Sentiment analysis with NLTK and Scikit-learn TfidfVectorizer
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| # https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| """ | |
| min_df=2, discard words appearing in less than 2 documents | |
| max_df=0.9, discard words appering in more than 90% of the documents | |
| sublinear_tf=True, use sublinear weighting | |
| use_idf=True, enable IDF | |
| """ | |
| vec = TfidfVectorizer( | |
| analyzer=preprocessing, | |
| min_df=2, | |
| max_df=0.9, | |
| sublinear_tf=True, | |
| use_idf=True | |
| ) | |
| train_vec = vec.fit_transform(train_tweets['tweet']) |
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