How to compute cosine similarity with scikit-learn

In this section, I will use the cosine_similarity() function in scikit-learn to show computation.

First, let’s use the same song, “New York, New York” by Frank Sinatra, that was used in Chapter 2, Text Representation:

doc_list = ["Start spreading the news",
"You're leaving today (tell him friend)",
"I want to be a part of it, New York, New York",
"Your vagabond shoes, they are longing to stray",
"And steps around the heart of it, New York, New York"
]

This document has five sentences. Let’s create the bag of words for this document with the CountVectorizer() function in scikit-learn:

from sklearn.feature_extraction.text import CountVectorizerimport pandas as pd
cv = CountVectorizer()
cv_fit = cv.fit_transform(doc_list)
word_list = cv.get_feature_names_out()
count_list = cv_fit.toarray().sum(axis=0)

Then let’s print out the result...