VODUM: A Topic Model Unifying Viewpoint, Topic and Opinion Discovery

  • Thibaut Thonet
  • Guillaume Cabanac
  • Mohand Boughanem
  • Karen Pinel-Sauvagnat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9626)

Abstract

The surge of opinionated on-line texts provides a wealth of information that can be exploited to analyze users’ viewpoints and opinions on various topics. This article presents VODUM, an unsupervised Topic Model designed to jointly discover viewpoints, topics, and opinions in text. We hypothesize that partitioning topical words and viewpoint-specific opinion words using part-of-speech helps to discriminate and identify viewpoints. Quantitative and qualitative experiments on the Bitterlemons collection show the performance of our model. It outperforms state-of-the-art baselines in generalizing data and identifying viewpoints. This result stresses how important topical and opinion words separation is, and how it impacts the accuracy of viewpoint identification.

References

  1. 1.
    Asuncion, A., Welling, M., Smyth, P., Teh, Y.W.: On smoothing and inference for topic models. In: Proceedings of UAI 2009, pp. 27–34 (2009)Google Scholar
  2. 2.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. In: Proceedings of NIPS 2001, pp. 601–608 (2001)Google Scholar
  3. 3.
    Fang, Y., Si, L., Somasundaram, N., Yu, Z.: Mining contrastive opinions on political texts using cross-perspective topic model. In: Proceedings of WSDM 2012, pp. 63–72 (2012)Google Scholar
  4. 4.
    Lin, W.H., Wilson, T., Wiebe, J., Hauptmann, A.: Which side are you on? Identifying perspectives at the document and sentence levels. In: Proceedings of CoNLL 2006, pp. 109–116 (2006)Google Scholar
  5. 5.
    Liu, B., Hu, M., Cheng, J.: Opinion observer: analyzing and comparing opinions on the web. In: Proceedings of WWW 2005, pp. 342–351 (2005)Google Scholar
  6. 6.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  7. 7.
    Paul, M.J., Girju, R.: Cross-cultural analysis of blogs and forums with mixed-collection topic models. In: Proceedings of EMNLP 2009, pp. 1408–1417 (2009)Google Scholar
  8. 8.
    Paul, M.J., Girju, R.: A two-dimensional topic-aspect model for discovering multi-faceted topics. In: Proceedings of AAAI 2010, pp. 545–550 (2010)Google Scholar
  9. 9.
    Paul, M.J., Zhai, C., Girju, R.: Summarizing contrastive viewpoints in opinionated text. In: Proceedings of EMNLP 2010, pp. 66–76 (2010)Google Scholar
  10. 10.
    Qiu, M., Jiang, J.: A latent variable model for viewpoint discovery from threaded forum posts. In: Proceedings of NAACL HLT 2013, pp. 1031–1040 (2013)Google Scholar
  11. 11.
    Qiu, M., Yang, L., Jiang, J.: Modeling interaction features for debate side clustering. In: Proceedings of CIKM 2013, pp. 873–878 (2013)Google Scholar
  12. 12.
    Trabelsi, A., Zaiane, O.R.: Mining contentious documents using an unsupervised topic model based approach. In: Proceedings of ICDM 2014, pp. 550–559 (2014)Google Scholar
  13. 13.
    Turney, P.D.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of ACL 2002, pp. 417–424 (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Thibaut Thonet
    • 1
  • Guillaume Cabanac
    • 1
  • Mohand Boughanem
    • 1
  • Karen Pinel-Sauvagnat
    • 1
  1. 1.IRITUniversité Paul SabatierToulouse CEDEX 9France

Personalised recommendations