This week, you'll apply your knowledge of data collections for sentiment analysis, a common technique applied to movie, product, and business reviews, as well as social media posts.

For this assignment, you'll determine whether movie reviews are positive or negative.

Modify the wordfreq.py program described in the textbook (available here - http://mcsp.wartburg.edu/zelle/python/ppics2/code/chapter11/ (Links to an external site.) ) to evaluate whether a particular review is positive, negative, or neutral.

You can use these collections of positive and negative words. Note the file contain comments and encoding that you'll need to accommodate by reading in like this:

negWords = open('negative-words.txt','r', encoding='utf-8', errors='ignore').read().splitlines()[35:] 

Your modified program should:

exclude 'stop' words from your word counts, using the the below list;

a, an, and, as, at, be, but, etc, for, in, it, its, is, of, or, so, such, the, this, to, with

print the remaining top 25 words, along with their frequency,

print the top 5 positive and top 5 negative words, along with their frequency,

calculate and display a sentiment score for the review, where the score is incremented (+1) for each positive word in the review and decremented (-1) for each negative word,

Hint

- You can use this sample movie review to test your program