Skip to main content
SpringerLink
Log in

Contrastive Reasons Detection and Clustering from Online Polarized Debates

  • Conference paper
  • First Online:
Computational Linguistics and Intelligent Text Processing (CICLing 2019)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13451))

  • 340 Accesses

Abstract

This work tackles the problem of unsupervised modeling and extraction of the main contrastive sentential reasons conveyed by divergent viewpoints on polarized issues. It proposes a pipeline approach centered around the detection and clustering of phrases, assimilated to argument facets using a novel Phrase Author Interaction Topic-Viewpoint model. The evaluation is based on the informativeness, the relevance and the clustering accuracy of extracted reasons. The pipeline approach shows a significant improvement over state-of-the-art methods in contrastive summarization on online debate datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.reddit.com/r/worldnews/comments/8ah8ys/the_us_was_the_only_un_security_council_member_to/.

References

  1. Abbott, R., Ecker, B., Anand, P., Walker, M.A.: Internet argument corpus 2.0: an SQL schema for dialogic social media and the corpora to go with it. In: LREC (2016)

    Google Scholar 

  2. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  3. Boltužić, F., Šnajder, J.: Back up your stance: recognizing arguments in online discussions. In: Proceedings of the First Workshop on Argumentation Mining, Baltimore, Maryland, pp. 49–58. Association for Computational Linguistics (2014). https://www.aclweb.org/anthology/W14-2107

  4. Boltužić, F., Šnajder, J.: Identifying prominent arguments in online debates using semantic textual similarity. In: Proceedings of the 2nd Workshop on Argumentation Mining, Denver, CO, pp. 110–115. Association for Computational Linguistics (2015). https://www.aclweb.org/anthology/W15-0514

  5. Bouma, G.: Normalized (pointwise) mutual information in collocation extraction. In: Proceedings of GSCL, pp. 31–40 (2009)

    Google Scholar 

  6. El-Kishky, A., Song, Y., Wang, C., Voss, C.R., Han, J.: Scalable topical phrase mining from text corpora. Proc. VLDB Endow. 8(3), 305–316 (2014). https://doi.org/10.14778/2735508.2735519

    Article  Google Scholar 

  7. Erkan, G., Radev, D.R.: LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. (JAIR) 22(1), 457–479 (2004)

    Article  Google Scholar 

  8. Habernal, I., Gurevych, I.: Argumentation mining in user-generated web discourse. Comput. Linguist. 43(1), 125–179 (2017)

    Article  MathSciNet  Google Scholar 

  9. Hasan, K.S., Ng, V.: 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, pp. 751–762. Association for Computational Linguistics (2014). https://www.aclweb.org/anthology/D14-1083

  10. Li, H., Mukherjee, A., Si, J., Liu, B.: Extracting verb expressions implying negative opinions. In: Proceedings of the AAAI Conference on Artificial Intelligence (2015). https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9398

  11. Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: Marie-Francine Moens, S.S. (ed.) Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, Barcelona, Spain, pp. 74–81. Association for Computational Linguistics (2004)

    Google Scholar 

  12. Misra, A., Anand, P., Fox Tree, J.E., Walker, M.: Using summarization to discover argument facets in online idealogical dialog. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Denver, Colorado, pp. 430–440. Association for Computational Linguistics (2015). https://www.aclweb.org/anthology/N15-1046

  13. Misra, A., Oraby, S., Tandon, S., Ts, S., Anand, P., Walker, M.A.: Summarizing dialogic arguments from social media. In: Proceedings of the 21th Workshop on the Semantics and Pragmatics of Dialogue (SemDial 2017), pp. 126–136 (2017)

    Google Scholar 

  14. Mohammad, S.M., Sobhani, P., Kiritchenko, S.: Stance and sentiment in tweets. ACM Trans. Internet Technol. 17(3), 26:1–26:23 (2017). https://doi.org/10.1145/3003433

  15. Park, J., Cardie, C.: Identifying appropriate support for propositions in online user comments. In: Proceedings of the First Workshop on Argumentation Mining, Baltimore, Maryland, pp. 29–38. Association for Computational Linguistics (2014). https://www.aclweb.org/anthology/W14-2105

  16. Paul, M., Zhai, C., Girju, R.: Summarizing contrastive viewpoints in opinionated text. In: Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, Cambridge, MA, pp. 66–76. Association for Computational Linguistics (2010). https://www.aclweb.org/anthology/D10-1007

  17. Qiu, M., Jiang, J.: A latent variable model for viewpoint discovery from threaded forum posts. In: Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Atlanta, Georgia, pp. 1031–1040. Association for Computational Linguistics (2013). https://www.aclweb.org/anthology/N13-1123

  18. Stab, C., Gurevych, I.: Identifying argumentative discourse structures in persuasive essays. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 46–56. Association for Computational Linguistics (2014). https://www.aclweb.org/anthology/D14-1006

  19. Swanson, R., Ecker, B., Walker, M.: Argument mining: extracting arguments from online dialogue. In: Proceedings of the 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Prague, Czech Republic, pp. 217–226. Association for Computational Linguistics (2015). https://aclweb.org/anthology/W15-4631

  20. Thonet, T., Cabanac, G., Boughanem, M., Pinel-Sauvagnat, K.: VODUM: a topic model unifying viewpoint, topic and opinion discovery. In: Ferro, N., et al. (eds.) ECIR 2016. LNCS, vol. 9626, pp. 533–545. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-30671-1_39

    Chapter  Google Scholar 

  21. Trabelsi, A., Zaiane, O.R.: Finding arguing expressions of divergent viewpoints in online debates. In: Proceedings of the 5th Workshop on Language Analysis for Social Media (LASM), Gothenburg, Sweden, pp. 35–43. Association for Computational Linguistics (2014). https://www.aclweb.org/anthology/W14-1305

  22. Trabelsi, A., Zaïane, O.R.: A joint topic viewpoint model for contention analysis. In: Métais, E., Roche, M., Teisseire, M. (eds.) Natural Language Processing and Information Systems, pp. 114–125. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07983-7_16

    Chapter  Google Scholar 

  23. Trabelsi, A., Zaiane, O.R.: Mining contentious documents using an unsupervised topic model based approach. In: Proceedings of the 2014 IEEE International Conference on Data Mining, pp. 550–559 (2014)

    Google Scholar 

  24. Trabelsi, A., Zaïane, O.R.: Extraction and clustering of arguing expressions in contentious text. Data Knowl. Eng. 100, 226–239 (2015)

    Article  Google Scholar 

  25. Trabelsi, A., Zaïane, O.R.: Mining contentious documents. Knowl. Inf. Syst. 48(3), 537–560 (2016)

    Article  Google Scholar 

  26. Trabelsi, A., Zaïane, O.R.: Unsupervised model for topic viewpoint discovery in online debates leveraging author interactions. In: Proceedings of the AAAI International Conference on Web and Social Media (ICWSM), Stanford, California, pp. 425–433. Association for the Advancement of Artificial Intelligence (2018)

    Google Scholar 

  27. Vilares, D., He, Y.: Detecting perspectives in political debates. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, pp. 1573–1582. Association for Computational Linguistics (2017). https://www.aclweb.org/anthology/D17-1165

Download references

Author information

Authors and Affiliations

  1. Department of Computing Science, University of Alberta, Edmonton, Canada

    Amine Trabelsi & Osmar R. Zaïane

Authors
  1. Amine Trabelsi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Osmar R. Zaïane
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Amine Trabelsi .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Trabelsi, A., Zaïane, O.R. (2023). Contrastive Reasons Detection and Clustering from Online Polarized Debates. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2019. Lecture Notes in Computer Science, vol 13451. Springer, Cham. https://doi.org/10.1007/978-3-031-24337-0_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-24337-0_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24336-3

  • Online ISBN: 978-3-031-24337-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation