Paper: Recognizing Stances in Online Debates

ACL ID P09-1026
Title Recognizing Stances in Online Debates
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009

This paper presents an unsupervised opin- ion analysis method for debate-side clas- sification, i.e., recognizing which stance a person is taking in an online debate. In order to handle the complexities of this genre, we mine the web to learn associa- tions that are indicative of opinion stances in debates. We combine this knowledge with discourse information, and formu- late the debate side classification task as an Integer Linear Programming problem. Our results show that our method is sub- stantially better than challenging baseline methods.