6.4This week, you'll apply your knowledge of data collections for sentiment analysis, a common technique applied to movie, product, and
ue applied to movie, product, and business reviews, as well as social media posts.
For this assignment, you'll determine whether teacher reviews are positive or negative, using real reviews from Rate my Professor.
Modify this wordfreq.py (Links to an external site.)Links to an external site. program to evaluate whether a particular review is positive, negative, or neutral.
You can use these collections of positivePreview the document and negativePreview the document words (in files folder of Canvas). Note the files contain non-ascii characters that you'll need to accommodate like so:
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 teacher, where the score is incremented (+1) for each positive word in the review and decremented (-1) for each negative word
- Use this set of real-life teacher reviews