SENTIMENT-BASED CLASSIFICATION OF TWEETERS AND UNIVERSITY PROGRAMS

The rapid growing World Wide Web (WWW) is no longer a passive information provider. Nowadays, Internet users themselves have become contributors of the WWW. A lot of user generated data, along with non-user-generated data, make our world an informative, however, perhaps over-informed society. Thus, the increasing amount of unorganized, randomly generated data drove the momentum of big data analysis, aiming to discover and learn the hidden patterns behind the data. In this thesis, we will look at two problems of mining knowledge from data. In the first project, we develop and test a new methods to classify “democrats” and “republicans ” in Twitter, with the help of sentiment analysis techniques. This sentiment-based classification model is proposed in order to address the problem that conventional quantitative features, such as tweet count, follower-to-following ratio, election tweet count, cannot predict the opinion alignment of Tweeters. Therefore we utilize sentiment features upon events, topics and then feed the sentiment feature vectors into the classification model. The result shows that, even only with contex