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Detecting Agreement and Disagreement in Political Debates

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Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10899))

Abstract

In this paper, the task of agreement/disagreement detection in political debates is studied. The main goal of this study is to detect agreement/disagreement between two individuals on a topic based on their conversations. This is a challenging task due to the lack of annotated corpora in this field. A self-labeling method is introduced for data collection and generating the training data. A new approach based on text classification is proposed for this task. The experimental results on Canadian Parliamentary debates and the United State 1960 Presidential Campaign datasets have proven the efficiency of the developed methodology and outperforms the baseline methodologies. In addition, the validity of the proposed self-labeling method is evaluated, and its efficiency is confirmed.

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References

  1. Abbott, R., Walker, M., Anand, P., Fox Tree, J.E., Bowmani, R., King, J.: How can you say such things?!?: recognizing disagreement in informal political argument. In: Proceedings of the Workshop on Languages in Social Media, pp. 2–11. Association for Computational Linguistics (2011)

    Google Scholar 

  2. 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 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, pp. 1–9. Association for Computational Linguistics (2011)

    Google Scholar 

  3. Andreas, J., Rosenthal, S., McKeown, K.: Annotating agreement and disagreement in threaded discussion. In: LREC, pp. 818–822. Citeseer (2012)

    Google Scholar 

  4. Awadallah, R., Ramanath, M., Weikum, G.: Language-model-based pro/con classification of political text. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 747–748. ACM (2010)

    Google Scholar 

  5. Blitzer, J., Dredze, M., Pereira, F., et al.: Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: ACL, vol. 7, pp. 440–447 (2007)

    Google Scholar 

  6. Burfoot, C.: Using multiple sources of agreement information for sentiment classification of political transcripts. In: Australasian Language Technology Association Workshop, vol. 6, pp. 11–18 (2008)

    Google Scholar 

  7. Galley, M., McKeown, K., Hirschberg, J., Shriberg, E.: Identifying agreement and disagreement in conversational speech: use of Bayesian networks to model pragmatic dependencies. In: Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, p. 669. Association for Computational Linguistics (2004)

    Google Scholar 

  8. Menini, S., Tonelli, S.: Agreement and disagreement: Comparison of points of view in the political domain. In: Proceedings of the 26th International Conference on Computational Linguistics, pp. 2461–2470 (2016)

    Google Scholar 

  9. Misra, A., Walker, M.A.: Topic independent identification of agreement and disagreement in social media dialogue. In: Conference of the Special Interest Group on Discourse and Dialogue, p. 920 (2013)

    Google Scholar 

  10. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  11. Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic inquiry and word count: LIWC 2001, vol. 71. Lawrence Erlbaum Associates, Mahway (2001)

    Google Scholar 

  12. Somasundaran, S., Wiebe, J.: 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, pp. 116–124. Association for Computational Linguistics (2010)

    Google Scholar 

  13. Thomas, M., Pang, B., Lee, L.: Get out the vote: determining support or opposition from congressional floor-debate transcripts. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 327–335. Association for Computational Linguistics (2006)

    Google Scholar 

  14. Turney, P.D., Pantel, P.: From frequency to meaning: vector space models of semantics. J. Artif. Intell. Res. 37, 141–188 (2010)

    MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Ontario Institute of Technology, Oshawa, ON, L1H 7K4, Canada

    Mahboubeh Ahmadalinezhad & Masoud Makrehchi

Authors
  1. Mahboubeh Ahmadalinezhad
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  2. Masoud Makrehchi
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Corresponding author

Correspondence to Mahboubeh Ahmadalinezhad .

Editor information

Editors and Affiliations

  1. United States Military Academy, West Point, New York, USA

    Robert Thomson

  2. Bucknell University, Lewisburg, Pennsylvania, USA

    Christopher Dancy

  3. The Ohio State University, Columbus, Ohio, USA

    Ayaz Hyder

  4. University of Michigan–Flint, Flint, Michigan, USA

    Halil Bisgin

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Ahmadalinezhad, M., Makrehchi, M. (2018). Detecting Agreement and Disagreement in Political Debates. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2018. Lecture Notes in Computer Science(), vol 10899. Springer, Cham. https://doi.org/10.1007/978-3-319-93372-6_6

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  • DOI: https://doi.org/10.1007/978-3-319-93372-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93371-9

  • Online ISBN: 978-3-319-93372-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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