BoW model for movie taglines

In this exercise, you have been provided with a corpus of more than 7000 movie tag lines. Your job is to generate the bag of words representation bow_matrix for these taglines. For this exercise, we will ignore the text preprocessing step and generate bow_matrix directly.

We will also investigate the shape of the resultant bow_matrix. The first five taglines in corpus have been printed to the console for you to examine.

Cet exercice fait partie du cours

Feature Engineering for NLP in Python

Afficher le cours

Instructions

Import the CountVectorizer class from sklearn.
Instantiate a CountVectorizer object. Name it vectorizer.
Using fit_transform(), generate bow_matrix for corpus.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Import CountVectorizer
from sklearn.feature_extraction.text import ____

# Create CountVectorizer object
____ = ____

# Generate matrix of word vectors
bow_matrix = vectorizer.____(____)

# Print the shape of bow_matrix
print(bow_matrix.shape)

Modifier et exécuter le code