Multiple text columns

In this exercise, you will continue working with the airline Twitter data. A dataset tweets has been imported for you.

In some situations, you might have more than one text column in a dataset and you might want to create a numeric representation for each of the text columns. Here, besides the text column, which contains the body of the tweet, there is a second text column, called negativereason. It contains the reason the customer left a negative review.

Your task is to build BOW representations for both columns and specify the required stop words.

This exercise is part of the course

Sentiment Analysis in Python

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Exercise instructions

Import the vectorizer package and the default list of English stop words.
Update the default list of English stop words and create the my_stop_words set.
Specify the stop words argument in the first vectorizer to the updated set, and in the second vectorizer - the default set of English stop words.

Hands-on interactive exercise

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

# Import the vectorizer and default English stop words list
____

# Define the stop words
my_stop_words = ____._____(['airline', 'airlines', '@', 'am', 'pm'])
 
# Build and fit the vectorizers
vect1 = CountVectorizer(____=my_stop_words)
vect2 = CountVectorizer(____=____) 
vect1.fit(tweets.text)
vect2.fit(tweets.negative_reason)

# Print the last 15 features from the first, and all from second vectorizer
print(vect1.get_feature_names()[-15:])
print(vect2.get_feature_names())

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