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Size of vocabulary of movies reviews

In this exercise, you will practice different ways to limit the size of the vocabulary using a sample of the movies reviews dataset. The first column is the review, which is of type object and the second column is the label, which is 0 for a negative review and 1 for a positive one.

The three methods that you will use will transform the text column to new numeric columns, capturing the count of a word or a phrase in each review. Each method will ultimately result in building a different number of new features.

This exercise is part of the course

Sentiment Analysis in Python

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Hands-on interactive exercise

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

from sklearn.feature_extraction.text import CountVectorizer 

# Build the vectorizer, specify size of vocabulary and fit
vect = CountVectorizer(____=____)
vect.fit(movies.review)

# Transform the review column
X_review = vect.transform(movies.review)
# Create the bow representation
X_df = pd.DataFrame(X_review.toarray(), columns=vect.get_feature_names())
print(X_df.head())
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