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+import os
+import csv
+
+from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.cluster import KMeans
+from konlpy.tag import Okt
+
+okt = Okt()
+def DocToNouns(docs):
+    return [{
+        'id': i,
+        'nouns': ' '.join(okt.nouns(doc)),
+    } for i, doc in enumerate(docs)]
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+
+posts = []
+with open(os.path.join(BASE_DIR + '/', 'data.csv'), 'r', encoding='utf-8-sig') as db:
+    reader = csv.reader(db)
+    for data in reader:
+        posts.append(data)
+
+nouns = DocToNouns(posts)
+print(nouns)
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