Cosine similarity matrix of a corpus

In this exercise, you have been given a corpus, which is a list containing five sentences. The corpus is printed in the console. You have to compute the cosine similarity matrix which contains the pairwise cosine similarity score for every pair of sentences (vectorized using tf-idf).

Remember, the value corresponding to the ith row and jth column of a similarity matrix denotes the similarity score for the ith and jth vector.

This exercise is part of the course

Feature Engineering for NLP in Python

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Exercise instructions

Initialize an instance of TfidfVectorizer. Name it tfidf_vectorizer.
Using fit_transform(), generate the tf-idf vectors for corpus. Name it tfidf_matrix.
Use cosine_similarity() and pass tfidf_matrix to compute the cosine similarity matrix cosine_sim.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Initialize an instance of tf-idf Vectorizer
tfidf_vectorizer = ____

# Generate the tf-idf vectors for the corpus
tfidf_matrix = tfidf_vectorizer.fit_transform(____)

# Compute and print the cosine similarity matrix
cosine_sim = ____(____, tfidf_matrix)
print(cosine_sim)

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