Determining the Polarity and Source of Opinions Expressed in Political Debates

  • Alexandra Balahur
  • Zornitsa Kozareva
  • Andrés Montoyo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5449)

Abstract

In this paper we investigate different approaches we developed in order to classify opinion and discover opinion sources from text, using affect, opinion and attitude lexicon. We apply these approaches on the discussion topics contained in a corpus of American Congressional speech data. We propose three approaches to classifying opinion at the speech segment level, firstly using similarity measures to the affect, opinion and attitude lexicon, secondly dependency analysis and thirdly SVM machine learning. Further, we study the impact of taking into consideration the source of opinion and the consistency in the opinion expressed, and propose three methods to classify opinion at the speaker intervention level, showing improvements over the classification of individual text segments. Finally, we propose a method to identify the party the opinion belongs to, through the identification of specific affective and non-affective lexicon used in the argumentations. We present the results obtained when evaluating the different methods we developed, together with a discussion on the issues encountered and some possible solutions. We conclude that, even at a more general level, our approach performs better than trained classifiers on specific data.

Keywords

opinion mining opinion source mining LSA political discourse 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pang, B., Lee, L.: Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)CrossRefGoogle Scholar
  2. 2.
    Balahur, A., Lloret, E., Ferrández, O., Montoyo, A., Palomar, M., Muñoz, R.: The DLSIUAES Team’s Participation in the TAC 2008 Tracks. In: Proceedings of the Text Analysis Conference 2008 Workshop, Washington, USA (2008)Google Scholar
  3. 3.
    Thomas, M., Pang, B., Lee, L.: Get out the vote: Determining support or opposition from Congressional floor-debate transcripts. In: Proceedings of EMNLP 2006 (2006)Google Scholar
  4. 4.
    Mullen, T., Malouf, R.: A preliminary investigation into sentiment analysis of informal political discourse. In: AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW), pp. 159–162 (2006)Google Scholar
  5. 5.
    Turney, P., Mullen, T., Malouf, R.: A preliminary investigation into sentiment analysis of informal political discourse. In: Proceedings of the AAAI Symposium on Computational Approaches to Analysing Weblogs (AAAI-CAAW 2002) (2002)Google Scholar
  6. 6.
    Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86 (2002)Google Scholar
  7. 7.
    Dave, K., Lawrence, S., Pennock, D.: Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews. In: Proceedings of WWW 2003 (2003)Google Scholar
  8. 8.
    Gamon, M.: Sentiment classification on customer feedback data: Noisy data, large feature vectors, and the role of linguistic analysis. In: Proceedings of the International Conference on Computational Linguistics (COLING) (2004)Google Scholar
  9. 9.
    Matsumoto, S., Takamura, H., Okumura, M.: Sentiment classification using word sub-sequences and dependency sub-trees. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS, vol. 3518, pp. 301–311. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Ng, V., Dasgupta, S., Arifin, S.M.N.: Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews. In: Proceedings of the COLING/ACL Main Conference Poster Sessions, July 2006, pp. 611–618. Association for Computational Linguistics, Sydney (2006)CrossRefGoogle Scholar
  11. 11.
    Laver, M., Benoit, K., Garry, J.: Extracting policy positions from political texts using wor words as data. American Political Science Review 97, 311–331 (2003)CrossRefGoogle Scholar
  12. 12.
    Martin, L.W., Vanberg, G.: A robust transformation procedure for interpreting political text. Political Analysis 16, 93–100 (2008)CrossRefGoogle Scholar
  13. 13.
    Lin, W.-H., Wilson, T., Wiebe, J., Hauptmann, A.: Which side are you on? Identifying perspectives at the document and sentence levels. In: Lin, et al. (eds.) Proceedings of the Conference on Natural Language Learning (CoNLL 2006) (2006)Google Scholar
  14. 14.
    Strapparava, C., Valitutti, A.: WordNet-Affect: an affective extension of WordNet. In: Proceedings ofthe 4th International Conference on Language Resources and Evaluation (LREC 2004), Lisbon, pp. 1083–1086 (May 2004)Google Scholar
  15. 15.
    Scherer, K., Wallbott, H.G.: The ISEAR Questionnaire and Codebook (1997)Google Scholar
  16. 16.
    Balahur, A., Montoyo, A.: An Incremental Multilingual Approach to Forming a Culture Dependent Emotion Triggers Database. In: Proceedings of the 8th International Conference on Terminology and Knowledge Engineering (TKE 2008), Copenhagen (2008)Google Scholar
  17. 17.
    Balahur, A., Montoyo, A.: Multilingual Feature–driven Opinion Mining and Summarization from Customer Reviews. In: Kapetanios, E., Sugumaran, V., Spiliopoulou, M. (eds.) NLDB 2008. LNCS, vol. 5039, pp. 345–346. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Alexandra Balahur
    • 1
  • Zornitsa Kozareva
    • 1
  • Andrés Montoyo
    • 1
  1. 1.Department of Software and Computing SystemsUniversity of AlicanteAlicanteSpain

Personalised recommendations