The names of the columns in CountVectorier sparse matrix in python

Question

When I use the code below:

from sklearn.feature_extraction.text import CountVectorizer
X = dataset.Tweet
y = dataset.Type

count_vect = CountVectorizer()
BoW = count_vect.fit_transform(X)

It returns the term frequency document as a sparse matrix.

I found out how to get the data, indices, and indptr of the sparse matrix.

My problem is how can I get the names of the columns (which should be the features or words)?

Answer 1

What you want to use is vectorizer.get_feature_names(). Here is an example from the docs:

from sklearn.feature_extraction.text import CountVectorizer
corpus = [
    'This is the first document.',
    'This document is the second document.',
    'And this is the third one.',
    'Is this the first document?',
]
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(corpus)
print(vectorizer.get_feature_names())
# ['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']
print(X.toarray())  
# [[0 1 1 1 0 0 1 0 1]
#  [0 2 0 1 0 1 1 0 1]
#  [1 0 0 1 1 0 1 1 1]
#  [0 1 1 1 0 0 1 0 1]]

Docs link: https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html