{"id":25329,"date":"2024-10-26T09:54:08","date_gmt":"2024-10-26T09:54:08","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=25329"},"modified":"2024-11-26T08:02:08","modified_gmt":"2024-11-26T08:02:08","slug":"%eb%94%a5-%eb%9f%ac%eb%8b%9d%ec%9d%84-%ec%9d%b4%ec%9a%a9%ed%95%9c-%ec%9e%90%ec%97%b0%ec%96%b4-%ec%b2%98%eb%a6%ac-%eb%84%a4%ec%9d%b4%eb%b2%84-%ec%87%bc%ed%95%91-%eb%a6%ac%eb%b7%b0-%ea%b0%90%ec%84%b1","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/25329\/","title":{"rendered":"\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac, \ub124\uc774\ubc84 \uc1fc\ud551 \ub9ac\ubdf0 \uac10\uc131 \ubd84\ub958\ud558\uae30"},"content":{"rendered":"<p><body><\/p>\n<p>\uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub294 \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \ucef4\ud4e8\ud130\uac00 \uc774\ud574\ud558\ub3c4\ub85d \ud558\ub294 \uae30\uc220\ub85c, \ucd5c\uadfc\uc5d0\ub294 \ub525 \ub7ec\ub2dd \uae30\ubc95\uc758 \ubc1c\uc804\uc73c\ub85c \uadf8 \uac00\ub2a5\uc131\uc774 \ub354\uc6b1 \ud655\ub300\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ub300\ub7c9\uc758 \ub9ac\ubdf0 \ub370\uc774\ud130\uac00 \uc874\uc7ac\ud558\ub294 \uc804\uc790\uc0c1\uac70\ub798 \ud50c\ub7ab\ud3fc\uc5d0\uc11c \uac10\uc131 \ubd84\uc11d\uc740 \uace0\uac1d\uc758 \ud53c\ub4dc\ubc31\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \ucc98\ub9ac\ud558\uace0, \ub9c8\ucf00\ud305 \uc804\ub7b5\uc744 \uc218\ub9bd\ud558\ub294 \ub370 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \uc774 \ube14\ub85c\uadf8\uc5d0\uc11c\ub294 \ub124\uc774\ubc84 \uc1fc\ud551 \ub9ac\ubdf0 \ub370\uc774\ud130\ub97c \uc774\uc6a9\ud55c \uac10\uc131 \ubd84\ub958 \ubc29\ubc95\uc744 \uc18c\uac1c\ud569\ub2c8\ub2e4.<\/p>\n<h2>1. \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub780?<\/h2>\n<p>\uc790\uc5f0\uc5b4 \ucc98\ub9ac(Natural Language Processing, NLP)\ub294 \ucef4\ud4e8\ud130 \uacfc\ud559\uacfc \uc778\uacf5\uc9c0\ub2a5\uc758 \ud55c \ubd84\uc57c\ub85c, \uc790\uc5f0\uc5b4(\uc778\uac04\uc758 \uc5b8\uc5b4)\ub97c \uc774\ud574\ud558\uace0 \ud574\uc11d\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4. NLP\ub294 \ud06c\uac8c \ub2e4\uc74c\uacfc \uac19\uc740 \uacfc\uc815\uc73c\ub85c \uad6c\uc131\ub429\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>\ud14d\uc2a4\ud2b8 \uc804\ucc98\ub9ac:<\/strong> \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uace0 \uc815\uc81c\ud558\ub294 \ub2e8\uacc4\uc785\ub2c8\ub2e4. \uc5ec\uae30\uc5d0\ub294 \ud1a0\ud070\ud654, \ubd88\uc6a9\uc5b4 \uc81c\uac70, \uc5b4\uac04 \ucd94\ucd9c \ub4f1\uc758 \uacfc\uc815\uc774 \ud3ec\ud568\ub429\ub2c8\ub2e4.<\/li>\n<li><strong>\ud2b9\uc9d5 \ucd94\ucd9c:<\/strong> \ud14d\uc2a4\ud2b8\uc5d0\uc11c \uc720\uc758\ubbf8\ud55c \uc815\ubcf4\ub97c \ucd94\ucd9c\ud558\uc5ec \uc218\uce58\ud654\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4. TF-IDF, Word2Vec, BERT\uc640 \uac19\uc740 \uae30\ubc95\uc774 \uc0ac\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\ubaa8\ub378 \ud559\uc2b5:<\/strong> \uba38\uc2e0\ub7ec\ub2dd \ub610\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uc774\uc6a9\ud574 \ub370\uc774\ud130\ub97c \ud559\uc2b5\uc2dc\ud0a4\ub294 \ub2e8\uacc4\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\ubaa8\ub378 \ud3c9\uac00:<\/strong> \ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uace0, \ud544\uc694\ud55c \uacbd\uc6b0 \ud30c\ub77c\ubbf8\ud130 \uc870\uc815 \ubc0f \ubaa8\ub378 \uc218\uc815\uc774 \uc774\ub8e8\uc5b4\uc9d1\ub2c8\ub2e4.<\/li>\n<li><strong>\uacb0\uacfc \ud65c\uc6a9:<\/strong> \ud559\uc2b5\ud55c \ubaa8\ub378\uc744 \ud1b5\ud574 \uc0c8\ub85c\uc6b4 \ub370\uc774\ud130\uc5d0 \ub300\ud55c \uc608\uce21\uc744 \uc218\ud589\ud558\uace0, \uc774\ub97c \uc2e4\uc81c \ube44\uc988\ub2c8\uc2a4\uc5d0 \uc801\uc6a9\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>2. \ub525 \ub7ec\ub2dd \uae30\ubc95\uc758 \ubc1c\uc804<\/h2>\n<p>\ub525 \ub7ec\ub2dd\uc740 \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \uae30\ubc18\uc73c\ub85c \ud55c \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc73c\ub85c, \uc801\uce35\ub41c \uad6c\uc870\ub97c \ud1b5\ud574 \ub370\uc774\ud130\uc5d0\uc11c \uc790\ub3d9\uc73c\ub85c \ud2b9\uc9d5\uc744 \ud559\uc2b5\ud558\ub294 \ub2a5\ub825\uc774 \ub6f0\uc5b4\ub0a9\ub2c8\ub2e4. \ucd5c\uadfc \uba87 \ub144\uac04 CNN(Convolutional Neural Networks)\uacfc RNN(Recurrent Neural Networks)\uacfc \uac19\uc740 \ub124\ud2b8\uc6cc\ud06c \uad6c\uc870\uac00 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc5d0 \ud6a8\uacfc\uc801\uc73c\ub85c \uc801\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, BERT(Bidirectional Encoder Representations from Transformers)\uc640 \uac19\uc740 \ubaa8\ub378\uc774 \uc790\uc5f0\uc5b4 \ucc98\ub9ac\uc758 \uc131\ub2a5\uc744 \ube44\uc57d\uc801\uc73c\ub85c \ud5a5\uc0c1\uc2dc\ucf30\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>3. \ub124\uc774\ubc84 \uc1fc\ud551 \ub9ac\ubdf0 \ub370\uc774\ud130 \uc218\uc9d1<\/h2>\n<p>\ub124\uc774\ubc84 \uc1fc\ud551\uc5d0\uc11c\uc758 \ub9ac\ubdf0 \ub370\uc774\ud130\ub294 \ub2e4\uc591\ud55c \uc18c\ube44\uc790\uc758 \uc758\uacac\uacfc \uac10\uc815\uc744 \ub2f4\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\uae30 \uc704\ud574 \uc6f9 \uc2a4\ud06c\ub808\uc774\ud551 \uae30\ubc95\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Python\uc758 BeautifulSoup \ub77c\uc774\ube0c\ub7ec\ub9ac\ub098 Scrapy \ud504\ub808\uc784\uc6cc\ud06c\ub97c \uc774\uc6a9\ud558\uc5ec \uc6d0\ud558\ub294 \ub9ac\ubdf0 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>3.1 BeautifulSoup\ub97c \uc774\uc6a9\ud55c \ub370\uc774\ud130 \uc218\uc9d1 \uc608\uc81c<\/h3>\n<pre><code>import requests\nfrom bs4 import BeautifulSoup\n\nurl = 'https:\/\/shopping.naver.com\/your_product_page'\nresponse = requests.get(url)\nsoup = BeautifulSoup(response.text, 'html.parser')\n\nreviews = soup.find_all('div', class_='review')\nfor review in reviews:\n    print(review.text)<\/code><\/pre>\n<h2>4. \ub370\uc774\ud130 \uc804\ucc98\ub9ac<\/h2>\n<p>\uc218\uc9d1\ud55c \ub9ac\ubdf0 \ub370\uc774\ud130\ub97c \ubaa8\ub378 \ud559\uc2b5\uc5d0 \uc801\ud569\ud558\ub3c4\ub85d \uc804\ucc98\ub9ac\ud574\uc57c \ud569\ub2c8\ub2e4. \uc804\ucc98\ub9ac \ub2e8\uacc4\uc5d0\uc11c\ub294 \ub2e4\uc74c\uacfc \uac19\uc740 \uc791\uc5c5\uc744 \uc218\ud589\ud569\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>\ud1a0\ud070\ud654:<\/strong> \ubb38\uc7a5\uc744 \ub2e8\uc5b4 \ub2e8\uc704\ub85c \ubd84\ub9ac\ud558\ub294 \uacfc\uc815\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\ubd88\uc6a9\uc5b4 \uc81c\uac70:<\/strong> \uc758\ubbf8\uac00 \uc5c6\ub294 \ub2e8\uc5b4\ub97c \uc81c\uac70\ud558\uc5ec \ub370\uc774\ud130\uc758 \ud488\uc9c8\uc744 \ub192\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\uc5b4\uac04 \ucd94\ucd9c:<\/strong> \ub2e8\uc5b4\uc758 \uc5b4\uc6d0\uc744 \ucd94\ucd9c\ud558\uc5ec \ud615\ud0dc\uc18c \ubd84\uc11d\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h3>4.1 \uc804\ucc98\ub9ac \uc608\uc81c<\/h3>\n<pre><code>import re\nfrom nltk.tokenize import word_tokenize\nfrom nltk.corpus import stopwords\n\ndef preprocess(text):\n    # \ud2b9\uc218\ubb38\uc790 \uc81c\uac70\n    text = re.sub('[^A-Za-z0-9\uac00-\ud7a3\\s]', '', text)\n    # \ud1a0\ud070\ud654\n    tokens = word_tokenize(text)\n    # \ubd88\uc6a9\uc5b4 \uc81c\uac70\n    tokens = [word for word in tokens if word not in stopwords.words('korean')]\n    return tokens<\/code><\/pre>\n<h2>5. \uac10\uc131 \ubd84\ub958 \ubaa8\ub378 \uad6c\ucd95<\/h2>\n<p>\uc804\ucc98\ub9ac\ub41c \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \uac10\uc131 \ubd84\ub958 \ubaa8\ub378\uc744 \uad6c\ucd95\ud569\ub2c8\ub2e4. \uac04\ub2e8\ud55c LSTM(Long Short-Term Memory) \ubaa8\ub378\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub9ac\ubdf0\uc758 \uac10\uc131\uc744 \uae0d\uc815, \ubd80\uc815\uc73c\ub85c \ubd84\ub958\ud558\ub294 \uc608\uc81c\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>5.1 LSTM \ubaa8\ub378 \uad6c\ucd95 \uc608\uc81c<\/h3>\n<pre><code>from keras.models import Sequential\nfrom keras.layers import Embedding, LSTM, Dense\n\nmodel = Sequential()\nmodel.add(Embedding(input_dim=vocab_size, output_dim=embedding_dim, input_length=max_length))\nmodel.add(LSTM(units=128, dropout=0.2, recurrent_dropout=0.2))\nmodel.add(Dense(units=1, activation='sigmoid'))\n\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])<\/code><\/pre>\n<h2>6. \ubaa8\ub378 \ud3c9\uac00 \ubc0f \uc131\ub2a5 \ud5a5\uc0c1<\/h2>\n<p>\ubaa8\ub378\uc758 \uc131\ub2a5\uc744 \ud3c9\uac00\ud558\uae30 \uc704\ud574 \ud559\uc2b5 \ub370\uc774\ud130\uc640 \uac80\uc99d \ub370\uc774\ud130\ub97c \ubd84\ub9ac\ud558\uc5ec \ud559\uc2b5 \ud6c4 \ud3c9\uac00\ub97c \uc9c4\ud589\ud569\ub2c8\ub2e4. \ub610\ud55c, \ubaa8\ub378\uc758 \uc815\ud655\ub3c4\ub97c \ub192\uc774\uae30 \uc704\ud55c \ub2e4\uc591\ud55c \ubc29\ubc95\uc744 \uc801\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li>\ub370\uc774\ud130 \uc99d\uac15: \ub2e4\uc591\ud55c \ubcc0\ud615\uc744 \ud1b5\ud574 \ub370\uc774\ud130 \uc591\uc744 \ub298\ub9bd\ub2c8\ub2e4.<\/li>\n<li>\ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130 \ud29c\ub2dd: \ud559\uc2b5\ub960, \ubc30\uce58 \ud06c\uae30 \ub4f1 \ubaa8\ub378\uc758 \ud558\uc774\ud37c\ud30c\ub77c\ubbf8\ud130\ub97c \uc870\uc815\ud569\ub2c8\ub2e4.<\/li>\n<li>\uc804\uc774 \ud559\uc2b5: \uc0ac\uc804 \ud559\uc2b5\ub41c \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uc5ec \uc131\ub2a5\uc744 \uac1c\uc120\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h3>6.1 \ud3c9\uac00 \uc608\uc81c<\/h3>\n<pre><code>loss, accuracy = model.evaluate(X_test, y_test)\nprint(f'Test accuracy: {accuracy * 100:.2f}%')<\/code><\/pre>\n<h2>7. \uacb0\uacfc \ud574\uc11d \ubc0f \ud65c\uc6a9<\/h2>\n<p>\ubaa8\ub378\uc758 \uacb0\uacfc\ub97c \uae30\ubc18\uc73c\ub85c \ub124\uc774\ubc84 \uc1fc\ud551 \ub9ac\ubdf0 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uace0, \uc18c\ube44\uc790\ub4e4\uc758 \uac10\uc815\uacfc \ud2b8\ub80c\ub4dc\ub97c \ud30c\uc545\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \ud2b9\uc815 \uc81c\ud488\uc758 \ub9ac\ubdf0\uc5d0\uc11c \uae0d\uc815\uc801\uc778 \ud53c\ub4dc\ubc31\uc774 \ub9ce\ub2e4\uba74 \ud574\ub2f9 \uc81c\ud488\uc758 \ub9c8\ucf00\ud305 \uc804\ub7b5\uc744 \uac15\ud654\ud558\ub294 \ubc29\ud5a5\uc73c\ub85c \ud65c\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>8. \uacb0\ub860<\/h2>\n<p>\ub525 \ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac \uae30\uc220\uc740 \ub124\uc774\ubc84 \uc1fc\ud551 \ub9ac\ubdf0\uc640 \uac19\uc740 \ub300\ub7c9\uc758 \ub370\uc774\ud130\ub97c \ud6a8\uacfc\uc801\uc73c\ub85c \ubd84\uc11d\ud560 \uc218 \uc788\ub294 \uac15\ub825\ud55c \ub3c4\uad6c\uc785\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\ub97c \ud1b5\ud574 \ub525 \ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud558\uc5ec \uac10\uc131 \ubd84\uc11d\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \uc18c\ube44\uc790 \ud53c\ub4dc\ubc31\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \ubd84\uc11d\ud558\uace0, \ube44\uc988\ub2c8\uc2a4 \uc758\uc0ac\uacb0\uc815\uc5d0 \ud65c\uc6a9\ud560 \uc218 \uc788\ub294 \uae30\ud68c\ub97c \uc81c\uacf5\ud558\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n<h2>9. \ucc38\uace0\ubb38\ud5cc<\/h2>\n<ul>\n<li>\uae40\uc0c1\ud615, &#8220;\uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub85c \ubc30\uc6b0\ub294 \ub525\ub7ec\ub2dd&#8221;, \ud55c\ube5b\ubbf8\ub514\uc5b4, 2020.<\/li>\n<li>\uc774\uc131\ud638, &#8220;\ub525\ub7ec\ub2dd\uc744 \uc774\uc6a9\ud55c \uc790\uc5f0\uc5b4 \ucc98\ub9ac&#8221;, \uc778\uc0ac\uc774\ud2b8, 2019.<\/li>\n<li>\uc774\ud574\uc778 \uc678, &#8220;\ud30c\uc774\uc36c \uae30\ubc18\uc758 \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd&#8221;, \uc815\ubcf4\ubb38\ud654\uc0ac, 2021.<\/li>\n<\/ul>\n<h2>10. \ucd94\uac00 \ub9ac\uc18c\uc2a4<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.tensorflow.org\/\">TensorFlow \uacf5\uc2dd \uc6f9\uc0ac\uc774\ud2b8<\/a><\/li>\n<li><a href=\"https:\/\/pytorch.org\/\">PyTorch \uacf5\uc2dd \uc6f9\uc0ac\uc774\ud2b8<\/a><\/li>\n<li><a href=\"https:\/\/www.kaggle.com\/\">Kaggle: \ub370\uc774\ud130 \uacfc\ud559 \uc790\uc728 \ud559\uc2b5 \ud50c\ub7ab\ud3fc<\/a><\/li>\n<\/ul>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\uc790\uc5f0\uc5b4 \ucc98\ub9ac\ub294 \uc778\uac04\uc758 \uc5b8\uc5b4\ub97c \ucef4\ud4e8\ud130\uac00 \uc774\ud574\ud558\ub3c4\ub85d \ud558\ub294 \uae30\uc220\ub85c, \ucd5c\uadfc\uc5d0\ub294 \ub525 \ub7ec\ub2dd \uae30\ubc95\uc758 \ubc1c\uc804\uc73c\ub85c \uadf8 \uac00\ub2a5\uc131\uc774 \ub354\uc6b1 \ud655\ub300\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ud2b9\ud788, \ub300\ub7c9\uc758 \ub9ac\ubdf0 \ub370\uc774\ud130\uac00 \uc874\uc7ac\ud558\ub294 \uc804\uc790\uc0c1\uac70\ub798 \ud50c\ub7ab\ud3fc\uc5d0\uc11c \uac10\uc131 \ubd84\uc11d\uc740 \uace0\uac1d\uc758 \ud53c\ub4dc\ubc31\uc744 \ud6a8\uacfc\uc801\uc73c\ub85c \ucc98\ub9ac\ud558\uace0, \ub9c8\ucf00\ud305 \uc804\ub7b5\uc744 \uc218\ub9bd\ud558\ub294 \ub370 \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud569\ub2c8\ub2e4. \uc774 \ube14\ub85c\uadf8\uc5d0\uc11c\ub294 \ub124\uc774\ubc84 \uc1fc\ud551 \ub9ac\ubdf0 \ub370\uc774\ud130\ub97c \uc774\uc6a9\ud55c \uac10\uc131 \ubd84\ub958 \ubc29\ubc95\uc744 \uc18c\uac1c\ud569\ub2c8\ub2e4. 1. \uc790\uc5f0\uc5b4 \ucc98\ub9ac(NLP)\ub780? 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