pip install pyLDAvis<\/code><\/pre>\n3.2 LDA \ubaa8\ub378 \uad6c\ucd95\ud558\uae30<\/h3>\n
\n LDA \ubaa8\ub378\uc744 \uad6c\uc131\ud558\uae30 \uc704\ud574 \uc801\uc808\ud55c \ub370\uc774\ud130 \uc138\ud2b8\ub97c \uc900\ube44\ud558\uace0, \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \uac70\uccd0\uc57c \ud569\ub2c8\ub2e4.
\n \uc774 \uacfc\uc815\uc5d0\ub294 \ud14d\uc2a4\ud2b8 \uc815\ub9ac, \ud1a0\ud070\ud654(tokenization), \ubd88\uc6a9\uc5b4 \uc81c\uac70 \ub4f1\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4.\n<\/p>\n
\nimport pandas as pd\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom gensim import corpora\nfrom gensim.models import LdaModel\n\n# \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\ndata = pd.read_csv('financial_data.csv')\n\n# \ud14d\uc2a4\ud2b8 \uc804\ucc98\ub9ac\ndata['cleaned_text'] = data['text'].apply(clean_text_function)\n\n# \ucf54\ud37c\uc2a4\uc640 \uc0ac\uc804 \uc0dd\uc131\nvectorizer = CountVectorizer()\nX = vectorizer.fit_transform(data['cleaned_text'])\n\n# LDA \ubaa8\ub378 \ud6c8\ub828\nlda_model = LdaModel(corpus=corpora.Dictionary(X.toarray()), num_topics=5, id2word=vectorizer.get_feature_names_out())\n<\/code><\/pre>\n3.3 LDA \uacb0\uacfc \uc2dc\uac01\ud654\ud558\uae30<\/h3>\n
\n \ud559\uc2b5\ub41c LDA \ubaa8\ub378 \uacb0\uacfc\ub97c pyLDAvis\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2dc\uac01\ud654\ud569\ub2c8\ub2e4. \uc774 \ub2e8\uacc4\uc5d0\uc11c\ub294 \uc8fc\uc81c \uac04\uc758 \uad00\uacc4\ub97c \uc2dc\uac01\uc801\uc73c\ub85c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
\nimport pyLDAvis\nimport pyLDAvis.gensim_models as gensimvis\n\n# \uc2dc\uac01\ud654\nvis = gensimvis.prepare(lda_model, corpus, dictionary)\npyLDAvis.show(vis)\n<\/code><\/pre>\n4. \uae30\ud0c0 \ud65c\uc6a9 \ubc29\uc548<\/h2>\n
\n LDA \ubaa8\ub378\uc740 \ub2e8\uc21c\ud788 \uc8fc\uc81c\ub97c \ucd94\ucd9c\ud558\ub294 \uac83\uc5d0 \uadf8\uce58\uc9c0 \uc54a\uace0, \ub354 \ub098\uc544\uac00 \ud22c\uc790 \uc804\ub7b5\uc5d0\ub3c4 \ud1b5\ud569\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.
\n \uc608\ub97c \ub4e4\uc5b4, \ud2b9\uc815 \uc8fc\uc81c\uc758 \uae30\uc0ac\uac00 \uc99d\uac00\ud558\uac70\ub098 \uac10\uc18c\ud558\ub294 \ucd94\uc138\ub97c \uac10\uc9c0\ud568\uc73c\ub85c\uc368 \ud2b9\uc815 \uc790\uc0b0\uc5d0 \ub300\ud55c \ud22c\uc790 \uacb0\uc815\uc744 \ub0b4\ub9b4 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
5. \uacb0\ub860<\/h2>\n
\n \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 \ub354\uc6b1 \uc815\uad50\ud558\uace0 \ud6a8\uc728\uc801\uc778 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \ub9cc\ub4dc\ub294 \ub370 \ub3c4\uc6c0\uc744 \uc90d\ub2c8\ub2e4.
\n LDA\uc640 \uac19\uc740 \uc8fc\uc81c \ubaa8\ub378\ub9c1 \uae30\ubc95\uc744 \ud65c\uc6a9\ud558\uc5ec \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0, pyLDAvis\ub97c \ud1b5\ud574 \uacb0\uacfc\ub97c \uc2dc\uac01\ud654\ud568\uc73c\ub85c\uc368
\n \uc778\uc0ac\uc774\ud2b8\ub97c \ub3c4\ucd9c\ud574 \ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n<\/p>\n
\n \ubcf8 \uac15\uc88c\ub97c \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc5d0 \ub300\ud55c \uc774\ud574\ub97c \ub192\uc774\uace0,
\n \uc2e4\uc81c \ub370\uc774\ud130\uc5d0 \uc801\uc6a9\ud574 \ubcf4\ub294 \ub370 \ub3c4\uc6c0\uc744 \ubc1b\uc744 \uc218 \uc788\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.\n<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\uae08\uc735 \uc2dc\uc7a5\uc740 \ubcf5\uc7a1\ud558\uace0 \ubcc0\ub3d9\uc131\uc774 \ud070 \ud658\uacbd\uc73c\ub85c, \uc774\uacf3\uc5d0\uc11c \uc131\uacf5\uc801\uc778 \ud2b8\ub808\uc774\ub529\uc744 \uc704\ud574\uc11c\ub294 \uc2e0\uc18d\ud558\uace0 \uc815\ud655\ud55c \uc758\uc0ac\uacb0\uc815\uc774 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 \uc774\ub7ec\ud55c \ubb38\uc81c\ub97c \ud574\uacb0\ud558\uae30 \uc704\ud55c \uac15\ub825\ud55c \ub3c4\uad6c\ub85c \uc790\ub9ac\uc7a1\uc600\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 LDA(Latent Dirichlet Allocation) \ubaa8\ub378\uc744 \ud1b5\ud574 \uae08\uc735 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0, \uc774\ub97c pyLDAvis \ud328\ud0a4\uc9c0\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc2dc\uac01\ud654\ud558\ub294 \ubc29\ubc95\uc744 \uc790\uc138\ud788 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd \uae30\ucd08 \uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud328\ud134\uc744 \ud559\uc2b5\ud558\uc5ec \uc608\uce21, \ubd84\ub958, \uad70\uc9d1\ud654 \ub4f1\uc758 \uc791\uc5c5\uc744 \uc218\ud589\ud558\ub294 … \ub354 \ubcf4\uae30 “\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, pyLDAvis\ub97c \uc774\uc6a9\ud55c LDA \uacb0\uacfc\uc758 \uc2dc\uac01\ud654”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29],"tags":[],"class_list":["post-28714","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"\n\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, pyLDAvis\ub97c \uc774\uc6a9\ud55c LDA \uacb0\uacfc\uc758 \uc2dc\uac01\ud654 - 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