{"id":29407,"date":"2024-10-27T16:18:18","date_gmt":"2024-10-27T16:18:18","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=29407"},"modified":"2024-11-26T06:52:44","modified_gmt":"2024-11-26T06:52:44","slug":"%eb%a8%b8%ec%8b%a0%eb%9f%ac%eb%8b%9d-%eb%b0%8f-%eb%94%a5%eb%9f%ac%eb%8b%9d-%ec%95%8c%ea%b3%a0%eb%a6%ac%ec%a6%98-%ed%8a%b8%eb%a0%88%ec%9d%b4%eb%94%a9-%ed%8a%b8%ec%9c%84%ed%84%b0%ec%99%80-%ec%98%90","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/29407\/","title":{"rendered":"\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ud2b8\uc704\ud130\uc640 \uc610\ud504 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud55c \uac10\uc131 \ubd84\uc11d"},"content":{"rendered":"<p><body><\/p>\n<h2>1. \uc11c\ub860<\/h2>\n<p>\ucd5c\uadfc \uba87 \ub144\uac04 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\uc220\uc758 \uc911\uc694\uc131\uc774 \uae09\uc99d\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc804\ud1b5\uc801\uc778 \uae08\uc735 \ubaa8\ub378\ubfd0\ub9cc \uc544\ub2c8\ub77c \ube44\uc815\ud615 \ub370\uc774\ud130(\uc608: \uc18c\uc15c \ubbf8\ub514\uc5b4, \ub9ac\ubdf0 \uc0ac\uc774\ud2b8 \ub4f1)\ub97c \ud65c\uc6a9\ud55c \uc0c8\ub85c\uc6b4 \uc811\uadfc \ubc29\uc2dd\uc774 \uac01\uad11\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c\uc758 \ubc1c\uc804\uc744 \ub2e4\ub8e8\uace0, \ud2b8\uc704\ud130\uc640 \uc610\ud504 \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud55c \uac10\uc131 \ubd84\uc11d \uae30\ubc95\uc744 \ud1b5\ud574 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uc218\ub9bd\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uae4a\uc774 \uc788\uac8c \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>2. \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uac1c\uc694<\/h2>\n<h3>2.1 \uba38\uc2e0\ub7ec\ub2dd\uc774\ub780?<\/h3>\n<p>\uba38\uc2e0\ub7ec\ub2dd\uc740 \ub370\uc774\ud130\uc5d0\uc11c \ud328\ud134\uc744 \ud559\uc2b5\ud558\uace0 \uc608\uce21\uc744 \uc791\uc131\ud558\ub294 \uc54c\uace0\ub9ac\uc998\uc785\ub2c8\ub2e4. \ub2e4\uc591\ud55c \uc54c\uace0\ub9ac\uc998\uc774 \uc788\uc73c\uba70, \uc8fc\ub85c \uac10\ub3c5 \ud559\uc2b5(Supervised Learning), \ube44\uac10\ub3c5 \ud559\uc2b5(Unsupervised Learning), \uac15\ud654 \ud559\uc2b5(Reinforcement Learning)\uc73c\ub85c \ubd84\ub958\ub429\ub2c8\ub2e4.<\/p>\n<h3>2.2 \ub525\ub7ec\ub2dd\uc774\ub780?<\/h3>\n<p>\ub525\ub7ec\ub2dd\uc740 \uba38\uc2e0\ub7ec\ub2dd\uc758 \ud558\uc704 \ubd84\uc57c\ub85c, \uc778\uacf5 \uc2e0\uacbd\ub9dd\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub354 \ubcf5\uc7a1\ud55c \ud328\ud134\uc744 \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \ub2e4\uce35 \uc2e0\uacbd\ub9dd(Multi-layer Neural Networks)\uc744 \ud1b5\ud574 \ub354 \ub192\uc740 \uc218\uc900\uc758 \ud2b9\uc131\uc744 \uc790\ub3d9\uc73c\ub85c \ucd94\ucd9c\ud574\ub0bc \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>3. \uae08\uc735 \uc2dc\uc7a5\uacfc \ub370\uc774\ud130\uc758 \uc911\uc694\uc131<\/h2>\n<p>\uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \ub370\uc774\ud130\ub294 \ub9e4\uc218 \ubc0f \ub9e4\ub3c4 \uacb0\uc815\uc744 \ub0b4\ub9ac\ub294 \ub370 \ub9e4\uc6b0 \ud070 \uc601\ud5a5\uc744 \ubbf8\uce69\ub2c8\ub2e4. \uac00\uaca9 \ub370\uc774\ud130\ubfd0\ub9cc \uc544\ub2c8\ub77c \ub274\uc2a4, \ud2b8\uc704\ud130, \ub9ac\ubdf0 \ub370\uc774\ud130 \ub4f1 \ube44\uc815\ud615 \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud558\uba74 \uc2dc\uc7a5\uc758 \uac10\uc815\uc744 \ud30c\uc545\ud558\uc5ec \ub354 \ub098\uc740 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uc218\ub9bd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>3.1 \ub370\uc774\ud130 \uc18c\uc2a4\ub97c \ud1b5\ud55c \uc778\uc0ac\uc774\ud2b8<\/h3>\n<p>\uc18c\uc15c \ubbf8\ub514\uc5b4 \ud50c\ub7ab\ud3fc\uc778 \ud2b8\uc704\ud130\uc640 \ub9ac\ubdf0 \ud50c\ub7ab\ud3fc\uc778 \uc610\ud504\ub294 \ud65c\uc6a9\ud560 \uc218 \uc788\ub294 \ubc29\ub300\ud55c \uc591\uc758 \uc2e4\uc2dc\uac04 \ub370\uc774\ud130\uc785\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\ub97c \ubc14\ud0d5\uc73c\ub85c \uc18c\ube44\uc790 \ubc0f \ud22c\uc790\uc790\uc758 \uac10\uc815\uc744 \ubd84\uc11d\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h2>4. \uac10\uc131 \ubd84\uc11d\uc758 \uc6d0\ub9ac<\/h2>\n<p>\uac10\uc131 \ubd84\uc11d\uc740 \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub97c \ud1b5\ud574 \uac10\uc815 \uc0c1\ud0dc\ub97c \ud30c\uc545\ud558\ub294 \ubc29\uc2dd\uc785\ub2c8\ub2e4. \uc8fc\ub85c \uc0ac\uc6a9\ub418\ub294 \uae30\ubc95\uc73c\ub85c\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n<ul>\n<li><strong>\uc815\uc11c \uc0ac\uc804 \uae30\ubc18 \ubc29\ubc95:<\/strong> \uc0ac\uc804\uc5d0 \uc815\uc758\ub41c \uac10\uc815 \ub2e8\uc5b4\ub97c \ud65c\uc6a9\ud558\uc5ec \ud14d\uc2a4\ud2b8\ub97c \ubd84\uc11d\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.<\/li>\n<li><strong>\uba38\uc2e0\ub7ec\ub2dd \uae30\ubc18 \ubc29\ubc95:<\/strong> \ud14d\uc2a4\ud2b8\ub97c \ubca1\ud130\ub85c \ubcc0\ud658\ud55c \ub4a4, \uac10\uc815\uc744 \uc608\uce21\ud558\uae30 \uc704\ud574 \uc5ec\ub7ec \uba38\uc2e0\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998\uc744 \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<li><strong>\ub525\ub7ec\ub2dd \uae30\ubc18 \ubc29\ubc95:<\/strong> LSTM, GRU\uc640 \uac19\uc740 \uc21c\ud658 \uc2e0\uacbd\ub9dd(RNN)\uc744 \ud65c\uc6a9\ud558\uc5ec \ubb38\ub9e5\uc744 \uace0\ub824\ud55c \uac10\uc815 \ubd84\uc11d\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>5. \ud2b8\uc704\ud130 API\ub97c \ud65c\uc6a9\ud55c \ub370\uc774\ud130 \uc218\uc9d1<\/h2>\n<p>\ud2b8\uc704\ud130 API\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud2b9\uc815 \uc8fc\uc81c\uc640 \uad00\ub828\ub41c \ud2b8\uc717 \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \uba3c\uc800 \ud2b8\uc704\ud130 \uac1c\ubc1c\uc790 \uacc4\uc815\uc744 \uc0dd\uc131\ud558\uace0 API \ud0a4\ub97c \ubc1c\uae09\ubc1b\uc740 \ud6c4, \uc544\ub798\uc640 \uac19\uc740 Python \ucf54\ub4dc\ub97c \uc2e4\ud589\ud558\uc5ec \ub370\uc774\ud130\ub97c \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>\nimport tweepy\n\n# \ud2b8\uc704\ud130 API \uc778\uc99d\nconsumer_key = 'YOUR_CONSUMER_KEY'\nconsumer_secret = 'YOUR_CONSUMER_SECRET'\naccess_token = 'YOUR_ACCESS_TOKEN'\naccess_token_secret = 'YOUR_ACCESS_TOKEN_SECRET'\n\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_token, access_token_secret)\napi = tweepy.API(auth)\n\n# \ud2b9\uc815 \ud0a4\uc6cc\ub4dc\ub85c \ud2b8\uc717 \uc218\uc9d1\nkeyword = '\ud22c\uc790'\ntweets = api.search(q=keyword, count=100)\nfor tweet in tweets:\n    print(tweet.text)\n    <\/code><\/pre>\n<h2>6. \uc610\ud504 \ub370\uc774\ud130 \uc218\uc9d1 \ubc0f \ucc98\ub9ac<\/h2>\n<p>\uc610\ud504 API\ub97c \uc0ac\uc6a9\ud558\uba74 \ud2b9\uc815 \ube44\uc988\ub2c8\uc2a4\uc5d0 \ub300\ud55c \ub9ac\ubdf0\ub97c \uc218\uc9d1\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c\uc740 \uc610\ud504 API\ub97c \uc774\uc6a9\ud55c \ub370\uc774\ud130 \uc218\uc9d1 \uc608\uc2dc\uc785\ub2c8\ub2e4.<\/p>\n<pre><code>\nimport requests\n\n# \uc610\ud504 API \uc778\uc99d\napi_key = 'YOUR_YELP_API_KEY'\nheaders = {'Authorization': 'Bearer ' + api_key}\nurl = 'https:\/\/api.yelp.com\/v3\/businesses\/search'\n\nparams = {\n    'term': 'restaurant',\n    'location': 'San Francisco'\n}\n\nresponse = requests.get(url, headers=headers, params=params)\nbusinesses = response.json()['businesses']\n\nfor business in businesses:\n    print(business['name'], business['rating'])\n    <\/code><\/pre>\n<h2>7. \ub370\uc774\ud130 \uc804\ucc98\ub9ac \ubc0f \uac10\uc131 \ubd84\uc11d<\/h2>\n<p>\uc218\uc9d1\ud55c \ud14d\uc2a4\ud2b8 \ub370\uc774\ud130\ub294 \uc804\ucc98\ub9ac \uacfc\uc815\uc744 \uac70\uccd0\uc57c \ud569\ub2c8\ub2e4. \uc804\ucc98\ub9ac \ub2e8\uacc4\uc5d0\uc11c\ub294 \ubd88\uc6a9\uc5b4 \uc81c\uac70, \ud1a0\ud070\ud654, \ud45c\uc81c\uc5b4 \ucd94\ucd9c \ub4f1\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n<h3>7.1 \ub370\uc774\ud130 \uc804\ucc98\ub9ac \uc608\uc2dc<\/h3>\n<pre><code>\nimport pandas as pd\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize\nfrom nltk.stem import WordNetLemmatizer\n\n# \ubd88\uc6a9\uc5b4 \uc124\uc815\nstop_words = set(stopwords.words('korean'))\nlemmatizer = WordNetLemmatizer()\n\ndef preprocess_text(text):\n    tokens = word_tokenize(text)\n    tokens = [lemmatizer.lemmatize(word) for word in tokens if word not in stop_words]\n    return ' '.join(tokens)\n\n# \ub370\uc774\ud130 \uc804\ucc98\ub9ac \uc801\uc6a9\ntweets_df['processed'] = tweets_df['text'].apply(preprocess_text)\n    <\/code><\/pre>\n<h3>7.2 \uac10\uc131 \ubd84\uc11d \ubaa8\ub378 \uad6c\ucd95<\/h3>\n<p>\uc774\uc81c \uc804\ucc98\ub9ac\ub41c \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud558\uc5ec \uba38\uc2e0\ub7ec\ub2dd \ub610\ub294 \ub525\ub7ec\ub2dd \ubaa8\ub378\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 \uac10\uc131 \ubd84\uc11d\uc744 \uc704\ud55c LSTM \ubaa8\ub378\uc758 \uad6c\ud604 \uc608\uc2dc\uc785\ub2c8\ub2e4.<\/p>\n<pre><code>\nfrom keras.models import Sequential\nfrom keras.layers import LSTM, Dense, Embedding, SpatialDropout1D\nfrom keras.preprocessing.sequence import pad_sequences\n\nmax_features = 20000\nmax_len = 100\n\n# LSTM \ubaa8\ub378 \uad6c\ucd95\nmodel = Sequential()\nmodel.add(Embedding(max_features, 128))\nmodel.add(SpatialDropout1D(0.2))\nmodel.add(LSTM(100))\nmodel.add(Dense(1, activation='sigmoid'))\n\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\nmodel.summary()\n    <\/code><\/pre>\n<h2>8. \ud2b8\ub808\uc774\ub529 \uc804\ub7b5 \uac1c\ubc1c<\/h2>\n<p>\uac10\uc131 \ubd84\uc11d \uacb0\uacfc\ub97c \ud65c\uc6a9\ud558\uc5ec \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uc218\ub9bd\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uae0d\uc815\uc801\uc778 \uac10\uc131\uc774 \ub192\uc740 \uacbd\uc6b0 \ub9e4\uc218, \ubd80\uc815\uc801\uc778 \uac10\uc131\uc774 \ub192\uc744 \uacbd\uc6b0 \ub9e4\ub3c4\uc758 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n<h3>8.1 \uac70\ub798 \uc2e0\ud638 \uc0dd\uc131<\/h3>\n<p>\uac10\uc131 \uc810\uc218\uc5d0 \ub530\ub77c \ub9e4\uc218 \ubc0f \ub9e4\ub3c4 \uc2e0\ud638\ub97c \uc0dd\uc131\ud558\ub294 \ub85c\uc9c1\uc744 \uc791\uc131\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\uc2dc \ucf54\ub4dc\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4.<\/p>\n<pre><code>\ndef generate_signals(sentiment_score):\n    if sentiment_score &gt; 0.5:\n        return 'buy'\n    elif sentiment_score &lt; 0.5:\n        return 'sell'\n    else:\n        return 'hold'\n\ndf['signal'] = df['sentiment_score'].apply(generate_signals)\n    <\/code><\/pre>\n<h2>9. \uc131\uacfc \ubd84\uc11d \ubc0f \uacb0\uacfc \ud3c9\uac00<\/h2>\n<p>\ucd5c\uc885\uc801\uc73c\ub85c \uac1c\ubc1c\ub41c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc758 \uc131\uacfc\ub97c \ubd84\uc11d\ud558\uc5ec \uc218\uc775\ub960\uc744 \ud3c9\uac00\ud574\uc57c \ud569\ub2c8\ub2e4. \uc5ec\ub7ec \uc9c0\ud45c\ub97c \ud1b5\ud574 \uc704\ud5d8 \uc870\uc815 \uc218\uc775\ub960, \ucd5c\ub300 \uc190\uc2e4 \ub4f1\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/p>\n<h3>9.1 \uc131\uacfc \ud3c9\uac00 \uc9c0\ud45c<\/h3>\n<ul>\n<li><strong>\uc0e4\ud504 \ube44\uc728(Sharpe Ratio):<\/strong> \ub2e8\uc704 \uc704\ud5d8\ub2f9 \ucd08\uacfc \uc218\uc775\uc744 \ub098\ud0c0\ub0c5\ub2c8\ub2e4.<\/li>\n<li><strong>\ub4dc\ub85c\uc6b0\ub2e4\uc6b4(Drawdown):<\/strong> \uc190\uc2e4\uc758 \ucd5c\ub300 \ud06c\uae30\ub97c \uce21\uc815\ud569\ub2c8\ub2e4.<\/li>\n<li><strong>\uc54c\ud30c(Alpha):<\/strong> \uad00\ub9ac\uc790\uac00 \uc2dc\uc7a5\uc744 \ucd08\uacfc\ud558\uc5ec \ub2ec\uc131\ud55c \uc218\uc775\uc785\ub2c8\ub2e4.<\/li>\n<\/ul>\n<h2>10. \uacb0\ub860<\/h2>\n<p>\ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \ud2b8\uc704\ud130\uc640 \uc610\ud504 \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud55c \uac10\uc131 \ubd84\uc11d\uc744 \ud1b5\ud574 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\ubc18\uc758 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ud1b5\ud574 \ub354\uc6b1 \uc815\uad50\ud55c \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c\uc744 \uad6c\ucd95\ud560 \uc218 \uc788\uc744 \uac83\uc785\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \ub098\ud0c0\ub098\ub294 \ub2e4\uc591\ud55c \uae30\ubc95\uacfc \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud558\uc5ec \uc9c0\uc18d\uc801\uc73c\ub85c \uc804\ub7b5\uc744 \uac1c\uc120\ud574 \ub098\uac00\uc57c \ud569\ub2c8\ub2e4.<\/p>\n<h3>10.1 \ucc38\uace0\uc790\ub8cc<\/h3>\n<ul>\n<li><a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2020\/03\/stock-market-prediction-using-machine-learning-in-python\/\">Stock Market Prediction Using Machine Learning<\/a><\/li>\n<li><a href=\"https:\/\/towardsdatascience.com\/sentiment-analysis-for-beginners-using-nltk-and-scikit-learn-7b06178f0f14\">Sentiment Analysis for Beginners<\/a><\/li>\n<li><a href=\"https:\/\/keras.io\/\">Keras Documentation<\/a><\/li>\n<\/ul>\n<p><\/body><\/p>\n","protected":false},"excerpt":{"rendered":"<p>1. \uc11c\ub860 \ucd5c\uadfc \uba87 \ub144\uac04 \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c\uc758 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uae30\uc220\uc758 \uc911\uc694\uc131\uc774 \uae09\uc99d\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \uc804\ud1b5\uc801\uc778 \uae08\uc735 \ubaa8\ub378\ubfd0\ub9cc \uc544\ub2c8\ub77c \ube44\uc815\ud615 \ub370\uc774\ud130(\uc608: \uc18c\uc15c \ubbf8\ub514\uc5b4, \ub9ac\ubdf0 \uc0ac\uc774\ud2b8 \ub4f1)\ub97c \ud65c\uc6a9\ud55c \uc0c8\ub85c\uc6b4 \uc811\uadfc \ubc29\uc2dd\uc774 \uac01\uad11\ubc1b\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\ub294 \uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ud2b8\ub808\uc774\ub529 \uc2dc\uc2a4\ud15c\uc758 \ubc1c\uc804\uc744 \ub2e4\ub8e8\uace0, \ud2b8\uc704\ud130\uc640 \uc610\ud504 \ub370\uc774\ud130\ub97c \ud65c\uc6a9\ud55c \uac10\uc131 \ubd84\uc11d \uae30\ubc95\uc744 \ud1b5\ud574 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uc218\ub9bd\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud574 \uae4a\uc774 &hellip; <a href=\"https:\/\/atmokpo.com\/w\/29407\/\" class=\"more-link\">\ub354 \ubcf4\uae30<span class=\"screen-reader-text\"> &#8220;\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, \ud2b8\uc704\ud130\uc640 \uc610\ud504 \ub370\uc774\ud130\ub97c \uc0ac\uc6a9\ud55c \uac10\uc131 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