A Review Corpus for Argumentation Analysis

  • Henning Wachsmuth
  • Martin Trenkmann
  • Benno Stein
  • Gregor Engels
  • Tsvetomira Palakarska
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8404)

Abstract

The analysis of user reviews has become critical in research and industry, as user reviews increasingly impact the reputation of products and services. Many review texts comprise an involved argumentation with facts and opinions on different product features or aspects. Therefore, classifying sentiment polarity does not suffice to capture a review’s impact. We claim that an argumentation analysis is needed, including opinion summarization, sentiment score prediction, and others. Since existing language resources to drive such research are missing, we have designed the ArguAna TripAdvisor corpus, which compiles 2,100 manually annotated hotel reviews balanced with respect to the reviews’ sentiment scores. Each review text is segmented into facts, positive, and negative opinions, while all hotel aspects and amenities are marked. In this paper, we present the design and a first study of the corpus. We reveal patterns of local sentiment that correlate with sentiment scores, thereby defining a promising starting point for an effective argumentation analysis.

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References

  1. 1.
    Amazon Mechanical Turk, http://www.mturk.com
  2. 2.
  3. 3.
    Besnard, P., Hunter, A.: Elements of Argumentation. The MIT Press (2008)Google Scholar
  4. 4.
    Cabrio, E., Villata, S.: Combining Textual Entailment and Argumentation Theory for Supporting Online Debates Interactions. In: Proc. of the 50th ACL: Short Papers, pp. 208–212 (2012)Google Scholar
  5. 5.
    Esuli, A., Sebastiani, F.: SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining. In: Proceedings of the 5th LREC, pp. 417–422 (2006)Google Scholar
  6. 6.
    Fleiss, J.L.: Statistical Methods for Rates and Proportions, 2nd edn. John Wiley & Sons (1981)Google Scholar
  7. 7.
    Hu, M., Liu, B.: Mining and Summarizing Customer Reviews. In: Proc. of the Tenth SIGKDD, pp. 168–177 (2004)Google Scholar
  8. 8.
    Mann, W.C., Thompson, S.A.: Rhetorical Structure Theory: Toward a Functional Theory of Text Organization. Text 8(3), 243–281 (1988)Google Scholar
  9. 9.
    Mao, Y., Lebanon, G.: Isotonic Conditional Random Fields and Local Sentiment Flow. Advances in Neural Information Processing Systems 19, 961–968 (2007)Google Scholar
  10. 10.
    Mochales, R., Moens, M.F.: Argumentation Mining. AI and Law 19(1), 1–22 (2011)Google Scholar
  11. 11.
    Mukherjee, S., Bhattacharyya, P.: Sentiment Analysis in Twitter with Lightweight Discourse Analysis. In: Proc. of the 24th COLING, pp. 1847–1864 (2012)Google Scholar
  12. 12.
    Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)CrossRefGoogle Scholar
  13. 13.
    Prettenhofer, P., Stein, B.: Cross-Language Text Classification using Structural Correspondence Learning. In: Proc. of the 48th ACL, pp. 1118–1127 (2010)Google Scholar
  14. 14.
    Sapkota, U., Solorio, T., Montes-y-Gómez, M., Rosso, P.: The Use of Orthogonal Similarity Relations in the Prediction of Authorship. In: Gelbukh, A. (ed.) CICLing 2013, Part II. LNCS, vol. 7817, pp. 463–475. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Teufel, S.: Argumentative Zoning: Information Extraction from Scientific Text. Ph.D. thesis, University of Edinburgh (1999)Google Scholar
  16. 16.
    Toulmin, S.E.: The Uses of Argument. Cambridge University Press (1958)Google Scholar
  17. 17.
  18. 18.
    Wachsmuth, H., Bujna, K.: Back to the Roots of Genres: Text Classification by Language Function. In: Proc. of the 5th IJCNLP, pp. 632–640 (2011)Google Scholar
  19. 19.
    Wang, H., Lu, Y., Zhai, C.: Latent Aspect Rating Analysis on Review Text Data: A Rating Regression Approach. In: Proc. of the 16th SIGKDD, pp. 783–792 (2010)Google Scholar
  20. 20.
    Wiebe, J., Wilson, T., Cardie, C.: Annotating Expressions of Opinions and Emotions in Language. Language Resources and Evaluation 1(2) (2005)Google Scholar
  21. 21.
    Zirn, C., Niepert, M., Stuckenschmidt, H., Strube, M.: Fine-Grained Sentiment Analysis with Structural Features. In: Proc. of the 5th IJCNLP, pp. 336–344 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Henning Wachsmuth
    • 1
  • Martin Trenkmann
    • 2
  • Benno Stein
    • 2
  • Gregor Engels
    • 1
  • Tsvetomira Palakarska
    • 2
  1. 1.s-lab – Software Quality LabUniversität PaderbornPaderbornGermany
  2. 2.Bauhaus-Universität WeimarWeimarGermany

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