code following :
s1 = “this is good movie”
s2 = “this was good movie”
s3 = “this is not good movie”
corpus = [s1,s2,s3]
tfidf = TfidfVectorizer()
vc = tfidf.fit_transform(corpus).toarray()
print(vc)
in vc = “good” was the first word but it didn’t have same weight for all 3 documents, why? as per the formula, they should be same , right?
Hey @sanandasaha3
Terms which appear the same number of times in different documents do not necessarily have the same TF-IDF score because we are missing one variable which can still be different among these docs.
TF(t) = (Number of times term t appears in a document) / (Total number of terms in the document).
The second term i.e. Total number of terms in the document is different for the last document and hence the different score.
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