Hi, I am using TfidfVectorizer of sklearn.feature_extraction.text for generating tf-idf matrix of a corpus. However, when I look at the features extracted from my corpus it seems that it has reduced my vocabulary size from 1860 to 598! I tried to play with max_df, min_df, and max_features. But nothing changed.
tf = TfidfVectorizer(ngram_range=(1,ngram),use_idf=False) tf_matrix = tf.fit_transform(corpus) feature_names = tf.get_feature_names() Does someone have an idea how to solve this problem? Thank you, Ehsan
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