Airline sentiment with stop words

You are given a dataset, called tweets, which contains customers' reviews and sentiments about airlines. It consists of two columns: airline_sentiment and text where the sentiment can be positive, negative or neutral, and the text is the text of the tweet.

In this exercise, you will create a BOW representation but will account for the stop words. Remember that stop words are not informative and you might want to remove them. That will result in a smaller vocabulary and eventually, fewer features. Keep in mind that we can enrich a default list of stop words with ones that are specific to our context.

Import the default list of English stop words.
Update the default list of stop words with the given list ['airline', 'airlines', '@'] to create my_stop_words.
Specify the stop words argument in the vectorizer.

Exercise

Airline sentiment with stop words

Instructions