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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