Question

how to do the tf-idf vectorization in python ? how do we fit the model?

i am trying like this but i am getting confused please explain with complete program

vectorizer = Tfidvectorizer()

x=vectorizer.fit_transofrm() // what data should be passed here ? do we split it in tokens ?? do we use vocabulary?? do we use s.split() ??

x.toarray() ?

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pleae provide complete program with output because i am not understanding

Answer 1

Solution: It is a problem associated with the Natural Language Processing and the TF-IDF is a concept that is used to measure the occurrence of the specific words inside the document. The whole concept is has been coded in Python and is up and running. Please find attached the code below.

Code:

from sklearn.feature_extraction.text import TfidfVectorizer
corpus = [
    'This is the first document.',
    'This document is the second document.',
    'And this is the third document.',
     'This is the fourth document',
 ]
vectorizer = TfidfVectorizer()
​​​​​​​#You do not need to tokenize the entire data just pass the corpus
X = vectorizer.fit_transform(corpus) 
print(vectorizer.get_feature_names())

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