Tweets as a Vote: Exploring Political Sentiments on Twitter for Opinion Mining

Abstract

Twitter feeds provide data scientists with a large repository for entity based sentiment analysis. Specifically, the tweets of individual users may be used in order to track the ebb and flow of their sentiments and opinions. However, this domain poses a challenge for traditional classifiers, since the vast majority of tweets are unlabeled. Further, tweets arrive at high speeds and in very large volumes. They are also suspect to change over time (so-called concept drift). In this paper, we present the PyStream algorithm that addresses these issues. Our method starts with a small annotated training set and bootstraps the learning process. We employ online analytic processing (OLAP) to aggregate the opinions of the individuals we track, expressed in terms of the votes they would cast in a national election. Our results indicate that we are able to capture the sentiments of individuals as they evolve over time.