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Recognizing stances in online debates
Open accessAbstract
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.
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“Studies in this respect define stance as an overall position held by a person toward an object, idea, or position (Somasundaran and Wiebe 2009;Walker et al. 2012).”
From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Somasundaran and Wiebe 2009
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“Stance classification is well studied in online debates where the aim is to classify the user entries as "for" or "against."”
From Detection and Resolution of Rumours in Social Media: A Survey (2019) · cites this paper as Somasundaran and Wiebe 2009
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Research works printed but not linked: 19
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- Mohit Bansal, Claire Cardie, and Lillian Lee. 2008. The power of negative thinking: Exploiting label disagreement in the min-cut classification frame- work. In Proceedings of COLING: Companion vol- ume: Posters.
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- Soo-Min Kim and Eduard Hovy. 2007. Crystal: An- alyzing predictive opinions on the web. In Pro- ceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Com- putational Natural Language Learning (EMNLP- CoNLL), pages 1056-1064.
- Nozomi Kobayashi, Ryu Iida, Kentaro Inui, and Yuji Matsumoto. 2005. Opinion extraction using a learning-based anaphora resolution technique. In Proceedings of the 2nd International Joint Confer- ence on Natural Language Processing (IJCNLP-05), poster, pages 175-180.
- Wei-Hao Lin, Theresa Wilson, Janyce Wiebe, and Alexander Hauptmann. 2006. Which side are you on? Identifying perspectives at the document and sentence levels. In Proceedings of the 10th Con- ference on Computational Natural Language Learn- ing (CoNLL-2006), pages 109-116, New York, New York.
- Livia Polanyi and Annie Zaenen. 2005. Contextual valence shifters. In Computing Attitude and Affect in Text. Springer.
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- Benjamin Snyder and Regina Barzilay. 2007. Multiple aspect ranking using the good grief algorithm. In Proceedings of NAACL-2007.
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- Veselin Stoyanov and Claire Cardie. 2008. Topic identification for fine-grained opinion analysis. In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 817-824, Manchester, UK, August. Coling 2008 Or- ganizing Committee.
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Cited by
Sorted by citations
Detection and Resolution of Rumours in Social Media: A Survey
Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., & Procter, R. (2019) · ACM Computing Surveys · doi:10.1145/3161603 · Cited by 862
Mohammad, S. M. (2016) · Emotion Measurement · doi:10.1016/b978-0-08-100508-8.00009-6 · Cited by 391
Metadata
- Authors
- Somasundaran, S., & Wiebe, J.
- Affiliations
- University of Pittsburgh (US)
- Venue
- Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - ACL-IJCNLP '09
- Published
- 2009
- Volume
- 1
- Pages
- 226
- OpenAlex
- W2150850338
- Cited by
- 303
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 11 · 2022: 10 · 2021: 19 · 2020: 26 · 2019: 20 · 2018: 22 · 2017: 38 · 2016: 38 · 2015: 36 · 2014: 22 · 2013: 19 · 2012: 18
- FWCI
- 8.97
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Funders
- U.S. Department of Homeland Security
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- stance detection, online debate, web mining, association rule mining, discourse information, integer linear programming, sentiment analysis
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