load.py

# Most of the cleaning was done during the data web scraping
# Find the notebook here 
# https://github.com/kipronokoech/Reviews-Classification/blob/master/data_collection.ipynb
reviews = []
with open("./data/reviews.txt") as fp:
    for index,line in enumerate(fp):
        # line is a strong on loading so we need to convert to dictionary
        review = ast.literal_eval(line)
        #categorize the review and append it to reviews
        reviews.append(Review(review['reviewBody'], review['stars']))
       
# print out the text and the sentiment category of review[0]
print(reviews[0].text)
print(reviews[0].sentiment)