Part $1$: Using the TextBlob Sentiment Analyzer

Import the movie review data as a data frame and ensure that the data is loaded properly.

How many of each positive and negative reviews are there?

Use TextBlob to classify each movie review as positive or negative. Assume that a polarity score greater than or equal to zero is a positive sentiment and less than $0$ is a negative sentiment$.$

Check the accuracy of this model. Is this model better than random guessing?

For up to five points extra credit, use another prebuilt text sentiment analyzer, e$.$g$.,$ VADER, and repeat steps $(3)$ and $(4).$