Unsupervised Stance Classification in Online Debates

This paper proposes an unsupervised debate stance classification algorithm. In other words, finding the side a post author is taking in an online debate. Stance detection has a complementary role in information retrieval, opinion mining, text summarization, etc. Existing stance detection techniques are not able to effectively handle two challenges: determine whether a given post is a debate or not? If the post is a debate on a given topic, correctly classify the side that the post author is taking. In this paper, we propose techniques that addresses both the above issues. Compared to existing technique, our technique gives 30% improvement in detection of whether a post is a debate or not. Our technique is able to find the side that an author is taking in a debate by 10% higher F1 score compared to existing work. We achieve this improvement by using new syntactic rules, better aspect popularity detection, co-reference resolution, and a novel integer linear programming model to solve the problem.