Stance Classification in Online Debates

Ghosh, Subrata (2017) Stance Classification in Online Debates. Masters thesis, Indian Institute of Technology Hyderabad.

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Abstract

This paper proposes an unsupervised debate stance classification algorithm. In other words, finding which side a post author is taking in an online debate. Stance detection plays complementary role in information retrieval, text summarization, etc. Existing techniques are not able to handle two challenges in stance detection, namely, 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 leads to 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 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.

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