Cleaning data by removing stopwords and lemmatization. Using TfidfVectorizer to find the best features for analysis. Using Sklean.naive_bayes.MultinomialNB() algorithm for predictions
A sentiment analysis job about the problems of each major U.S. airline. Twitter data was scraped from February of 2015 and contributors were asked to first classify positive, negative, and neutral tweets, followed by categorizing negative reasons (such as “late flight” or “rude service”).