Classification of the Stance in Online Debates Using the Dependency Relations Feature

Authors

  • Thiri Kyaw
  • Sint Sint Aung

Keywords:

stance classification, sentiment analysis, opinion mining, machine learning

Abstract

Online discussion forums offer Internet users a medium for discussions about current political debates. The debate is a system of claims regarding interactivity and representation. Users make claims in an online discussion with superior content to support their position. Factual accuracy and emotional appeal are critical attributes used to convince readers. A key challenge in debate forums is to identify the participants’ stance, each of which is inter-dependent and inter-connected. This research work aims to construct a classifier that takes the linguistic features of the posts as input and outputs predictions for the stance label of each post. Three types of features which include Lexical, Dependency, and Morphology are used to detect the stance of the posts. Lexical features such as cue words are employed as surface features, and deep features include dependency and morphology features. Multinomial Naïve Bayes classifier is used to build a model for classifying stance and the Chi-Square method is used to select the good feature set. The performance of the stance classification system is evaluated in terms of accuracy. The result of stance labels for this proposed research represents as for and against by analyzing the surface and deep features that capture the content of a post.

References

. S. Somasundaran and J. Wiebe, “Recognizing stances in online debates,” in 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, Suntec, Singapore, 2009, vol. 1, p. 226, doi: 10.3115/1687878.1687912.

. S. Somasundaran and J. Wiebe, “Recognizing Stances in Ideological On-Line Debates,” in Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text, 2010, pp. 116-124.

. P. Anand, M. Walker, R. Abbott, J. E. F. Tree, R. Bowmani, and M. Minor, “Cats Rule and Dogs Drool!: Classifying Stance in Online Debate,” in Proceedings of the 2nd workshop on computational approaches to subjectivity and sentiment analysis, 2011, pp. 1-9.

. M. Walker, P. Anand, R. Abbott, and R. Grant, “Stance Classification using Dialogic Properties of Persuasion,” in Proceedings of the 2012 conference of the North American chapter of the association for computational linguistics: Human language technologies, 2012, pp. 592-596.

. K. S. Hasan and V. Ng, “Why are You Taking this Stance? Identifying and Classifying Reasons in Ideological Debates,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 2014, pp. 751–762, doi: 10.3115/v1/D14-1083.

. D. Sridhar, L. Getoor, and M. Walker, “Collective stance classification of posts in online debate forums,” in Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media, 2014, pp. 109-117.

. A. Murakami and R. Raymond, “Support or Oppose? Classifying Positions in Online Debates from Reply Activities and Opinion Expressions,” in Proceedings of the 23rd International Conference on Computational Linguistics: Posters, 2010, pp. 869-875.

. K. S. Hasan and V. Ng, “Extra-Linguistic Constraints on Stance Recognition in Ideological Debates,” in Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2013, pp. 816-821.

. P. Sobhani, D. Inkpen, and S. Matwin, “From argumentation mining to stance classification,” in Proceedings of the 2nd Workshop on Argumentation Mining, 2015, pp. 67-77.

. L. Wang, and C. Cardie, “Improving agreement and disagreement identification in online discussions with a socially-tuned sentiment lexicon,” arXiv preprint arXiv:1606.05706, 2016.

. M.A. Walker, J.E.F. Tree, P. Anand, R. Abbott, and J. King, “A Corpus for Research on Deliberation and Debate,” in LREC, 2012, Vol. 12, pp. 812-817.

. M. Joshi and C. Rosé, “Generalizing dependency features for opinion mining,” in Proceedings of the ACL-IJCNLP 2009 conference short papers, 2009, pp. 313-316.

. A. Mandya, A. Siddharthan, and A. Wyner, “Scrutable Feature Sets for Stance Classification,” in Proceedings of the Third Workshop on Argument Mining (ArgMining2016), Berlin, Germany, 2016, pp. 60–69, doi: 10.18653/v1/W16-2807.

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Published

2020-07-20

How to Cite

Kyaw, T. ., & Aung, S. S. . (2020). Classification of the Stance in Online Debates Using the Dependency Relations Feature. International Journal of Computer (IJC), 38(1), 153–163. Retrieved from https://ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1669

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Articles