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# create document term matrix for your data
# you can use TfidfVectorizer instead of CountVectorizer as well
from sklearn.feature_extraction.text import CountVectorizer
cvec = CountVectorizer()
docTermMat = cvec.fit_transform(data['text'].values)
# truncated SVD to preserve 20 topics
from sklearn.decomposition import TruncatedSVD
lsa = TruncatedSVD(n_components = 20, n_iter = 500)
lsa.fit(docTermMat)
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