective', '보라색/Noun', '느린/Adjective', '디게/Adverb', '겸치/Noun', '거구/Noun', '이쁜/Adjective', '뱌뱌/Noun'], ['클릭/Noun', '획득/Noun', '상자/Noun', '대화/Noun', '붉은/Adjective', '단축키/Noun', '입수/Noun', '아이/Noun', '설정/Noun', '가방/Noun', '화살표/Noun', '찾기/Noun', '친추/Noun', '마구간/Noun', '버튼/Noun', '안보/Noun', '부족/Noun', '가고일/Noun', '가안/Noun', 'ㅇㅎ/KoreanParticle', '물음표/Noun', '환경설정/Noun', '느낌표/Noun', '소환/Noun', '엔피씨/Noun', '갈기/Noun', '반지/Noun', '살피/Noun', '신전/Noun', '안대/Noun', '날개/Noun', '자루/Noun', '해골/Noun', '처치/Noun', '밀렵/Noun', '마우스/Noun', '변신/Noun', '동굴/Noun', '문서/Noun', '사마귀/Noun', '보물/Noun', '귀걸이/Noun', '여깃/Noun', '물소/Noun', '부엉이/Noun', '스콧/Noun', '인화/Noun', '물질/Noun', '경비/Noun', '에르/Noun', 'ㅈㅈ/KoreanParticle', '발차기/Noun', '군주/Noun', '가어/Noun', '레드/Noun', '코볼트/Noun', '거대/Noun', '공학자/Noun', '두목/Noun', '달리기/Noun', '플로/Noun', '브리/Noun', '안개/Noun', '시체/Noun', '지하실/Noun', '수집/Noun', '유령/Noun', '수호/Noun', '조사/Noun', '거미/Noun', '심복/Noun', '가치/Noun', '아이콘/Noun', 'ㅣㅁ/KoreanParticle', '치료/Noun', '속옷/Noun', '목걸이/Noun', '골렘/Noun', '봉인/Noun', '신발/Noun', '수상한/Noun', '산채/Noun', '합죽이/Noun', '마리안/Noun', '후타/Noun', '교단/Noun', '워짐/Noun', '대면/Noun', '나스/Noun', '디초/Noun', '거미줄/Noun', '장치/Noun', '횃불/Noun', '석상/Noun', '콜린/Noun', '마법진/Noun', '수도사/Noun', '제이콥/Noun'], ['사서/Noun', '산삼/Noun', '얼마나/Noun', '채집/Noun', '텃밭/Noun', '세금/Noun', '누가/Noun', '묘목/Noun', '나무/Noun', '서리/Noun', '주지/Noun', '벌목/Noun', '아보카도/Noun', '청소/Noun', '도구/Noun', '발자국/Noun', '박아/Noun', '엉겅퀴/Noun', '고무나무/Noun', '벼락/Noun', '불타는/Adjective', '그루/Noun', '카드/Noun', '발견/Noun', '식물/Noun', '농사/Noun', '노동/Noun', '허수아비/Noun', '충전/Noun', '달걀/Noun', '비싸/Adjective', '소나무/Noun', '아보/Noun', '부족해/Adjective', '길가/Noun', '대나무/Noun', '재배/Noun', '다자/Noun', '포도/Noun', '버드나무/Noun', '남아/Noun', '레몬/Noun', '침대/Noun', '공사/Noun', '현장/Noun', '떡갈나무/Noun', '딸기/Noun', '무화과/Noun', '울타리/Noun', '소모/Noun', '포도나무/Noun', '백합/Noun'], ['타고/Noun', '있는/Adjective', '가야/Noun', '있던/Adjective', '있지/Adjective', '하얀/Adjective', '여신/Noun', '기억/Noun', '매사/Noun', '고원/Noun', '저기/Noun', '어디가/Noun', '날틀/Noun', '마차/Noun', '있어/Adjective', '하디/Noun', '왼쪽/Noun', '어딘가/Noun', '릴리/Noun', '신기루/Noun', '가까운/Adjective', '지역/Noun', '있네/Adjective', '가까/Adjective', '방법/Noun', '다리/Noun', '건너/Noun', '멀리/Noun', '표시/Noun', '리드/Noun', '있자/Adjective', '방향/Noun', '인벤/Noun', '지도/Noun', '입구/Noun', '지기/Noun', '여러/Noun', '로이스터/Noun', '그거/Noun', '거기/Noun', '의문/Noun', '안쪽/Noun', '마을/Noun', '왕관/Noun', '장소/Noun', '버섯/Noun', '위치/Noun', '체크/Noun', '에서/Noun', '집안/Noun', '회수/Noun', '보시/Noun', '구릉지/Noun', '보물상자/Noun', '따로/Adverb', '오스/Noun', '다른사람/Noun', '주거지역/Noun', '있다/Adjective', '이안/Noun', '노래/Noun', '공용/Noun', '농장/Noun', '항구/Noun', '위쪽/Noun', '각성/Noun', '주머니/Noun', '잡화상/Noun', '근처/Noun', '도시/Noun', '저장/Noun', '계심/Noun', '건물/Noun', '사이/Noun', '포탈/Noun', '있긴/Adjective', '어느/Adverb', '보이/Noun', '고기/Noun', '있구/Adjective', '평원/Noun', '기록/Noun', '바위/Noun', '무지개/Noun', '벌판/Noun', '호랑이/Noun', '등뼈/Noun', '산맥/Noun', '광산/Noun', '상어/Noun', '해안/Noun', '상인/Noun', '상단/Noun', '주거/Noun', '다가/Noun', '모래/Noun', '광부/Noun', '안내/Noun', '지나/Noun', '가면/Noun', '사다리/Noun', '일로/Noun', '모양/Noun', '여행자/Noun', '등짐/Noun', '지고/Noun', '있죠/Adjective', '오름/Noun', '젖소/Noun', '아래/Noun', '언덕/Noun', '오른쪽/Noun', '가만히/Adverb', '황금/Noun', '중간/Noun', '모루/Noun', '마하/Noun', '데비/Noun', '정확히/Adjective', '목장/Noun', '비행선/Noun', '가축/Noun', '작물/Noun', '녹색/Noun', '뒤쪽/Noun', '이동/Noun', '탈수/Noun', '마굿/Noun', '주인/Noun', '갈수/Noun', '지점/Noun', '햇무리/Noun', '군데/Noun', '지정/Noun', '귀환/Noun', '소금/Noun', '자작나무/Noun', '주변/Noun', '먼지/Noun', '아샤/Noun', '쉬프트/Noun', '다모/Noun', '아련한/Adjective', '광장/Noun', '몰래/Noun', '동틀녘/Noun', '폭포/Noun', '계단/Noun', '있더/Adjective', '배달/Noun', '관리인/Noun', '공간/Noun', '자작/Noun', '중앙/Noun', '파수/Noun', '가까이/Noun', '꼭대기/Noun', '그늘/Noun', '푸른/Noun', '솔즈/Noun', '왕좌/Noun', '댕기/Noun', '잉크/Noun', '수목원/Noun', '비파/Noun', '반도/Noun', '야영지/Noun', '선장/Noun', '있잖/Adjective', '무덤/Noun', '구석/Noun', '대도시/Noun', '징조/Noun', '우측/Noun', '감시/Noun', '니드/Noun', '뭐시기/Noun'], ['미안/Noun', 'ㅡㅡ/KoreanParticle', 'ㅇㅇ/KoreanParticle', '야한/Adjective', '바보/Noun', '빨리/Adverb', '안해/Noun', '헤헤/Noun', '진짜/Noun', '돼지/Noun', 'ㅅㅂ/KoreanParticle', '미친/Adjective', '시발/Noun', '먹음/Noun', '야야/Noun', '망햇/Noun', '구만/Noun', '조종/Noun', '무슨/Noun', '존나/Noun', '빡치/Adjective', '머냐/Adjective', '이름/Noun', '벌써/Noun', '하하/Noun', '거지/Noun', '오지/Noun', '없다/Adjective', '라이/Noun', 'ㄴㄴ/KoreanParticle', '설마/Noun', '보라/Noun', '얼굴/Noun', '임마/Noun', '으앙/Adverb', '엄마/Noun', '아앙/Noun', '새끼/Noun', '사망/Noun', '나와라/Noun', 'ㅈㄴ/KoreanParticle', 'ㅏㄴ/KoreanParticle', '간지/Noun', '글자/Noun', '돋네/Adjective', '리기/Noun', '인간/Noun', '빡쳐/Adjective', '없지/Adjective', '아씨/Noun', '머임/Noun', '타조/Noun', '무시/Noun', '주금/Noun', 'ㅁㅊ/KoreanParticle', '도축/Noun', '마자/Noun', '을해/Noun', '빠름/Adjective', '오오/Noun', '모름/Noun', '자라/Noun', '점검/Noun', 'ㄱㅊ/KoreanParticle', '제거/Noun', '시러/Adjective', '친구/Noun', '애기/Noun', '겜톡/Noun', '쩐다/Noun', '시바/Noun', '하자/Noun', '가라/Noun', '흐흐/Noun', 'ㅣㄴ/KoreanParticle', '거봐/Noun', '요정/Noun', '돌진/Noun', '오예/Noun', '운지/Noun', '보임/Noun', '달성/Noun', 'ㅏㅁ/KoreanParticle', '도하/Noun', '야하지/Adjective', '드디어/Noun', '기네/Noun', '멘붕/Noun', '여우/Noun', '시벌/Noun', 'ㅏㄹ/KoreanParticle', '웃기/Noun', '린다/Noun', '햇음/Noun', '저리/Noun', '자식/Noun', '스틸/Noun', '나좀/Noun', '개쩜/Noun', '만드/Noun', '카톡/Noun', '마크/Noun', '동물/Noun', '시밤/Noun', '싫다/Adjective', '면서/Noun', '진자/Noun', '댄스/Noun', '안녕/Noun', '글쎄/Noun', '안타/Noun', 'ㅇㄴ/KoreanParticle', '복수/Noun', '뭐함/Noun', 'ㅁㅁ/KoreanParticle', '왜캐/Noun', '슈발/Noun', '갓어/Noun', '거뭐/Noun', '또또/Noun', '아빠/Noun', '커서/Noun', '주제/Noun', '분동/Noun', '기타/Noun', '대답/Noun', '아저씨/Noun', '퇴근/Noun', '왓어/Noun', '머함/Noun', '타라/Noun', '라벤더/Noun', '지네/Noun', '씨발/Noun', '미쳤/Adjective', '엇어/Noun', '물어/Noun', '시비/Noun', '낙사/Noun', '도둑질/Noun', '죤나/Noun', '증발/Noun', '존내/Noun', '이럼/Adjective', '늘왜/Noun'], ['내일/Noun', '약탈/Noun', '무역/Noun', '우리/Noun', '지금/Noun', '아침/Noun', '나나/Noun', '수도/Noun', '있겠/Adjective', '운전/Noun', '잠수/Noun', '하라/Noun', '은신/Noun', '달라/Noun', '도착/Noun', '어떠/Adjective', '쾌속/Noun', '정은/Noun', '부럽/Adjective', '상대/Noun', '자유/Noun', '준비/Noun', '감옥/Noun', '신고/Noun', '배심원/Noun', '범선/Noun', '권한/Noun', '벌레/Noun', '바다/Noun', '나룻배/Noun', '이즈/Noun', '패기/Noun', '돈좀/Noun', '로동/Noun', '킬러/Noun', '배타/Noun', '자유도/Noun', '우린/Noun', '갈래/Noun', '이아/Noun', '모험/Noun', '해적/Noun', '간다/Noun', '작살/Noun', '당함/Adjective', '싸움/Noun', '시차/Noun', '일드/Noun', '구경/Noun', '대륙/Noun', '모이/Noun', '십자/Noun', '이내/Noun', '학살/Noun', '하리/Noun', '하란/Noun', '가능/Noun', '재판/Noun', '속정/Noun', '점수/Noun', '대포/Noun', '왓는데/Noun', '위해/Noun', 'ㅇㄷ/KoreanParticle', '케이/Noun', '헉헉/Adverb', '말좀/Noun', '가드/Noun', '본캐/Noun', '마이크/Noun', '말로/Noun', '이미/Adverb', '마왕/Noun', '불명예/Noun', '오라/Noun', '헤엄/Noun', '가다가/Noun', '깜짝/Noun', '수영/Noun', '출발/Noun', '모드/Noun', '현상/Noun', '모래톱/Noun', '크라켄/Noun', '가나다라/Noun', '해파리/Noun', '정이/Noun', '목표/Noun', '역할/Noun', '만약/Noun', '진영/Noun', '생산/Noun', '지옥/Noun', 'ㄱㄷ/KoreanParticle', '이배/Noun', '호위/Noun', '가나다/Noun', '니스/Noun', '테르/Noun', '베베/Noun', '무인도/Noun', '전쟁/Noun', '자국/Noun', '무도/Noun', '노예/Noun', '눈앞/Noun', 'ㅊㅋ/KoreanParticle', '피케/Noun', '분노/Noun', '탈옥/Noun', '바다로/Noun', '번개/Noun', '난파선/Noun', '지원/Noun', '대리/Noun', '단체/Noun', '강제/Noun', '장기/Noun', '등산/Noun', '포탄/Noun', '초승달/Noun', '치킨/Noun', '숟가락/Noun', '자폭/Noun', '가게/Noun', '공초/Noun', '납치/Noun', '범죄/Noun', '전차/Noun', '초원/Noun', '보호지역/Noun', '당할/Adjective', '쇼크웨이브/Noun', '공성/Noun', '폭풍/Noun', '노르드/Noun', '판결/Noun', '쇼크/Noun', '황금평/Noun', '공개/Noun', '고요한/Noun', '중립/Noun', '봇짐/Noun', '황평/Noun', '블랙펄/Noun', '피비/Noun', '북맨/Noun', '영지/Noun', '늪지/Noun', '부케/Noun', '신난/Noun', '전방/Noun', '이득/Noun', '택시/Noun', '레이/Noun', '저택/Noun', '데타/Noun', '이로/Noun', '너구리/Noun', '로카/Noun', '수배/Noun', '팔천/Noun'], ['당신/Noun', '안남/Noun', '느낌/Noun', '이지/Noun', '저런/Noun', '매너/Noun', '나쁜/Adjective', '채팅/Noun', '관심/Noun', '종자/Noun', '인터넷/Noun', '사과/Noun', '길드워/Noun', '테스트/Noun', '한국/Noun', '아이디/Noun', '솔직히/Adjective', '재밌/Adjective', '사양/Noun', '지랄/Noun', '그래픽/Noun', '플레이/Noun', '게임/Noun', '아키/Noun', '옵션/Noun', '더러운/Adjective', '보기/Noun', '마라/Adjective', '멘탈/Noun', '와우/Noun', '제대로/Noun', '거나/Noun', '그만/Adverb', '제일/Noun', '직접/Noun', '공지/Noun', '서버/Noun', '다운/Noun', '테스/Noun', '다르/Adjective', '모든/Noun', '채널/Noun', '캐릭/Noun', '부분/Noun', '건의/Noun', '오픈/Noun', '낚시/Noun', '종료/Noun', '분명/Noun', '정식/Noun', '시스템/Noun', '시기/Noun', '테라/Noun', '송재경/Noun', '범죄자/Noun', '여러분/Noun', '스샷/Noun', '음악/Noun', '나쁘/Adjective', '불만/Noun', '블소/Noun', '재밌는/Adjective', '재밌어/Adjective', '기분/Noun', '운영/Noun', '제발/Noun', '소리/Noun', '남자/Noun', '쿠폰/Noun', '스토리/Noun', '이유/Noun', '오베/Noun', '근대/Noun', '도둑/Noun', '아키에이지/Noun', '가슴/Noun', '컴터/Noun', '캐릭터/Noun', '이야기/Noun', '인생/Noun', '리니지/Noun', '재미없/Adjective', '만세/Noun', '영자/Noun', '마음/Noun', '컴퓨터/Noun', '유저/Noun', '전투/Noun', '베타/Noun', '낫다/Noun', '뭐임/Noun', '타격/Noun', '스타일/Noun', '같다/Adjective', '우리나라/Noun', '군대/Noun', '만렙/Noun', '오픈베타/Noun', '유료/Noun', '풀옵/Noun', '차떄/Noun', '방식/Noun', '크라이엔진/Noun', '종족/Noun', '셔플/Noun', '재미/Noun', '안감/Noun', '한글/Noun', '피방/Noun', '아이온/Noun', '똑같/Adjective', '하셈/Noun', '스카이림/Noun', '그러니까/Adverb', '디아/Noun', '아디/Noun', '구려/Noun', '엑스/Noun', '만원/Noun', '패치/Noun', '차때/Noun', '학교/Noun', '개새끼/Noun', '여자/Noun', '아따/Noun', '오유/Noun', '쓰레기/Noun', '매우/Noun', '일본/Noun', '개념/Noun', '재밌다/Adjective', '축구/Noun', '닉네임/Noun', '아닙니/Adjective', '의미/Noun', '마비노기/Noun', '요즘/Noun', '결국/Adverb', '운영자/Noun', '보지/Noun', '배경/Noun', '얘기/Noun', '안함/Noun', '거임/Noun', '차가/Adjective', '드립/Noun', '중국/Noun', '따위/Noun', '냄새/Noun', '병신/Noun', '투자/Noun', '알바/Noun', '절도/Noun', '자체/Noun', '게이/Noun', '만하/Noun', '전설/Noun', '수준/Noun', '재밌네/Adjective', '온라인/Noun', '거지같/Adjective', '컨텐츠/Noun', '있냐/Adjective', '공부/Noun', '자연/Noun', '짱깨/Noun', '종범/Noun', '지젼/Noun', '목적/Noun', '현실/Noun', '최소/Noun', '계정/Noun', '홈피/Noun', '초딩/Noun', '에이지/Noun', '잘만/Noun', '예전/Noun', '최고/Noun', '진심/Noun', '호구/Noun', '근성/Noun', '지리/Noun', '마비/Noun', '도배/Noun', '마이/Noun', '판다/Noun', '타겟/Noun', '없냐/Adjective', '연장/Noun', '잉여/Noun', '외국인/Noun', '비교/Noun', '이래/Adjective', '오브/Noun', '여캐/Noun', '지겹/Adjective', '개객기/Noun', '아뒤/Noun', '리얼/Noun', '같으/Adjective', '메이플/Noun', '짱개/Noun', '이해/Noun', '프레임/Noun', '마영전/Noun', '맨날/Adverb', '진리/Noun', 'ㅉㅉ/KoreanParticle', '이면/Noun', '조용히/Adjective', '전장/Noun', '노트북/Noun', '징징/Noun', '아니냐/Adjective', '최하/Noun', '최적화/Noun', '당연히/Adjective', '덕후/Noun', '인기/Noun', '삭제/Noun', '웰컴/Noun', '레알/Noun', '그래픽카드/Noun', '목소리/Noun', '세상/Noun', '새키/Noun', '하드/Noun', '허허/Adverb', '서비스/Noun', '엔진/Noun', '동영상/Noun', '회사/Noun', '실제/Noun', '정지/Noun', '파워/Noun', '공짜/Noun', '모션/Noun', '같지/Adjective', '인대/Noun', '차단/Noun', '나라/Noun', '피시방/Noun', '지존/Noun', '리아/Noun', 'ㅗㅗ/KoreanParticle', '구라/Noun', '일베/Noun', '이드/Noun', '게시판/Noun', '전라도/Noun', '옛날/Noun', '폭팔/Noun', '투더/Noun', '다이쥬/Noun', '리그/Noun', '신섭/Noun', '드레이븐/Noun'], ['속도/Noun', '불꽃/Noun', '공격/Noun', '스킬/Noun', '자동/Noun', '순간이동/Noun', '대신/Noun', '기본/Noun', '버프/Noun', '평결/Noun', '지속/Noun', '상태/Noun', '정신/Noun', '심판/Noun', '의창/Noun', '사냥/Noun', '체력/Noun', '회복/Noun', '파괴/Noun', '자신/Noun', '빨라/Adjective', '까마귀/Noun', '기술/Noun', '돌격/Noun', '연속/Noun', '필수/Noun', '연주/Noun', '뎀지/Noun', '시브/Noun', '포인트/Noun', '그다음/Noun', '축복/Noun', '선택/Noun', '어떰/Adjective', '배움/Noun', '천이/Noun', '천옷/Noun', '대지/Noun', '지능/Noun', '악기/Noun', '경험치/Noun', '이속/Noun', '데미지/Noun', '증가/Noun', '효과/Noun', '트리/Noun', '시전/Noun', '캐스팅/Noun', '스턴/Noun', '공포/Noun', '몰이/Noun', '약한/Adjective', '사기/Noun', '치명/Noun', '콤보/Noun', '마상/Noun', '저주/Noun', '출혈/Noun', '평타/Noun', '단축/Noun', '차이/Noun', '연쇄/Noun', '기다림/Noun', '기운/Noun', '충격/Noun', '프로/Noun', '즉시/Noun', '살기/Noun', '생명력/Noun', '치유/Noun', '세번/Noun', '저항/Noun', '나머지/Noun', '회피/Noun', '감소/Noun', '공속/Noun', '사라/Noun', '강화/Noun', '무한/Noun', '방울방울/Noun', '사거리/Noun'], ['언니/Noun', '여기/Noun', '이제/Noun', '아까/Adjective', '요기/Noun', '바람/Noun', '길이/Noun', '오디/Noun', '고고/Noun', '그러/Adjective', 'ㅠㅠ/KoreanParticle', '마리/Noun', '요거/Noun', '방금/Noun', '완료/Noun', '아직/Noun', '아녀/Adjective', '항아리/Noun', '명령/Noun', 'ㅎㄷㄷ/KoreanParticle', '여요/Noun', '오메/Noun', '퀘템/Noun', '한참/Noun', '수고/Noun', '거만/Noun', '죄송/Noun', '무섭/Adjective', '하셧/Noun', '어요/Noun', '고하/Noun', 'ㅊㅋㅊㅋ/KoreanParticle', 'ㄷㄷ/KoreanParticle', '아뇨/Noun', '글쎄요/Noun', '진행/Noun', '다음/Noun', '대상/Noun', '고생/Noun', 'ㅡㅡㅋ/KoreanParticle', '고맙/Adjective', '래야/Noun', 'ㅜㅜ/KoreanParticle', '우왕/Noun', '감사용/Noun', '우두머리/Noun', '전이/Noun', '혼자/Noun', '네네/Noun', '다나/Noun', '얼른/Noun', '신비/Noun', '어여/Adverb', '잠깐/Noun', '즐겜/Noun', '이만/Noun', '구나/Noun', '저건/Noun', '죄송해/Adjective', '있느/Adjective', '이따/Noun', '초과/Noun', '실험/Noun', '기달/Noun', 'ㅜㅠ/KoreanParticle', '퀘완/Noun', '깜놀/Noun', '저쪽/Noun', '야하는/Adjective', '나용/Noun', '어어/Noun', '인제/Noun', '셧다/Noun', '쿠나/Noun', '아마도/Adverb', '글쿤/Noun', '다른/Adjective', '일루/Noun', '안햇/Noun', '반대/Noun', '화장실/Noun', '오키/Noun', '시나/Noun', '영혼/Noun', '애니/Noun', '담배/Noun', 'ㅎㅇㅇ/KoreanParticle', '보상/Noun', '추가/Noun', '재접/Noun', '전화/Noun', '오삼/Noun', '여서/Noun', '저녁/Noun', '덕분/Noun', '위로/Noun', '아영/Noun', '아우/Noun', '서여/Noun', '번은/Noun', '저가/Noun', '으잉/Noun', 'ㅃㅃ/KoreanParticle', '굿굿/Noun', '가염/Noun', 'ㅊㅊ/KoreanParticle', '고마워/Adjective', '드뎌/Noun', '악마/Noun', '언능/Adverb', '하피/Noun', '달리/Noun', '한판/Noun', '퀘좀/Noun', 'ㅠㅜ/KoreanParticle', '모하/Noun', '가세/Noun', '실수/Noun', '죽지/Noun', '오잉/Noun', '엉엉/Noun', '각자/Noun', '바이/Noun', '계세/Noun', 'ㅈㅅㅈㅅ/KoreanParticle', 'ㅇㅋㅇㅋ/KoreanParticle', '포기/Noun', '데리/Noun', '야해서/Adjective', '나가요/Noun', '빨간색/Noun', '오크/Noun', '가도/Noun', '누나/Noun', '라가/Noun', '마곡/Noun', 'ㅡㅜ/KoreanParticle', '부족하다/Adjective', '야하네/Adjective', '덴슬/Noun', '하셧어/Noun', '심장/Noun', '리스/Noun', '마녀/Noun', '저걸/Noun', '오니/Noun', '가바/Noun', '수거/Noun', '조기/Noun', '아퍼/Adjective', '지송/Noun', '귀속/Noun', '위고/Noun', '득템/Noun'], ['어디/Noun', '사자/Noun', '어떻/Adjective', '아이템/Noun', '씨앗/Noun', '어디서/Adverb', '아기/Noun', '우물/Noun', '어딧어/Noun', '귓속말/Noun', '언제/Noun', '사용/Noun', '어딧/Noun', '흰색/Noun', '퀘스트/Noun', '망토/Noun', '수리/Noun', '보리/Noun', '대체/Noun', '당나귀/Noun', '사나/Noun', '정보/Noun', '어떤/Adjective', 'ㄳㄳ/KoreanParticle', '안주/Noun', '사령관/Noun', '열매/Noun', '신관/Noun', '기계/Noun', '타레/Noun', '서사/Noun', '인어/Noun', '님들/Noun', '병아리/Noun', 'ㅎㅇ/KoreanParticle', '호수/Noun', '하나요/Noun', '어캐/Noun', '수확/Noun', '아시/Noun', '어딧음/Noun', '성장/Noun', '물이/Noun', '편지/Noun', '작은/Adjective', '늑대/Noun', '비밀/Noun', '왜안/Noun', '구함/Noun', '부품/Noun', '어딧는/Noun', '무타즈/Noun', '바꿈/Noun', '하만/Noun', '먹이/Noun', '검은/Adjective', '망아지/Noun', '엘크/Noun', '링크/Noun', 'ㅅㄱ/KoreanParticle', '탈것/Noun', '안나/Noun', '박하/Noun', '화제/Noun', '사슴/Noun', '오이/Noun', '감자/Noun', '전직/Noun', '레테/Noun', '어딧나/Noun', '도대체/Noun', '메인/Noun', '법좀/Noun', '협정/Noun', '투성이/Noun', '서해/Noun', '타요/Noun', '어찌/Noun', '대여/Noun', '탈출/Noun', '타래/Noun', '물건/Noun', '우편함/Noun', '보신/Noun', '자의/Noun', '늑대개/Noun', '우유/Noun', '닭고기/Noun', '망치/Noun', '인도/Noun', '볍씨/Noun', '인형/Noun'], ['골드/Noun', '목화/Noun', '팔고/Noun', '개인/Noun', '거래/Noun', '특산/Noun', '델피/Noun', '토끼풀/Noun', '필요없/Adjective', '사시/Noun', '주택/Noun', '개도/Noun', '우편/Noun', '실버/Noun', '몇개/Noun', '개요/Noun', '필요해/Adjective', '드림/Noun', '드릴/Noun', '창고/Noun', '넝마/Noun', '주괴/Noun', '통나무/Noun', '산호초/Noun', '철광석/Noun', '옥수수/Noun', '철괴/Noun', '살수/Noun', '기부/Noun', '부족해서/Adjective', '재료/Noun', '특급/Noun', '도면/Noun', '교역/Noun', '필요한/Adjective', '유물/Noun', '사면/Noun', '철거/Noun', '목재/Noun', '꾸러미/Noun', '도안/Noun', '철광/Noun', '물품/Noun', '암석/Noun', '재산/Noun', '필요하세/Adjective', '가공/Noun', '구입/Noun', '석재/Noun', '팔면/Noun', '초가집/Noun', '필요/Noun', '산다/Noun', '전복/Noun', '소형/Noun', '옷감/Noun', '안남/Adjective', '집도/Noun', '만듬/Noun', '사고/Noun', '부캐/Noun', '다만/Noun', '필요함/Adjective', '양식/Noun', '비싼/Adjective', '인양/Noun', '개정/Noun', '수선화/Noun', '철쭉/Noun', '달구지/Noun', '완성/Noun', '윤기/Noun', '철주/Noun', '강철/Noun', '필요하신/Adjective', '우체통/Noun', '루비/Noun', '가구/Noun', '캐러/Noun', '불가/Noun', '필요하다/Adjective', '부유/Noun', '채광/Noun', '광천수/Noun', '만들기/Noun', '연금/Noun', '불길/Noun', '장미/Noun', '부족하네/Adjective', '진주/Noun', '키조개/Noun', '양털/Noun', '집터/Noun', '껍질/Noun'], ['저번/Noun', '배고프/Adjective', '가장/Noun', '아니었/Adjective', '이상하게/Adjective', '용이/Noun', '처럼/Noun', '높아/Adjective', '중임/Noun', '기준/Noun', '수가/Noun', '일정/Noun', '그래서/Adverb', '이후/Noun', '훨씬/Adverb', '중요한/Adjective', '순간/Noun', '항상/Noun', '왠만하면/Adjective', '리가/Noun', '이럴/Adjective', '해도/Noun', '그림/Noun', '고요/Noun', '보통/Noun', '궁금한/Adjective', '으잌/Noun', '심하네/Adjective', '거리/Noun', '어때/Adjective', '그걸/Adverb', '전체/Noun', '더니/Noun', '랩업/Noun', '아주/Noun', '은근/Noun', '가끔/Noun', '같고/Adjective', '일부러/Noun', '영어/Noun', '무거/Noun', '상관없/Adjective', '닥사/Noun', '도끼/Noun', '거인/Noun', '오리/Noun', '오베때/Noun', '아웃/Noun', '이군/Noun', '그대로/Noun', '부자/Noun', '질문/Noun', '많은/Adjective', '경우/Noun', '효율/Noun', '주기/Noun', '생활/Noun', '괜히/Adverb', '슬프/Adjective', '일지/Noun', '노가다/Noun', '서울/Noun', '사진/Noun', '장사/Noun', '동네/Noun', '여태/Noun', '새로운/Adjective', '맘대로/Noun', '보이지/Noun', '난리/Noun', '자고/Noun', '느데/Noun', '취소/Noun', '점점/Noun', '큰일/Noun', '낮은/Adjective', '없더/Adjective', '차라리/Noun', '대부분/Noun', '정상/Noun', '죄다/Adverb', '참고/Noun', '그나마/Adverb', '많지/Adjective', '쓸모/Noun', '살짝/Noun', '이나/Noun', '차땐/Noun', '저렇게/Adverb', '하내/Noun', '그나저나/Adverb', '잔뜩/Noun', '저렙/Noun'], ['버그/Noun', '그냥/Noun', '일찍/Noun', '어쩐지/Noun', '시간/Noun', '미리/Noun', '렙업/Noun', '태풍/Noun', '나중/Noun', '하루/Noun', '그러면/Adverb', '그때/Noun', '동안/Noun', '거의/Noun', '장난/Noun', '대충/Noun', '도움/Noun', '새벽/Noun', '주말/Noun', '접속/Noun', '조금/Noun', '그것/Noun', '이번/Noun', '분위기/Noun', '열심히/Adverb', '기세/Noun', '다행/Noun', '이렇/Adjective', '시야/Noun', '아마/Noun', '가지/Noun', '즐거운/Adjective', '좀더/Noun', '기간/Noun', '복구/Noun', '인사/Noun', '시작/Noun', '아깝/Adjective', '마저/Noun', '접종/Noun', '튀어/Noun', '있고/Adjective', '던데/Noun', '쉬운/Adjective', '어제/Noun', '까지/Noun', '슬슬/Noun', '성공/Noun', '최대/Noun', '어짜피/Adverb', '자주/Noun', '아들/Noun', '기전/Noun', '하겟/Noun', '전부/Noun', '어차피/Noun', '실패/Noun', '일이/Noun', '라나/Noun', '라면/Noun', '최대한/Noun', '종일/Noun', '나가야/Noun', '겨우/Noun', 'ㅇㅅㅇ/KoreanParticle', '인원/Noun', '고렙/Noun', '빠를/Adjective', '암튼/Adverb', '이구/Noun', '관리/Noun', '걱정/Noun', '영상/Noun', '아님/Adjective', '없구/Adjective', '절대/Noun', '상황/Noun', '여행/Noun', '마여/Noun', '필요할/Adjective', '지지/Noun', '이대로/Adverb', '만해/Noun', '기회/Noun', '동생/Noun', '부족한/Adjective', '힐좀/Noun'], ['기사/Noun', '마법/Noun', '레벨/Noun', '법사/Noun', '암살자/Noun', '사명/Noun', '백마/Noun', '철벽/Noun', '격투/Noun', '추천/Noun', '계열/Noun', '죽음/Noun', '고민/Noun', '용병/Noun', '의지/Noun', '직업/Noun', '사랑/Noun', '흑마/Noun', '궁수/Noun', '마법사/Noun', '근접/Noun', '원거리/Noun', '전사/Noun', '강령/Noun', '술사/Noun', '능력/Noun', '낭만/Noun', '환술/Noun', '조합/Noun', '야성/Noun', '추적자/Noun', '검사/Noun', '정찰/Noun', '악사/Noun', '사가/Noun', '도적/Noun', '변경/Noun', '위주/Noun', '암살/Noun', '머머/Noun', '초심자/Noun', '그림자/Noun', '광전사/Noun', '백기/Noun', '저격자/Noun', '자객/Noun', '특성/Noun', '속박/Noun', '기적/Noun', '투사/Noun']]\n"
     ]
    }
   ],
   "source": [
    "print(clusterWordList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100022\n",
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      "1001\n",
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    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-260-d458c945e01d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     15\u001b[0m     \u001b[0mreal_vector\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muser_id\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0muser_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muser_cluster_word\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m     \u001b[0muser_index\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muser_id\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0muser_index\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "\n",
    "real_vector = []\n",
    "user_index = 0\n",
    "for user in wv_texts_ko:\n",
    "    user_cluster_word = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
    "    for word_for_user in user:\n",
    "        count = 0\n",
    "        for cl in clusterWordList:\n",
    "            if word_for_user in cl:\n",
    "                index = count\n",
    "                user_cluster_word[index] += 1\n",
    "                break\n",
    "            else:\n",
    "                count += 1\n",
    "    real_vector.append((user_id[user_index], user_cluster_word))\n",
    "    user_index += 1\n",
    "    print(user_id[user_index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4974"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(real_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_DF = pd.DataFrame.from_items(real_vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         0   1   2   3   4   5   6   7   8   9\n",
      "100016  33   9  46  76  10  29  37  36  11  23\n",
      "100022   1   6   7   5   9   0   7  26   2   1\n",
      "100079  12  21  29  24  17  67  54  37   5  33\n",
      "100086   2   4  13  12   0  34  10  31   1   7\n",
      "1001    37   2  11  17  38  70  51   9   9  10\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "print(real_DF.T.head())\n",
    "print(len(real_DF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_DF= real_DF.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27b05562898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ks = range(1, 20)\n",
    "inertias = []\n",
    "for k in ks:\n",
    "    model = KMeans(n_clusters=k)\n",
    "    model.fit(real_DF)\n",
    "    inertias.append(model.inertia_)\n",
    "\n",
    "matplotlib.rc('font', family=font_name, size=10)\n",
    "plt.plot(ks, inertias, '-o')\n",
    "plt.xlabel('number of clusters, k')\n",
    "plt.ylabel('inertia')\n",
    "plt.xticks(ks)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=4)\n",
    "\n",
    "kmeans = kmeans.fit(real_DF)\n",
    "\n",
    "labels = kmeans.predict(real_DF)\n",
    "\n",
    "centroids = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_DF['label'] = labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster 0 840\n",
      "cluster 1 2182\n",
      "cluster 2 779\n",
      "cluster 3 1173\n",
      "cluster 4 0\n"
     ]
    }
   ],
   "source": [
    "for i in range(0, 5):\n",
    "    print('cluster ' + str(i), len(real_DF.loc[real_DF['label']==int(i)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_DF.to_csv('w2v_upper200.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [],
   "source": [
    "kmeans = KMeans(n_clusters=4)\n",
    "\n",
    "kmeans = kmeans.fit(real_DF)\n",
    "\n",
    "labels = kmeans.predict(real_DF)\n",
    "\n",
    "centroids = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster 0 888\n",
      "cluster 1 754\n",
      "cluster 2 1162\n",
      "cluster 3 2170\n",
      "cluster 4 0\n"
     ]
    }
   ],
   "source": [
    "real_DF['label'] = labels\n",
    "for i in range(0, 5):\n",
    "    print('cluster ' + str(i), len(real_DF.loc[real_DF['label']==int(i)]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_DF['label'].to_csv('user_label.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: from_csv is deprecated. Please use read_csv(...) instead. Note that some of the default arguments are different, so please refer to the documentation for from_csv when changing your function calls\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "stat = pd.DataFrame.from_csv('cbt5_stat.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_DF.to_csv('cluster_word_per_user_word20.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         harve  comqu  crime  die  failq  guild  ind_i  jail  kicke  pvpdi  \\\n",
      "game_id                                                                      \n",
      "1001         4      1      0    0      0      0      1     0      0      0   \n",
      "1002         0      0      0    0      0      0      1     0      0      0   \n",
      "1003         0      0      0    0      0      0      0     0      0      0   \n",
      "1006         0      0      0    0      0      0      0     0      0      0   \n",
      "1007         1      0      0    0      0      0      1     0      0      0   \n",
      "\n",
      "          ...    playtime  maxlevel  logday  nick_len  lan  adjective  verb  \\\n",
      "game_id   ...                                                                 \n",
      "1001      ...      113936        50      13         3  kor          0     0   \n",
      "1002      ...       14803         1       6         4  kor          0     0   \n",
      "1003      ...        1232         1       6         5  kor          0     0   \n",
      "1006      ...        8644         1       2         4  kor          0     0   \n",
      "1007      ...        4628         1       4         5  kor          0     1   \n",
      "\n",
      "        total_pos  search_num  search  \n",
      "game_id                                \n",
      "1001            2           0      no  \n",
      "1002            2        6206      hi  \n",
      "1003            1          36      mi  \n",
      "1006            2        1983      hi  \n",
      "1007            3           1      sm  \n",
      "\n",
      "[5 rows x 23 columns]\n"
     ]
    }
   ],
   "source": [
    "print(stat.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(stat['die']['1001'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "stat[\"label\"] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         harve  comqu  crime  die  failq  guild  ind_i  jail  kicke  pvpdi  \\\n",
      "game_id                                                                      \n",
      "1001         4      1      0    0      0      0      1     0      0      0   \n",
      "1002         0      0      0    0      0      0      1     0      0      0   \n",
      "1003         0      0      0    0      0      0      0     0      0      0   \n",
      "1006         0      0      0    0      0      0      0     0      0      0   \n",
      "1007         1      0      0    0      0      0      1     0      0      0   \n",
      "\n",
      "         ...    maxlevel  logday  nick_len  lan  adjective  verb  total_pos  \\\n",
      "game_id  ...                                                                  \n",
      "1001     ...          50      13         3  kor          0     0          2   \n",
      "1002     ...           1       6         4  kor          0     0          2   \n",
      "1003     ...           1       6         5  kor          0     0          1   \n",
      "1006     ...           1       2         4  kor          0     0          2   \n",
      "1007     ...           1       4         5  kor          0     1          3   \n",
      "\n",
      "        search_num  search  label  \n",
      "game_id                            \n",
      "1001             0      no    NaN  \n",
      "1002          6206      hi    NaN  \n",
      "1003            36      mi    NaN  \n",
      "1006          1983      hi    NaN  \n",
      "1007             1      sm    NaN  \n",
      "\n",
      "[5 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "print(stat.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan\n"
     ]
    }
   ],
   "source": [
    "print(stat['label']['1001'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Int64Index([  1001,   1002,   1003,   1006,   1007,   1008,   1009,   1010,\n",
      "              1011,   1012,\n",
      "            ...\n",
      "            122517, 122518, 122520, 122522, 122528, 122529, 122530, 122531,\n",
      "            122532, 122533],\n",
      "           dtype='int64', name='game_id', length=39550)\n",
      "1001\n",
      "<class 'int'>\n",
      "1001\n",
      "<class 'int'>\n"
     ]
    }
   ],
   "source": [
    "print(stat.T.keys())\n",
    "for t in stat.T.keys():\n",
    "    print(t)\n",
    "    print(type(t))\n",
    "    break\n",
    "    \n",
    "for tt in real_DF.T.keys():\n",
    "    print(t)\n",
    "    print(type(t))\n",
    "    break\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "stat['label']['1002'] = real_DF['label']['1002']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'1003'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   2565\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2566\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mlibts\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value_box\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2567\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.get_value_box\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/tslib.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslib.get_value_box\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-184-e8d78af5b8fd>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreal_DF\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'label'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'1003'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    621\u001b[0m         \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    622\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 623\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    624\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    625\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   2572\u001b[0m                     \u001b[1;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2573\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2574\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2575\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pragma: no cover\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2576\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m   2558\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2559\u001b[0m             return self._engine.get_value(s, k,\n\u001b[1;32m-> 2560\u001b[1;33m                                           tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[0;32m   2561\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2562\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'integer'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'boolean'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: '1003'"
     ]
    }
   ],
   "source": [
    "print(real_DF['label']['1003'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         harve  comqu  crime  die  failq  guild  ind_i  jail  kicke  pvpdi  \\\n",
      "game_id                                                                      \n",
      "1001         4      1      0    0      0      0      1     0      0      0   \n",
      "1002         0      0      0    0      0      0      1     0      0      0   \n",
      "1003         0      0      0    0      0      0      0     0      0      0   \n",
      "1006         0      0      0    0      0      0      0     0      0      0   \n",
      "1007         1      0      0    0      0      0      1     0      0      0   \n",
      "\n",
      "         ...    maxlevel  logday  nick_len  lan  adjective  verb  total_pos  \\\n",
      "game_id  ...                                                                  \n",
      "1001     ...          50      13         3  kor          0     0          2   \n",
      "1002     ...           1       6         4  kor          0     0          2   \n",
      "1003     ...           1       6         5  kor          0     0          1   \n",
      "1006     ...           1       2         4  kor          0     0          2   \n",
      "1007     ...           1       4         5  kor          0     1          3   \n",
      "\n",
      "        search_num  search  label  \n",
      "game_id                            \n",
      "1001             0      no    1.0  \n",
      "1002          6206      hi    3.0  \n",
      "1003            36      mi    NaN  \n",
      "1006          1983      hi    NaN  \n",
      "1007             1      sm    NaN  \n",
      "\n",
      "[5 rows x 24 columns]\n"
     ]
    }
   ],
   "source": [
    "print(stat.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "for key in real_DF.T.keys():\n",
    "    stat['label'][str(key)] = real_DF['label'][str(key)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['얼마/Noun', '하나/Noun', '골드/Noun', '있으/Adjective', '경매/Noun', '목화/Noun', '팔고/Noun', '개인/Noun', '거래/Noun', '얼마나/Noun', '특산/Noun', '델피/Noun', '눈물/Noun', '토끼풀/Noun', '필요없/Adjective', '햇빛/Noun', '가루/Noun', '시세/Noun', '정도/Noun', '가죽/Noun', '로이스터/Noun', '달빛/Noun', '우편/Noun', '실버/Noun', '몇개/Noun', '개요/Noun', '필요해/Adjective', '결정/Noun', '드림/Noun', '사실/Noun', '에서/Noun', '드릴/Noun', '창고/Noun', '주거지역/Noun', '넝마/Noun', '별빛/Noun', '지구/Noun', '주괴/Noun', '통나무/Noun', '산호초/Noun', '철광석/Noun', '옥수수/Noun', '잡화상/Noun', '가격/Noun', '조각/Noun', '신분/Noun', '살수/Noun', '주시/Noun', '벼락/Noun', '광산/Noun', '가능/Noun', '일반/Noun', '재료/Noun', '도면/Noun', '교역/Noun', '필요한/Adjective', 'ㅅㅅ/KoreanParticle', '사면/Noun', '구리/Noun', '노동/Noun', '광석/Noun', '목재/Noun', '꾸러미/Noun', '오리/Noun', '높은/Adjective', '상점/Noun', '도안/Noun', '분해/Noun', '철광/Noun', '당근/Noun', '암석/Noun', '숙련/Noun', '제작/Noun', '가공/Noun', '석재/Noun', '팔면/Noun', '초가집/Noun', '필요/Noun', '소형/Noun', '수정/Noun', '슬프/Adjective', '비싸/Adjective', '옷감/Noun', 'ㅍㅍ/KoreanParticle', '안남/Adjective', '집도/Noun', '종류/Noun', '필요하/Adjective', '만듬/Noun', '사고/Noun', '자작나무/Noun', '필요함/Adjective', '업글/Noun', '양식/Noun', '암튼/Adverb', '물건/Noun', '비싼/Adjective', '고급/Noun', '가능해/Adjective', '귓주세/Noun', '철쭉/Noun', '달구지/Noun', '교환/Noun', '철주/Noun', '광맥/Noun', '야영지/Noun', '가구/Noun', '검색/Noun', '부유/Noun', '채광/Noun', '정수/Noun', '정제/Noun']\n"
     ]
    }
   ],
   "source": [
    "print(clusterWordList[9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('원딜/Noun', 0.7735545039176941),\n",
       " ('팟점/Noun', 0.7234616279602051),\n",
       " ('팟좀/Noun', 0.7062204480171204),\n",
       " ('도실/Noun', 0.7048341631889343),\n",
       " ('왕국/Noun', 0.6998692154884338),\n",
       " ('서방/Noun', 0.6980137825012207),\n",
       " ('파탈/Noun', 0.6938217878341675),\n",
       " ('불탄/Adjective', 0.6916957497596741),\n",
       " ('이분/Noun', 0.6882418990135193),\n",
       " ('딜러/Noun', 0.6835995316505432)]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv_model_ko.most_similar(['파티/Noun'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('동생/Noun', 0.7154369950294495),\n",
       " ('피시방/Noun', 0.7073309421539307),\n",
       " ('미안해/Adjective', 0.701209306716919),\n",
       " ('애기/Noun', 0.6903117299079895),\n",
       " ('나이/Noun', 0.6881504058837891),\n",
       " ('인사/Noun', 0.6816860437393188),\n",
       " ('목소리/Noun', 0.6811408996582031),\n",
       " ('아프리카/Noun', 0.6724939942359924),\n",
       " ('친추/Noun', 0.6703541278839111),\n",
       " ('안녕/Noun', 0.6544557213783264)]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv_model_ko.most_similar(['친구/Noun'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('인팟/Noun', 0.8768270015716553),\n",
       " ('도실/Noun', 0.8643829822540283),\n",
       " ('막넴/Noun', 0.8527936935424805),\n",
       " ('팟구해/Noun', 0.8490995168685913),\n",
       " ('막보/Noun', 0.8402490615844727),\n",
       " ('클리/Noun', 0.8368479609489441),\n",
       " ('딜러/Noun', 0.8247366547584534),\n",
       " ('공략/Noun', 0.8234536051750183),\n",
       " ('팟점/Noun', 0.8190087080001831),\n",
       " ('왕국/Noun', 0.8171093463897705)]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv_model_ko.most_similar(['던전/Noun'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('원정/Noun', 0.8142492771148682),\n",
       " ('끼리/Noun', 0.7951613664627075),\n",
       " ('아르/Noun', 0.7939311265945435),\n",
       " ('부대/Noun', 0.7855406403541565),\n",
       " ('카페/Noun', 0.77524334192276),\n",
       " ('동맹/Noun', 0.7646075487136841),\n",
       " ('드라이어/Noun', 0.758230984210968),\n",
       " ('아프리카/Noun', 0.7496179342269897),\n",
       " ('길마/Noun', 0.7473348379135132),\n",
       " ('맨더/Noun', 0.7347431778907776)]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv_model_ko.most_similar(['길드/Noun'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('새끼/Noun', 0.8939669132232666),\n",
       " ('씨발/Noun', 0.8902525901794434),\n",
       " ('ㅁㅊ/KoreanParticle', 0.8585203886032104),\n",
       " ('개새끼/Noun', 0.8521537184715271),\n",
       " ('ㅅㅂ/KoreanParticle', 0.844786524772644),\n",
       " ('미친/Adjective', 0.8443993330001831),\n",
       " ('시바/Noun', 0.8383446931838989),\n",
       " ('존나/Noun', 0.8278866410255432),\n",
       " ('있냐/Adjective', 0.7704236507415771),\n",
       " ('마라/Adjective', 0.7697054147720337)]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv_model_ko.most_similar(['시발/Noun'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('교환/Noun', 0.7323195934295654),\n",
       " ('우편/Noun', 0.7291386127471924),\n",
       " ('드릴/Noun', 0.7271155714988708),\n",
       " ('불가/Noun', 0.7064212560653687),\n",
       " ('특급/Noun', 0.7056891918182373),\n",
       " ('집도/Noun', 0.7033056616783142),\n",
       " ('필요하신/Adjective', 0.7008700966835022),\n",
       " ('필요하세/Adjective', 0.6878462433815002),\n",
       " ('철주/Noun', 0.6737403273582458),\n",
       " ('기부/Noun', 0.6708500981330872)]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv_model_ko.most_similar(['거래/Noun'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('발자국/Noun', 0.8227037191390991),\n",
       " ('수배/Noun', 0.7772793769836426),\n",
       " ('서리/Noun', 0.7279030680656433),\n",
       " ('현상/Noun', 0.723227858543396),\n",
       " ('감옥/Noun', 0.6962117552757263),\n",
       " ('당했/Adjective', 0.6737354397773743),\n",
       " ('당함/Adjective', 0.6553884744644165),\n",
       " ('불명예/Noun', 0.6438547372817993),\n",
       " ('다른사람/Noun', 0.6389830112457275),\n",
       " ('작물/Noun', 0.6228328347206116)]"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv_model_ko.most_similar(['신고/Noun'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('사용/Noun', 0.728905439376831),\n",
       " ('링크/Noun', 0.7239853143692017),\n",
       " ('분해/Noun', 0.7130257487297058),\n",
       " ('기능/Noun', 0.7046092748641968),\n",
       " ('신발/Noun', 0.6864684224128723),\n",
       " ('착용/Noun', 0.6503843069076538),\n",
       " ('장비/Noun', 0.637758195400238),\n",
       " ('일반/Noun', 0.6235982179641724),\n",
       " ('망치/Noun', 0.6180912852287292),\n",
       " ('구도/Noun', 0.614588737487793)]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv_model_ko.most_similar(['아이템/Noun'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: DeprecationWarning: Call to deprecated `most_similar` (Method will be removed in 4.0.0, use self.wv.most_similar() instead).\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[('공대/Noun', 0.84939044713974),\n",
       " ('이분/Noun', 0.7340853810310364),\n",
       " ('부탁드려/Adjective', 0.715470016002655),\n",
       " ('탈퇴/Noun', 0.7029819488525391),\n",
       " ('파초/Noun', 0.6528900265693665),\n",
       " ('부탁드립니/Adjective', 0.646327793598175),\n",
       " ('해체/Noun', 0.6421756744384766),\n",
       " ('파티/Noun', 0.6286109089851379),\n",
       " ('초대/Noun', 0.6086872220039368),\n",
       " ('사령관/Noun', 0.6041877269744873)]"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wv_model_ko.most_similar(['공격대/Noun'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: from_csv is deprecated. Please use read_csv(...) instead. Note that some of the default arguments are different, so please refer to the documentation for from_csv when changing your function calls\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "labelperuser = pd.DataFrame.from_csv('cbt5_corpus_withlabel/cbt5_corpus_withlabel.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                    corpus label\n",
      "game_id                                                         \n",
      "1001     놓치는 시기 시기 야할 계신 계신 사태 교훈 왕관 오래 오래 오래 오래 회수 회수 ...     a\n",
      "10014    인생 인생 있다 같은 랩때 날틀 날틀 싶은 지랄 지랄 지랄 지랄 쉬발 당연한 열매 ...     f\n",
      "10037    렉쩌는데 문제 추자 춤좀 김정 겜톡 겜톡 겜톡 겜톡 겜톡 안되는 셔플 셔플 셔플 쩐...     f\n",
      "10185    나뭇가지 어렵 제발 몽상 몽상 몽상 사서 상어 상어 구하는 구하는 하다 죽엿는데 죽...     a\n",
      "10230    찾기 ㅇㅋㅇㅋ 나지 어딧는 잠긴 업데이트 파티 좀덜 마우스 마우스 마우스 마우스 개...     f\n"
     ]
    }
   ],
   "source": [
    "print(labelperuser.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 299,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelperuser = labelperuser.drop(columns=['corpus'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        real_label\n",
      "game_id           \n",
      "1001             a\n",
      "10014            f\n",
      "10037            f\n",
      "10185            a\n",
      "10230            f\n"
     ]
    }
   ],
   "source": [
    "print(labelperuser.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [],
   "source": [
    "labelperuser.columns = ['real_label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'game_id' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-309-fb837bad2fe6>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[0mreal_DF\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'label'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mgame_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabeling\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'game_id' is not defined"
     ]
    }
   ],
   "source": [
    "#labels[game_id] = label\n",
    "\n",
    "#def labelig(game_id):\n",
    "#    return labels[game_id]\n",
    "\n",
    "\n",
    "#real_DF['label'] = labels[game_id].apply(labeling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>game_id</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <td>3</td>\n",
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       "      <th>100022</th>\n",
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       "      <th>1001</th>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          0  label\n",
       "game_id           \n",
       "100016   33      3\n",
       "100022    1      1\n",
       "100079   12      2\n",
       "100086    2      1\n",
       "1001     37      2"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = real_DF[[0,'label']]\n",
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind = list(temp.index)\n",
    "a = []\n",
    "for i in ind:\n",
    "    a.append(int(i))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\KeenCat\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "temp['game_id2'] = a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = temp.set_index('game_id2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "he = labelperuser.join(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "        real_label   0  label\n",
       "game_id                      \n",
       "1001             a  37      2\n",
       "10014            f   2      1\n",
       "10037            f   2      1\n",
       "10185            a  56      3\n",
       "10230            f  11      1"
      ]
     },
     "execution_count": 337,
     "metadata": {},
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    }
   ],
   "source": [
    "he.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_DF.index.name = 'game_id'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "metadata": {},
   "outputs": [
    {
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       "        real_label\n",
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       "1001             a\n",
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    "labelperuser.head()"
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   "execution_count": 349,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "  real_label  label  counts\n",
       "0          a      0      54\n",
       "1          a      1      19\n",
       "2          a      2      60\n",
       "3          a      3     368\n",
       "4          f      0      82\n",
       "5          f      1     424\n",
       "6          f      2      13\n",
       "7          f      3      21"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "he.groupby(['real_label','label']).size().reset_index(name='counts')"
   ]
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  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>8954</th>\n",
       "      <td>f</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9071</th>\n",
       "      <td>f</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9093</th>\n",
       "      <td>f</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9136</th>\n",
       "      <td>f</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9159</th>\n",
       "      <td>f</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9226</th>\n",
       "      <td>f</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9253</th>\n",
       "      <td>f</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9319</th>\n",
       "      <td>f</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9446</th>\n",
       "      <td>f</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9457</th>\n",
       "      <td>f</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9506</th>\n",
       "      <td>f</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9570</th>\n",
       "      <td>f</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9600</th>\n",
       "      <td>f</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9642</th>\n",
       "      <td>f</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9763</th>\n",
       "      <td>f</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9957</th>\n",
       "      <td>f</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>424 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        real_label   0  label\n",
       "game_id                      \n",
       "10014            f   2      1\n",
       "10037            f   2      1\n",
       "10230            f  11      1\n",
       "10231            f   6      1\n",
       "10412            f   7      1\n",
       "10774            f  28      1\n",
       "11119            f  29      1\n",
       "1138             f   6      1\n",
       "11451            f   3      1\n",
       "11626            f   5      1\n",
       "11743            f   3      1\n",
       "11748            f  24      1\n",
       "1200             f  37      1\n",
       "12159            f   1      1\n",
       "1216             f   8      1\n",
       "12213            f   9      1\n",
       "1223             f   8      1\n",
       "12326            f   4      1\n",
       "12445            f   4      1\n",
       "12579            f  12      1\n",
       "1263             f  11      1\n",
       "12796            f  22      1\n",
       "12837            f   4      1\n",
       "1287             f   6      1\n",
       "1288             f  12      1\n",
       "12986            f  12      1\n",
       "13037            f   4      1\n",
       "13130            f  15      1\n",
       "1329             f  10      1\n",
       "13364            f   8      1\n",
       "...            ...  ..    ...\n",
       "7979             f  23      1\n",
       "8002             f   7      1\n",
       "8058             f  20      1\n",
       "8074             f   2      1\n",
       "8084             f   6      1\n",
       "8155             f   1      1\n",
       "8206             f  13      1\n",
       "8507             f   3      1\n",
       "8572             f   1      1\n",
       "8601             f   7      1\n",
       "8694             f   6      1\n",
       "8718             f   1      1\n",
       "8867             f   3      1\n",
       "8895             f   5      1\n",
       "8954             f   5      1\n",
       "9071             f   8      1\n",
       "9093             f   9      1\n",
       "9136             f  12      1\n",
       "9159             f  36      1\n",
       "9226             f   2      1\n",
       "9253             f   3      1\n",
       "9319             f   6      1\n",
       "9446             f   3      1\n",
       "9457             f  10      1\n",
       "9506             f   4      1\n",
       "9570             f   5      1\n",
       "9600             f   8      1\n",
       "9642             f   0      1\n",
       "9763             f   7      1\n",
       "9957             f   9      1\n",
       "\n",
       "[424 rows x 3 columns]"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "he.loc[(he['real_label']=='f') &( he['label'] == 1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "he"
   ]
  }
 ],
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