Abstract
Online debate sites act as a popular platform for users to express and form opinions. In this paper, we propose a novel unsupervised approach to perform stance classification of two-sided online debate posts. We propose the use of word embeddings to address the problem of identifying the preferred target of each aspect. We also use word embeddings to train a supervised classifier for selecting only target related aspects. The aspect-target preference information is used to model the stance classification task as an integer linear programming problem. The classifier gives an average aspect classification accuracy of 84% on multiple datasets. Our word embedding based stance classification approach gives 19.80% higher user stance classification accuracy (F1-score) compared to the existing methods. Our results suggest that the use of word embeddings improves accuracy and enables us to perform stance classification without the need for external domain-specific information.
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Notes
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http://www.convinceme.net, http://www.createdebate.com, and http://www.debate.org are some of the popular social debate sites.
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References
Anand, P., Walker, M., Abbott, R., Tree, J.E.F., Bowmani, R., Minor, M.: Cats rule and dogs drool! classifying stance in online debate. In: Proceedings of the Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, pp. 1–9. ACL (2011)
Burfoot, C., Bird, S., Baldwin, T.: Collective classification of congressional floor-debate transcripts. In: Proceedings of ACL HLT 2011, pp. 1506–1515. ACL (2011)
Ghosh, S., Anand, K., Rajanala, S., Reddy, A.B., Singh, M.: Unsupervised stance classification in online debates. In: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, pp. 30–36. ACM (2018)
Hu, M., Liu, B.: Mining opinion features in customer reviews. In: Proceedings of AAAI 2004, vol. 4, pp. 755–760 (2004)
Krejzl, P., Steinberger, J.: Uwb at semeval-2016 task 6: stance detection. In: Proceedings of SemEval@ NAACL-HLT, pp. 408–412 (2016)
Levy, O., Goldberg, Y.: Dependency-based word embeddings. In: Proceedings of ACL 2014, pp. 302–308 (2014)
Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of WWW 2005, pp. 342–351. ACM (2005)
Liu, Q., Liu, B., Zhang, Y., Kim, D.S., Gao, Z.: Improving opinion aspect extraction using semantic similarity and aspect associations. In: Proceedings of AAAI 2016, pp. 2986–2992. AAAI Press (2016)
Lu, Y., Wang, H., Zhai, C., Roth, D.: Unsupervised discovery of opposing opinion networks from forum discussions. In: Proceedings of CIKM 2012, pp. 1642–1646. ACM (2012)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of NIPS 2013, pp. 3111–3119 (2013)
Mohammad, S.M.: # emotional tweets. In: Proceedings of SemEval 2012, pp. 246–255. ACL (2012)
Mohammad, S.M., Turney, P.D.: NRC emotion lexicon. NRC Technical report (2013)
Pfohl, S.R., Triebe, O.: Stance detection for the fake news challenge with attention and conditional encoding. In: Stanford CS224d Deep Learning for NLP final project (2017)
Somasundaran, S., Wiebe, J.: Recognizing stances in online debates. In: Proceedings of ACL-IJCNLP 2009, pp. 226–234. ACL (2009)
Thomas, M., Pang, B., Lee, L.: Get out the vote: determining support or opposition from congressional floor-debate transcripts. In: Proceedings of EMNLP 2006, pp. 327–335. ACL (2006)
Walker, M.A., Anand, P., Abbott, R., Grant, R.: Stance classification using dialogic properties of persuasion. In: Proceedings of ACL 2012, pp. 592–596. ACL (2012)
Xu, L., Liu, K., Zhao, J., et al.: Joint opinion relation detection using one-class deep neural network. In: Proceedings of the COLING 2014, vol. 14, pp. 677–687 (2014)
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Konjengbam, A., Ghosh, S., Kumar, N., Singh, M. (2018). Debate Stance Classification Using Word Embeddings. In: Ordonez, C., Bellatreche, L. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2018. Lecture Notes in Computer Science(), vol 11031. Springer, Cham. https://doi.org/10.1007/978-3-319-98539-8_29
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DOI: https://doi.org/10.1007/978-3-319-98539-8_29
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