Abstract
This paper presents an unsupervised opinion analysis method for debate-side classification, 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 associations that are indicative of opinion stances in debates. We combine this knowledge with discourse information, and formulate the debate side classification task as an Integer Linear Programming problem. Our results show that our method is substantially better than challenging baseline methods. © 2009 ACL and AFNLP.
Cite
CITATION STYLE
Somasundaran, S., & Wiebe, J. (2009). Recognizing stances in online debates. In ACL-IJCNLP 2009 - Joint Conf. of the 47th Annual Meeting of the Association for Computational Linguistics and 4th Int. Joint Conf. on Natural Language Processing of the AFNLP, Proceedings of the Conf. (pp. 226–234). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1687878.1687912
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