\ucd5c\uadfc \uba87 \ub144\uac04, \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \ub370\uc774\ud130\uc758 \uc591\uacfc \uc811\uadfc\uc131\uc774 \uc99d\uac00\ud568\uc5d0 \ub530\ub77c \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529 \ubc0f \uba38\uc2e0\ub7ec\ub2dd, \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \uae08\uc735 \ubd84\uc11d\uc774 \ud070 \uc8fc\ubaa9\uc744 \ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \uae00\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud558\uc5ec \ub274\uc2a4 \uae30\uc0ac\ub97c \ubd84\ub958\ud558\uace0 \uc774\ub97c \ud2b8\ub808\uc774\ub529\uc5d0 \uc801\uc6a9\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uae4a\uc774 \uc788\uac8c \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529\uc740 \ud2b9\uc815 \uc54c\uace0\ub9ac\uc998\uc774\ub098 \uaddc\uce59\uc744 \uae30\ubc18\uc73c\ub85c \uc790\ub3d9\uc73c\ub85c \ub9e4\ub9e4\ub97c \uc2e4\ud589\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. \ucef4\ud4e8\ud130 \ud504\ub85c\uadf8\ub7a8\uc774 \uc2e4\uc2dc\uac04\uc73c\ub85c \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0, \uc0ac\uc804\uc5d0 \uc124\uc815\ub41c \uaddc\uce59\uc5d0 \ub530\ub77c \ub9e4\uc218\ub97c \ud558\uac70\ub098 \ub9e4\ub3c4\ud558\ub294 \ubc29\uc2dd\uc73c\ub85c \uac70\ub798\ub97c \uc9c4\ud589\ud569\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubc29\uc2dd\uc740 \uc778\uac04\uc758 \uac10\uc815\uc774\ub098 \ud3b8\ud5a5 \uc5c6\uc774 \uc77c\uad00\ub41c \ud2b8\ub808\uc774\ub529\uc744 \uac00\ub2a5\ud558\uac8c \ud569\ub2c8\ub2e4.<\/p>\n
\uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\ub85c\ubd80\ud130 \ud559\uc2b5\ud558\uace0 \uc608\uce21\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc744 \uc124\uacc4\ud558\ub294 \ubd84\uc57c\ub85c, \ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud55c \ubd84\uc57c\ub85c \uc778\uacf5\uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \ub450 \uae30\ubc95\uc758 \uac04\ub2e8\ud55c \ube44\uad50\uc785\ub2c8\ub2e4:<\/p>\n
\uae08\uc735 \uc2dc\uc7a5\uc740 \ub274\uc2a4\uc640 \uc815\ubcf4\uc5d0 \ubbfc\uac10\ud569\ub2c8\ub2e4. \uae0d\uc815\uc801\uc778 \ub274\uc2a4\ub294 \uc8fc\uac00 \uc0c1\uc2b9\uc73c\ub85c \uc774\uc5b4\uc9c8 \uc218 \uc788\uc73c\uba70, \ubd80\uc815\uc801\uc778 \ub274\uc2a4\ub294 \uc2e4\ud328\ub85c \uc774\uc5b4\uc9c8 \uac00\ub2a5\uc131\uc774 \ud07d\ub2c8\ub2e4. \ub530\ub77c\uc11c \ub274\uc2a4 \uae30\uc0ac\ub97c \uc790\ub3d9\uc73c\ub85c \ubd84\ub958\ud558\uc5ec \uae30\uc138\uc5d0 \ub530\ub77c \ub9e4\ub9e4 \uc804\ub7b5\uc744 \uc138\uc6b0\ub294 \uac83\uc740 \uc911\uc694\ud569\ub2c8\ub2e4.<\/p>\n
\ub274\uc2a4 \uae30\uc0ac\ub97c \uc218\uc9d1\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc591\ud569\ub2c8\ub2e4:<\/p>\n
NewsAPI<\/code>\uc640 \uac19\uc740 \uc11c\ube44\uc2a4\ub97c \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n- \uc6f9 \ud06c\ub864\ub9c1: BeautifulSoup, Scrapy\uc640 \uac19\uc740 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud2b9\uc815 \ub274\uc2a4 \uc6f9\uc0ac\uc774\ud2b8\uc5d0\uc11c \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
5. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n
\uc218\uc9d1\ud55c \ub274\uc2a4 \uae30\uc0ac\ub294 \uc885\uc885 \ub178\uc774\uc988\uac00 \ub9ce\uc73c\uba70, \uc774\ub97c \uc815\uc81c\ud558\ub294 \uacfc\uc815\uc740 \ud544\uc218\uc801\uc785\ub2c8\ub2e4. \uc804\ucc98\ub9ac \uacfc\uc815\uc758 \uc8fc\uc694 \ub2e8\uacc4\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- \ud14d\uc2a4\ud2b8 \uc815\uc81c:<\/strong> HTML \ud0dc\uadf8 \uc81c\uac70, \ud2b9\uc218\ubb38\uc790 \ubc0f \uc22b\uc790 \uc81c\uac70<\/li>\n
- \ud1a0\ud070\ud654:<\/strong> \ubb38\uc7a5\uc744 \ub2e8\uc5b4 \ub2e8\uc704\ub85c \ubd84\ud560\ud569\ub2c8\ub2e4.<\/li>\n
- \ubd88\uc6a9\uc5b4 \uc81c\uac70:<\/strong> \uc758\ubbf8\uac00 \uc5c6\ub294 \ub2e8\uc5b4(\uc608: \uadf8\ub9ac\uace0, \ud558\uc9c0\ub9cc \ub4f1)\ub97c \uc81c\uac70\ud569\ub2c8\ub2e4.<\/li>\n
- \ud45c\uc81c\uc5b4 \ucd94\ucd9c \ub610\ub294 \ud615\ud0dc\uc18c \ubd84\uc11d:<\/strong> \ub2e8\uc5b4\uc758 \uae30\ubcf8 \ud615\ud0dc\ub85c \ubcc0\ud658\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
6. \uae30\uacc4 \ud559\uc2b5\uc744 \ud1b5\ud55c \ub274\uc2a4 \uae30\uc0ac \ubd84\ub958<\/h2>\n
\ub274\uc2a4 \uae30\uc0ac\ub97c \uae0d\uc815\uc801, \ubd80\uc815\uc801, \uc911\ub9bd\uc801\uc73c\ub85c \ubd84\ub958\ud558\uae30 \uc704\ud574 \ub2e4\uc591\ud55c \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\ub294 \uc54c\uace0\ub9ac\uc998\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0:<\/strong> \uc774\uc9c4 \ubd84\ub958 \ubb38\uc81c\uc5d0 \uc801\ud569\ud569\ub2c8\ub2e4. \uae30\uc0ac \ub0b4\uc6a9\uc744 \ubc14\ud0d5\uc73c\ub85c \ud655\ub960\uc744 \uacc4\uc0b0\ud558\uc5ec \ud074\ub798\uc2a4\ub97c \ubd84\ub958\ud569\ub2c8\ub2e4.<\/li>\n
- \uc0ac\uc774\ud0a5(SVM):<\/strong> \ub370\uc774\ud130 \ud3ec\uc778\ud2b8 \uac04\uc758 \uacbd\uacc4\ub97c \ucc3e\uc544 \ub450 \ud074\ub798\uc2a4\ub97c \ub098\ub204\ub294 \uac15\ub825\ud55c \uc54c\uace0\ub9ac\uc998\uc785\ub2c8\ub2e4.<\/li>\n
- \ub79c\ub364 \ud3ec\ub808\uc2a4\ud2b8:<\/strong> \uc5ec\ub7ec \uac1c\uc758 \uacb0\uc815 \ud2b8\ub9ac\ub97c \uc0ac\uc6a9\ud558\uc5ec \uc608\uce21\uc744 \uc9c4\ud589\ud558\uba70, \uacfc\uc801\ud569\uc744 \ubc29\uc9c0\ud558\ub294 \ub370 \uc720\ub9ac\ud569\ub2c8\ub2e4.<\/li>\n
- \uc2e0\uacbd\ub9dd:<\/strong> \uc55e\uc11c \uc5b8\uae09\ud55c \uac83\ucc98\ub7fc \uc5ec\ub7ec \uac1c\uc758 \uce35\uc744 \ud1b5\ud574 \ud559\uc2b5\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
6.1 \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0\ub97c \ud1b5\ud55c \ubd84\ub958 \uc608\uc81c<\/h3>\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.linear_model import LogisticRegression\n\n# \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\ndata = pd.read_csv('news_articles.csv')\nfeatures = data['article']\nlabels = data['label']\n\n# \ud6c8\ub828 \ubc0f \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 \ubd84\ub9ac\nX_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=0.2, random_state=42)\n\n# \ud14d\uc2a4\ud2b8\ub97c \ubca1\ud130\ud654\nvectorizer = CountVectorizer()\nX_train_vect = vectorizer.fit_transform(X_train)\nX_test_vect = vectorizer.transform(X_test)\n\n# \ub85c\uc9c0\uc2a4\ud2f1 \ud68c\uadc0 \ubaa8\ub378 \ud559\uc2b5\nmodel = LogisticRegression()\nmodel.fit(X_train_vect, y_train)\n\n# \uc608\uce21 \uc218\ud589\npredictions = model.predict(X_test_vect)\n<\/code><\/pre>\n7. \ub525\ub7ec\ub2dd\uc744 \ud1b5\ud55c \ub274\uc2a4 \uae30\uc0ac \ubd84\ub958<\/h2>\n
\ub525\ub7ec\ub2dd \ubaa8\ub378\uc740 \ub354 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130\ub97c \ucc98\ub9ac\ud560 \uc218 \uc788\uc5b4\uc11c, LSTM(Long Short-Term Memory) \uac19\uc740 \uc21c\ud658 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub7ec\ud55c \ubaa8\ub378\uc740 \uc2dc\uac04 \uc21c\uc11c\uc5d0 \ub530\ub978 \ub370\uc774\ud130\uc758 \uc0c1\uad00\uad00\uacc4\ub97c \uc798 \ud3ec\ucc29\ud569\ub2c8\ub2e4.<\/p>\n
7.1 LSTM \ubaa8\ub378 \uad6c\ucd95 \uc608\uc81c<\/h3>\nimport numpy as np\nimport pandas as pd\nfrom keras.models import Sequential\nfrom keras.layers import Dense, LSTM, Embedding\nfrom keras.preprocessing.sequence import pad_sequences\nfrom sklearn.model_selection import train_test_split\n\n# \ub370\uc774\ud130 \ubd88\ub7ec\uc624\uae30\ndata = pd.read_csv('news_articles.csv')\nfeatures = data['article']\nlabels = data['label']\n\n# \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \uc218\uce58\ud615\uc73c\ub85c \ubcc0\ud658\ntokenizer = Tokenizer(num_words=5000)\ntokenizer.fit_on_texts(features)\nsequences = tokenizer.texts_to_sequences(features)\nX_pad = pad_sequences(sequences, maxlen=100)\n\n# \ud6c8\ub828 \ubc0f \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130 \ubd84\ub9ac\nX_train, X_test, y_train, y_test = train_test_split(X_pad, labels, test_size=0.2, random_state=42)\n\n# LSTM \ubaa8\ub378 \uad6c\ucd95\nmodel = Sequential()\nmodel.add(Embedding(input_dim=5000, output_dim=128))\nmodel.add(LSTM(100))\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_train, y_train, epochs=5, batch_size=32)\n<\/code><\/pre>\n8. \ub274\uc2a4 \uae30\uc0ac\uc758 \uc8fc\uac00 \uc608\uce21\uc5d0\uc758 \ud65c\uc6a9<\/h2>\n
\ub274\uc2a4 \uae30\uc0ac\ub97c \ud1b5\ud574 \uc8fc\uac00\ub97c \uc608\uce21\ud558\ub294 \uacfc\uc815\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uacc4\ub97c \ud3ec\ud568\ud569\ub2c8\ub2e4:<\/p>\n
\n- \uc804\ucc98\ub9ac \ubc0f \ubd84\ub958: \uc218\uc9d1\ud55c \ub274\uc2a4 \uae30\uc0ac\ub97c \uc804\ucc98\ub9ac\ud558\uace0 \ubd84\ub958\ud574 \uae0d\uc815, \ubd80\uc815\uc758 \ub808\uc774\ube14\uc744 \ubd99\uc785\ub2c8\ub2e4.<\/li>\n
- \uc8fc\uac00 \ub370\uc774\ud130 \uc218\uc9d1: \uc8fc\uac00 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uc5ec \ub274\uc2a4 \uae30\uc0ac\uc640 \uc5f0\uacb0\ud569\ub2c8\ub2e4.<\/li>\n
- \ubaa8\ub378 \ud559\uc2b5: \ucd94\ucd9c\ub41c \ud2b9\uc131\uacfc \ub808\uc774\ube14\uc744 \uae30\ubc18\uc73c\ub85c \uc8fc\uac00\ub97c \uc608\uce21\ud560 \uc218 \uc788\ub294 \ubaa8\ub378\uc744 \ud559\uc2b5\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
9. \uacb0\uacfc \ubd84\uc11d \ubc0f \ud3c9\uac00<\/h2>\n
\ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ud63c\ub3d9 \ud589\ub82c(confusion matrix), \uc815\ud655\ub3c4(accuracy), F1 \uc810\uc218 \ub4f1\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ubaa8\ub378\uc758 \uac15\uc810\uc744 \ud30c\uc545\ud558\uace0 \uac1c\uc120\ud560 \ubc29\ud5a5\uc744 \ubaa8\uc0c9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
9.1 \ubaa8\ub378 \ud3c9\uac00 \ucf54\ub4dc \uc608\uc81c<\/h3>\nfrom sklearn.metrics import classification_report, confusion_matrix\n\n# \uc608\uce21 \uacb0\uacfc \ud3c9\uac00\ncm = confusion_matrix(y_test, predictions)\ncr = classification_report(y_test, predictions)\n\nprint(\"Confusion Matrix:\\n\", cm)\nprint(\"Classification Report:\\n\", cr)\n<\/code><\/pre>\n10. \uacb0\ub860 \ubc0f \ud5a5\ud6c4 \uc5f0\uad6c \ubc29\ud5a5<\/h2>\n
\ub274\uc2a4 \uae30\uc0ac \ubd84\ub958\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\ubc95\uc744 \ud65c\uc6a9\ud558\uc5ec \ud6a8\uacfc\uc801\uc73c\ub85c \uc774\ub8e8\uc5b4\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud5a5\ud6c4 \uc5f0\uad6c\uc5d0\uc11c\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \ubc29\ud5a5\uc73c\ub85c \ubc1c\uc804\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n