Explain TFIDF used in Gensim in detail

In this recipe, we will learn what if TF-IDF and how to use TF-IDF with the help of the Gensim library. We will also look at an example.

Recipe Objective: Explain TF-IDF in Gensim in detail

For turning text into numbers, the bag of words method works well. It does, however, have one flaw. It gives the word a score based on how many times it appears in a document, and it doesn't account for the fact that the word may also frequently appear in other publications. This problem is solved by using TF-IDF.

Learn How to use XLNet for Text Classification 

The term frequency is computed as follows:

  Term frequency = (Frequency of the word in a document)/(Total words in the document)

And the Inverse Document Frequency is calculated as:

  IDF = DF(word) = Log((Total number of documents)/(Number of documents containing the word))

The TfidfModel class from the Gensim library's models module can be used to get the TF-IDF value. We only need to supply the bag of word corpus as a parameter to the TfidfModel class's constructor. All of the words in the three sentences are listed in the output, along with their TF-IDF values.

#importing required libraries
import gensim
from gensim import corpora
from gensim import models
import numpy as np

#creating a sample corpus
txt = ["This is sample document",
"Collection of documents make a corpus",
"You can vectorize your corpus for a mathematically convenient representation of a document"]

#tokenization
tokens = [[token for token in sentence.split()] for sentence in txt]

#creating a dictionary
gensim_dictionary = corpora.Dictionary()

#creating a bow corpus
gensim_corpus = [gensim_dictionary.doc2bow(token, allow_update=True) for token in tokens]

#creating a tf-idf corpus
tfidf = models.TfidfModel(gensim_corpus, smartirs='ntc')

#displaying
for sent in tfidf[gensim_corpus]:
print([[gensim_dictionary[id], np.around(frequency, decimals=2)] for id, frequency in sent])

Output:
[['This', 0.55], ['document', 0.28], ['is', 0.55], ['sample', 0.55]]
[['Collection', 0.52], ['a', 0.26], ['corpus', 0.26], ['documents', 0.52], ['make', 0.52], ['of', 0.26]]
[['document', 0.16], ['a', 0.32], ['corpus', 0.16], ['of', 0.16], ['You', 0.32], ['can', 0.32], ['convenient', 0.32], ['for', 0.32], ['mathematically', 0.32], ['representation', 0.32], ['vectorize', 0.32], ['your', 0.32]]

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