Stance Classification with Improved Elementary Classifiers Using Lemmatization (Grand Challenge)

Twitter, a microblogging and social networking service, gives us access to a large scale social data. In this report, we try to obtain sentiments of the tweets in context of the #metoo movement. We try to derive a model that classifies tweets into categories of different linguistic aspects like hate, sarcasm, allegations and support/opposition. This helps analyze the tweets and flag them according to relevance. Here we explore elementary machine learning algorithms, multinomial naive Bayes and random forest classifier with ratio selection classification to improve stance classification at greater efficiency than baseline models.