Word Cloud on movie reviews

You have been working with the movie reviews dataset. You have explored the distribution of the reviews and have seen how long the longest and the shortest reviews are. But what do positive and negative reviews talk about?

In this exercise, you will practice building a word cloud of the top 100 positive reviews.

What are the words that pop up? Do they make sense to you?

The string descriptions has been created for you by concatenating the descriptions of the top 100 positive reviews. A movie-specific set of stopwords (very frequent words, such as the, a/an, and, which will not be very informative and we'd like to exclude from the graph) is available as my_stopwords. Recall that the interpolation argument makes the word cloud appear more smoothly.

This exercise is part of the course

Sentiment Analysis in Python

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Exercise instructions

Import the wordcloud function from the respective package.
Apply the word cloud function to the descriptions string. Set the background color as 'white', and change the stopwords argument.
Create a wordcloud image.
Finally, do not forget to display the image.

Hands-on interactive exercise

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

# Import the word cloud function  
____

# Create and generate a word cloud image 
my_cloud = ____(____='white', ____=my_stopwords).____(descriptions)

# Display the generated wordcloud image
plt.____(____, interpolation='bilinear') 
plt.axis("off")

# Don't forget to show the final image
____

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