\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac: \ub124\uc774\ubc84 \uc601\ud654 \ub9ac\ubdf0 \uac10\uc131 \ubd84\ub958\ud558\uae30 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/atmokpo.com\/w\/25327\/\" \/>\n<meta property=\"og:locale\" content=\"ko_KR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac: \ub124\uc774\ubc84 \uc601\ud654 \ub9ac\ubdf0 \uac10\uc131 \ubd84\ub958\ud558\uae30 - \ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"og:description\" content=\"\uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub294 \ucef4\ud4e8\ud130\uac00 \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uae30\uc220\ub85c, \ucd5c\uadfc \ub525 \ub7ec\ub2dd\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \ub9ce\uc740 \ud601\uc2e0\uc744 \uc774\ub8e8\uc5b4\ub0c8\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \ud55c \uc608\ub85c, \ub124\uc774\ubc84 \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\uc14b\uc744 \ud65c\uc6a9\ud558\uc5ec \uc601\ud654 \ub9ac\ubdf0\uc758 \uac10\uc131\uc744 \ubd84\ub958\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \uac1c\uc694 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP, Natural Language Processing)\ub294 \ucef4\ud4e8\ud130 \uacfc\ud559\uacfc \uc5b8\uc5b4\ud559\uc758 \uc735\ud569 \ubd84\uc57c\ub85c, \uc778\uac04\uc758 … \ub354 \ubcf4\uae30 "\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac: \ub124\uc774\ubc84 \uc601\ud654 \ub9ac\ubdf0 \uac10\uc131 \ubd84\ub958\ud558\uae30"\" \/>\n<meta property=\"og:url\" content=\"https:\/\/atmokpo.com\/w\/25327\/\" \/>\n<meta property=\"og:site_name\" content=\"\ub77c\uc774\ube0c\uc2a4\ub9c8\ud2b8\" \/>\n<meta property=\"article:published_time\" content=\"2024-10-26T09:54:06+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-11-26T08:02:09+00:00\" \/>\n<meta name=\"author\" content=\"root\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:site\" content=\"@bebubo4\" \/>\n<meta name=\"twitter:label1\" content=\"\uae00\uc4f4\uc774\" \/>\n\t<meta name=\"twitter:data1\" content=\"root\" \/>\n\t<meta name=\"twitter:label2\" content=\"\uc608\uc0c1 \ub418\ub294 \ud310\ub3c5 \uc2dc\uac04\" \/>\n\t<meta name=\"twitter:data2\" content=\"1\ubd84\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/atmokpo.com\/w\/25327\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/atmokpo.com\/w\/25327\/\"},\"author\":{\"name\":\"root\",\"@id\":\"https:\/\/atmokpo.com\/w\/#\/schema\/person\/91b6b3b138fbba0efb4ae64b1abd81d7\"},\"headline\":\"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac: \ub124\uc774\ubc84 \uc601\ud654 \ub9ac\ubdf0 \uac10\uc131 \ubd84\ub958\ud558\uae30\",\"datePublished\":\"2024-10-26T09:54:06+00:00\",\"dateModified\":\"2024-11-26T08:02:09+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/atmokpo.com\/w\/25327\/\"},\"wordCount\":33,\"publisher\":{\"@id\":\"https:\/\/atmokpo.com\/w\/#organization\"},\"articleSection\":[\"\ub525\ub7ec\ub2dd \uc790\uc5f0\uc5b4\ucc98\ub9ac\"],\"inLanguage\":\"ko-KR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/atmokpo.com\/w\/25327\/\",\"url\":\"https:\/\/atmokpo.com\/w\/25327\/\",\"name\":\"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac: \ub124\uc774\ubc84 \uc601\ud654 \ub9ac\ubdf0 \uac10\uc131 \ubd84\ub958\ud558\uae30 - 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<\/p>\n

\uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub294 \ucef4\ud4e8\ud130\uac00 \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uae30\uc220\ub85c, \ucd5c\uadfc \ub525 \ub7ec\ub2dd\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \ub9ce\uc740 \ud601\uc2e0\uc744 \uc774\ub8e8\uc5b4\ub0c8\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \ud55c \uc608\ub85c, \ub124\uc774\ubc84 \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\uc14b\uc744 \ud65c\uc6a9\ud558\uc5ec \uc601\ud654 \ub9ac\ubdf0\uc758 \uac10\uc131\uc744 \ubd84\ub958\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. <\/p>\n

1. \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \uac1c\uc694<\/h2>\n

\uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP, Natural Language Processing)\ub294 \ucef4\ud4e8\ud130 \uacfc\ud559\uacfc \uc5b8\uc5b4\ud559\uc758 \uc735\ud569 \ubd84\uc57c\ub85c, \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ud574\uc11d\ud558\uc5ec \ucef4\ud4e8\ud130\uc5d0\uc11c \uc790\uc5f0\uc5b4\uc758 \uc758\ubbf8\ub97c \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. NLP\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uc5ec\ub7ec \ub2e8\uacc4\ub85c \uad6c\ubd84\ub429\ub2c8\ub2e4:<\/p>\n

\ud1a0\ud070\ud654(Tokenization):<\/strong> \ubb38\uc7a5\uc744 \ub2e8\uc5b4 \ub610\ub294 \ubb38\uc7a5\uc73c\ub85c \ubd84\ub9ac\ud558\ub294 \uacfc\uc815<\/li>\n
\uc5b4\uac04 \ucd94\ucd9c(Stemming) \ubc0f \ud45c\uc81c\uc5b4 \ucd94\ucd9c(Lemmatization):<\/strong> \ub2e8\uc5b4\uc758 \uae30\ubcf8\ud615\uc744 \ucc3e\uc544\ub0b4\ub294 \uacfc\uc815<\/li>\n
\ud488\uc0ac \ud0dc\uae45(POS tagging):<\/strong> \uac01 \ub2e8\uc5b4\uc758 \ud488\uc0ac\ub97c \uc2dd\ubcc4\ud558\ub294 \uacfc\uc815<\/li>\n

\ubb38\ub9e5 \uc774\ud574:<\/strong> \ubb38\uc7a5\uc758 \uc758\ubbf8\uc640 \ubb38\ubc95\uc801 \uad6c\uc870\ub97c \uc774\ud574\ud558\ub294 \uacfc\uc815<\/li>\n<\/ul>\n
2. \ub525 \ub7ec\ub2dd\uc744 \ud1b5\ud55c \uac10\uc131 \ubd84\uc11d<\/h2>\n
\uac10\uc131 \ubd84\uc11d(Sentiment Analysis)\uc740 \ud14d\uc2a4\ud2b8\uc5d0\uc11c \uac10\uc815\uc744 \ucd94\ucd9c\ud558\uace0 \ubd84\ub958\ud558\ub294 \uae30\uc220\ub85c, \uae0d\uc815, \ubd80\uc815, \uc911\ub9bd \ub4f1\uc758 \uac10\uc815\uc744 \ubd84\ub958\ud558\ub294 \uac83\uc744 \ubaa9\ud45c\ub85c \ud569\ub2c8\ub2e4. \ub525 \ub7ec\ub2dd \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uba74 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub300\ud45c\uc801\uc778 \ubaa8\ub378\ub85c\ub294 LSTM(Long Short-Term Memory), RNN(Recurrent Neural Networks), CNN(Convolutional Neural Networks) \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3. \ub124\uc774\ubc84 \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\uc14b \uc18c\uac1c<\/h2>\n
\ub124\uc774\ubc84 \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\uc14b\uc740 \uc601\ud654\uc5d0 \ub300\ud55c \ub9ac\ubdf0\ub97c \uc218\uc9d1\ud55c \ub370\uc774\ud130\uc14b\uc73c\ub85c, \uac01 \ub9ac\ubdf0\ub294 \uae0d\uc815\uc801 \ub610\ub294 \ubd80\uc815\uc801 \uac10\uc131\uc744 \ud3ec\ud568\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\uc14b\uc740 \uac10\uc131 \ubd84\uc11d \ubaa8\ub378 \ud559\uc2b5\uc5d0 \uc788\uc5b4 \ud6cc\ub96d\ud55c \uc790\ub8cc\uac00 \ub429\ub2c8\ub2e4. \ub370\uc774\ud130\uc14b\uc758 \ud2b9\uc9d5\uacfc \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\n
\ub370\uc774\ud130 \uad6c\uc870:<\/strong> \ub9ac\ubdf0 \ub0b4\uc6a9\uacfc \ud574\ub2f9 \ub9ac\ubdf0\uc758 \uac10\uc131\uc774 \ub808\uc774\ube14\ub85c \ubd99\uc5b4 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
\ub370\uc774\ud130 \uc804\ucc98\ub9ac:<\/strong> \ubb38\uc790\uc5f4 \ucc98\ub9ac \ubc0f \ubd88\uc6a9\uc5b4 \uc81c\uac70 \ub4f1\uc758 \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \uac70\uccd0\uc57c \ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
4. \ud658\uacbd \uc124\uc815 \ubc0f \uc758\uc874\uc131<\/h2>\n
\ubcf8 \uac15\uc88c\ub97c \uc9c4\ud589\ud558\uae30 \uc704\ud574 \uc544\ub798\uc640 \uac19\uc740 \ub77c\uc774\ube0c\ub7ec\ub9ac\uc640 \ud234\uc744 \uc124\uce58\ud574\uc57c \ud569\ub2c8\ub2e4:<\/p>\n
!pip install numpy pandas matplotlib seaborn tensorflow keras nltk<\/code><\/pre>\n5. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n\ubaa8\ub378 \ud559\uc2b5 \uc804\uc5d0 \ub370\uc774\ud130 \uc804\ucc98\ub9ac \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ub370\uc774\ud130\uc758 \ud488\uc9c8\uc744 \ud5a5\uc0c1\uc2dc\ud0a4\uace0 \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \uac1c\uc120\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n import pandas as pd\n\n# \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\ndata = pd.read_csv('naver_movie_reviews.csv')\n\n# \uacb0\uce21\uce58 \uc81c\uac70\ndata.dropna(inplace=True)\n\n# \ud14d\uc2a4\ud2b8 \uc815\uc81c \ud568\uc218 \uc815\uc758\ndef clean_text(text):\n # \ucd94\uac00\uc801\uc778 \uc815\uc81c \uc791\uc5c5\uc744 \uc218\ud589 \uac00\ub2a5\n return text\n\ndata['cleaned_reviews'] = data['reviews'].apply(clean_text)<\/code><\/pre>\n6. \ud14d\uc2a4\ud2b8 \ubca1\ud130\ud654<\/h2>\n\ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \ubaa8\ub378\uc5d0 \uc801\uc6a9\ud558\uae30 \uc704\ud574\uc11c\ub294 \ubca1\ud130\ud654 \uacfc\uc815\uc774 \ud544\uc694\ud569\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\ub294 \ubc29\ubc95\uc73c\ub85c\ub294 TF-IDF \ub610\ub294 Word2Vec\uacfc \uac19\uc740 \uc784\ubca0\ub529 \uae30\ubc95\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n from sklearn.feature_extraction.text import TfidfVectorizer\n\n# TF-IDF \ubca1\ud130\ud654\nvectorizer = TfidfVectorizer(max_features=5000) \nX = vectorizer.fit_transform(data['cleaned_reviews']).toarray()\ny = data['sentiment']<\/code><\/pre>\n7. \ubaa8\ub378 \uad6c\ucd95 \ubc0f \ud559\uc2b5<\/h2>\n\ub525 \ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud558\uace0 \uac10\uc131 \ubd84\uc11d\uc744 \uc704\ud55c \ud559\uc2b5\uc744 \uc9c4\ud589\ud569\ub2c8\ub2e4. LSTM \ubaa8\ub378\uc744 \uc608\ub85c \ub4e4\uba74 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n from keras.models import Sequential\nfrom keras.layers import Embedding, LSTM, Dense\n\nmodel = Sequential()\nmodel.add(Embedding(input_dim=5000, output_dim=128, input_length=X.shape[1]))\nmodel.add(LSTM(units=64, return_sequences=False))\nmodel.add(Dense(1, activation='sigmoid'))\n\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n# \ubaa8\ub378 \ud559\uc2b5\nmodel.fit(X, y, epochs=10, batch_size=32, validation_split=0.2)<\/code><\/pre>\n8. \ubaa8\ub378 \uc131\ub2a5 \ud3c9\uac00<\/h2>\n\ubaa8\ub378\uc744 \ud559\uc2b5\ud55c \ud6c4, \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4. \ud3c9\uac00 \ubc29\ubc95\uc73c\ub85c\ub294 \uc815\ud655\ub3c4, \uc815\ubc00\ub3c4, \uc7ac\ud604\uc728, F1 \uc810\uc218 \ub4f1\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n from sklearn.metrics import classification_report\n\n# \uc608\uce21\ny_pred = model.predict(X_test)\n\n# \ubd84\ub958 \ubcf4\uace0\uc11c \ucd9c\ub825\nprint(classification_report(y_test, y_pred.round()))<\/code><\/pre>\n9. \uacb0\uacfc \ubc0f \uacb0\ub860<\/h2>\n\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uae30\ubc95\uc73c\ub85c, \ub124\uc774\ubc84 \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\uc14b\uc744 \uc0ac\uc6a9\ud574 \uac10\uc131 \ubd84\uc11d\uc744 \uc218\ud589\ud574\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130 \uc804\ucc98\ub9ac\ubd80\ud130 \ubaa8\ub378 \ud559\uc2b5 \ubc0f \ud3c9\uac00\uae4c\uc9c0\uc758 \uc804 \uacfc\uc815\uc744 \uc0b4\ud3b4\ubcf4\uc558\uc73c\uba70, \uc55e\uc73c\ub85c \ub2e4\uc591\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubb38\uc81c\uc5d0 \ub300\ud574\uc11c\ub3c4 \uc801\uc6a9\ud560 \uc218 \uc788\ub294 \uae30\ucd08\ub97c \ub2e4\uc9c8 \uc218 \uc788\uc5c8\uc2b5\ub2c8\ub2e4.<\/p>\n 10. \ucd94\uac00 \uc790\ub8cc<\/h2>\n\nTensorFlow \uacf5\uc2dd \ubb38\uc11c<\/a><\/li>\n Pandas \uacf5\uc2dd \ubb38\uc11c<\/a><\/li>\n Scikit-Learn \uacf5\uc2dd \ubb38\uc11c<\/a><\/li>\n<\/ul>\n\ub525 \ub7ec\ub2dd\uacfc \uc790\uc5f0\uc5b4 \ucc98\ub9ac \ubd84\uc57c\ub294 \ub9e4\uc6b0 \ube60\ub974\uac8c \ubc1c\uc804\ud558\uace0 \uc788\uc73c\uba70, \uc55e\uc73c\ub85c\uc758 \uac00\ub2a5\uc131\uc774 \ubb34\uad81\ubb34\uc9c4\ud569\ub2c8\ub2e4. \uc774 \uac15\uc88c\uac00 \uc5ec\ub7ec\ubd84\uc758 \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uae30\uc220 \ud5a5\uc0c1\uc5d0 \ub3c4\uc6c0\uc774 \ub418\uae38 \ubc14\ub78d\ub2c8\ub2e4!<\/p>\n <\/body><\/p>\n","protected":false},"excerpt":{"rendered":"\uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub294 \ucef4\ud4e8\ud130\uac00 \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \uc774\ud574\ud558\uace0 \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \ud558\ub294 \uae30\uc220\ub85c, \ucd5c\uadfc \ub525 \ub7ec\ub2dd\uc758 \ubc1c\uc804\uc73c\ub85c \uc778\ud574 \ub9ce\uc740 \ud601\uc2e0\uc744 \uc774\ub8e8\uc5b4\ub0c8\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \ud55c \uc608\ub85c, \ub124\uc774\ubc84 \uc601\ud654 \ub9ac\ubdf0 \ub370\uc774\ud130\uc14b\uc744 \ud65c\uc6a9\ud558\uc5ec \uc601\ud654 \ub9ac\ubdf0\uc758 \uac10\uc131\uc744 \ubd84\ub958\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP) \uac1c\uc694 \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP, Natural Language Processing)\ub294 \ucef4\ud4e8\ud130 \uacfc\ud559\uacfc \uc5b8\uc5b4\ud559\uc758 \uc735\ud569 \ubd84\uc57c\ub85c, \uc778\uac04\uc758 … \ub354 \ubcf4\uae30 “\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac: \ub124\uc774\ubc84 \uc601\ud654 \ub9ac\ubdf0 \uac10\uc131 \ubd84\ub958\ud558\uae30”<\/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":[16],"tags":[],"class_list":["post-25327","post","type-post","status-publish","format-standard","hentry","category-16"],"yoast_head":"\n\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac: \ub124\uc774\ubc84 \uc601\ud654 \ub9ac\ubdf0 \uac10\uc131 \ubd84\ub958\ud558\uae30 - 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