Advertisement

Finding and Quantifying Temporal-Aware Contradiction in Reviews

  • Ismail BadacheEmail author
  • Sébastien Fournier
  • Adrian-Gabriel Chifu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10648)

Abstract

Opinions (reviews) on web resources (e.g., courses, movies), generated by users, become increasingly exploited in text analysis tasks, the detection of contradictory opinions being one of them. This paper focuses on the quantification of sentiment-based contradictions around specific aspects in reviews. However, it is necessary to study the contradictions with respect to the temporal dimension of reviews (their sessions). In general, for web resources such as online courses (e.g. coursera or edX), reviews are often generated during the course sessions. Between sessions, users stop reviewing courses, and there are chances that courses will be updated. So, in order to avoid the confusion of contradictory reviews coming from two or more different sessions, the reviews related to a given resource should be firstly grouped according to their corresponding session. Secondly, aspects are identified according to the distributions of the emotional terms in the vicinity of the most frequent nouns in the reviews collection. Thirdly, the polarity of each review segment containing an aspect is estimated. Then, only resources containing these aspects with opposite polarities are considered. Finally, the contradiction intensity is estimated based on the joint dispersion of polarities and ratings of the reviews containing aspects. The experiments are conducted on the Massive Open Online Courses data set containing 2244 courses and their 73,873 reviews, collected from coursera.org. The results confirm the effectiveness of our approach to find and quantify contradiction intensity.

Keywords

Sentiment analysis Aspect detection Contradiction intensity 

Notes

Acknowledgement

The project leading to this publication has received funding from Excellence Initiative of Aix-Marseille University - A*MIDEX, a French “Investissements d’Avenir” programme.

References

  1. 1.
    Badache, I., Boughanem, M.: Social priors to estimate relevance of a resource. In: IIiX, pp. 106–114 (2014)Google Scholar
  2. 2.
    Badache, I., Boughanem, M.: Fresh and diverse social signals: any impacts on search? In: CHIIR, pp. 155–164 (2017)Google Scholar
  3. 3.
    De Marneffe, M-C., Rafferty, A., Manning, C.: Finding contradictions in text. In: ACL, vol. 8, pp. 1039–1047 (2008)Google Scholar
  4. 4.
    Dori-Hacohen, S., Allan, J.: Automated controversy detection on the web. In: Hanbury, A., Kazai, G., Rauber, A., Fuhr, N. (eds.) ECIR 2015. LNCS, vol. 9022, pp. 423–434. Springer, Cham (2015). doi: 10.1007/978-3-319-16354-3_46 Google Scholar
  5. 5.
    Ennals, R., Byler, D., Agosta, J.M., Rosario, B.: What is disputed on the web? In: WICOW, pp. 67–74 (2010)Google Scholar
  6. 6.
    Hamdan, H., Bellot, P., Bechet, F.: Lsislif: Crf and logistic regression for opinion target extraction and sentiment polarity analysis. In: SemEval, pp. 753–758 (2015)Google Scholar
  7. 7.
    Harabagiu, S., Hickl, A., Lacatusu, F.: Negation, contrast and contradiction in text processing. In: AAAI, vol. 6, pp. 755–762 (2006)Google Scholar
  8. 8.
    Hassan, A., Abu-Jbara, A., Radev, D.: Detecting subgroups in online discussions by modeling positive and negative relations among participants. In: EMNLP (2012)Google Scholar
  9. 9.
    Htait, A., Fournier, S., Bellot, P.: Using web search engines for English and Arabic unsupervised sentiment intensity prediction. In: SemEval (2016)Google Scholar
  10. 10.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: KDD (2004)Google Scholar
  11. 11.
    Jang, M., Allan, J.: Improving automated controversy detection on the web. In: SIGIR, pp. 865–868 (2016)Google Scholar
  12. 12.
    Kim, S., Zhang, J., Chen, Z., Oh, A., Liu, S.: A hierarchical aspect-sentiment model for online reviews. In: AAAI (2013)Google Scholar
  13. 13.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Berkeley Symposium on Mathematical Statistics and Probability (1967)Google Scholar
  14. 14.
    Mohammad, S.M., Kiritchenko, S., Zhu, X.: Nrc-canada: Building the state-of-the-art in sentiment analysis of tweets. In: SemEval (2013)Google Scholar
  15. 15.
    Mukherjee, A., Liu, B.: Mining contentions from discussions and debates. In: KDD, pp. 841–849 (2012)Google Scholar
  16. 16.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: EMNLP, pp. 79–86 (2002)Google Scholar
  17. 17.
    Poria, S., Cambria, E., Ku, L., Gui, C., Gelbukh, A.: A rule-based approach to aspect extraction from product reviews. In: SocialNLP (2014)Google Scholar
  18. 18.
    Qiu, M., Yang, L., Jiang, J.: Modeling interaction features for debate side clustering. In: CIKM, pp. 873–878 (2013)Google Scholar
  19. 19.
    Socher, R., Perelygin, A., Wu, J.Y., Chuang, J., Manning, C.D., Ng, A.Y., Potts, C.: Recursive deep models for semantic compositionality over a sentiment treebank. In: EMNLP, vol. 1631, p. 1642 (2013)Google Scholar
  20. 20.
    Titov, I., McDonald, R.: Modeling online reviews with multi-grain topic models. In: WWW, pp. 111–120 (2008)Google Scholar
  21. 21.
    Tsytsarau, M., Palpanas, T., Denecke, K.: Scalable discovery of contradictions on the web. In: WWW, pp. 1195–1196. ACM (2010)Google Scholar
  22. 22.
    Tsytsarau, M., Palpanas, T., Denecke, K.: Scalable detection of sentiment-based contradictions. DiversiWeb, WWW (2011)Google Scholar
  23. 23.
    Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: ACL, pp. 417–424 (2002)Google Scholar
  24. 24.
    Wang, L., Cardie, C.: A piece of my mind: a sentiment analysis approach for online dispute detection. In: ACL, pp. 693–699 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ismail Badache
    • 1
    Email author
  • Sébastien Fournier
    • 1
  • Adrian-Gabriel Chifu
    • 1
  1. 1.LSIS UMR 7296 CNRSUniversity Aix-MarseilleMarseilleFrance

Personalised recommendations