Created
April 7, 2019 07:43
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create and display pyLDAvis
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| news_content = df[df.category=='reliable'].news_content.tolist() | |
| tf_vectorizer = CountVectorizer(strip_accents = 'unicode', | |
| stop_words = 'english', | |
| lowercase = True, | |
| token_pattern = r'\b[a-zA-Z]{3,}\b', | |
| max_df = 0.5, | |
| min_df = 10) | |
| tfidf_vectorizer = TfidfVectorizer(**tf_vectorizer.get_params()) | |
| dtm_tfidf = tfidf_vectorizer.fit_transform(news_content) | |
| lda_tfidf = LatentDirichletAllocation(n_topics=20, random_state=0) | |
| lda_tfidf.fit(dtm_tfidf) | |
| vis_data = pyLDAvis.sklearn.prepare(lda_tfidf, dtm_tfidf, tf_vectorizer) | |
| pyLDAvis.display(vis_data) |
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