Automatically Determining Attitude Type and Force for Sentiment Analysis

  • Shlomo Argamon
  • Kenneth Bloom
  • Andrea Esuli
  • Fabrizio Sebastiani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5603)

Abstract

Recent work in sentiment analysis has begun to apply fine-grained semantic distinctions between expressions of attitude as features for textual analysis. Such methods, however, require the construction of large and complex lexicons, giving values for multiple sentiment-related attributes to many different lexical items. For example, a key attribute is what type of attitude is expressed by a lexical item; e.g., beautiful expresses appreciation of an object’s quality, while evil expresses a negative judgment of social behavior. In this chapter we describe a method for the automatic determination of complex sentiment-related attributes such as attitude type and force, by applying supervised learning to WordNet glosses. Experimental results show that the method achieves good effectiveness, and is therefore well-suited to contexts in which these lexicons need to be generated from scratch.

Keywords

Sentiment analysis Lexicon learning WordNet Appraisal theory 

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References

  1. 1.
    Turney, P.D., Littman, M.L.: Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems 21(4), 315–346 (2003)CrossRefGoogle Scholar
  2. 2.
    Taboada, M., Grieve, J.: Analyzing appraisal automatically. In: Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications (2004)Google Scholar
  3. 3.
    Whitelaw, C., Garg, N., Argamon, S.: Using appraisal groups for sentiment analysis. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM 2005), Bremen, DE, pp. 625–631 (2005)Google Scholar
  4. 4.
    Wilson, T., Wiebe, J., Hwa, R.: Just how mad are you? Finding strong and weak opinion clauses. In: Proceedings of the 21st Conference of the American Association for Artificial Intelligence (AAAI 2004), San Jose, US, pp. 761–769 (2004)Google Scholar
  5. 5.
    Martin, J.R., White, P.R.: The Language of Evaluation: Appraisal in English. Palgrave, London (2005)Google Scholar
  6. 6.
    Bloom, K., Argamon, S., Garg, N.: Extracting appraisal expressions. In: Proceedings of NAACL-HLT 2007, pp. 308–315 (2007)Google Scholar
  7. 7.
    Esuli, A., Sebastiani, F.: Determining the semantic orientation of terms through gloss analysis. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM 2005), Bremen, DE, pp. 617–624 (2005)Google Scholar
  8. 8.
    Argamon, S., Whitelaw, C., Chase, P., Hota, S., Garg, N., Levitan, S.: Stylistic Text Classification Using Functional Lexical Features. Journal of the American Society for Information Science and Technology 58(6), 802–822 (2007)CrossRefGoogle Scholar
  9. 9.
    Osgood, C., Suci, G., Tannenbaum, P.: The measurement of meaning. University of Illinois Press, Urbana (1957)Google Scholar
  10. 10.
    Kamps, J., Marx, M.: Words with attitude. In: Proceedings of the 1st Global WordNet (GWC 2002) Conference, Mysore, IN, pp. 332–341 (2002)Google Scholar
  11. 11.
    Mullen, T., Collier, N.: Sentiment analysis using support vector machines with diverse information sources. In: Proceedings of the 9th Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), Barcelona, ES, pp. 412–418 (2004)Google Scholar
  12. 12.
    Stone, P.J., Dunphy, D.C., Smith, M.S., Ogilvie, D.M.: The General Inquirer: A Computer Approach to Content Analysis. The MIT Press, Cambridge (1966)Google Scholar
  13. 13.
    Crammer, K., Singer, Y.: Pranking with ranking. In: Advances in Neural Information Processing Systems, vol. 14, pp. 641–647. MIT Press, Cambridge (2002)Google Scholar
  14. 14.
    Esuli, A., Fagni, T., Sebastiani, F.: Boosting multi-label hierarchical text categorization. Information Retrieval 11(4), 287–313 (2008)CrossRefGoogle Scholar
  15. 15.
    Esuli, A., Sebastiani, F.: Determining term subjectivity and term orientation for opinion mining. In: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2006), Trento, IT, pp. 193–200 (2006)Google Scholar
  16. 16.
    Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics (ACL 1997), Madrid, ES, pp. 174–181 (1997)Google Scholar
  17. 17.
    Takamura, H., Inui, T., Okumura, M.: Extracting emotional polarity of words using spin model. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL 2005), Ann Arbor, US, pp. 133–140 (2005)Google Scholar
  18. 18.
    Kamps, J., Marx, M., Mokken, R.J., De Rijke, M.: Using WordNet to measure semantic orientation of adjectives. In: Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC 2004), Lisbon, PT, vol. IV, pp. 1115–1118 (2004)Google Scholar
  19. 19.
    Kim, S.M., Hovy, E.: Determining the sentiment of opinions. In: Proceedings of the 20th International Conference on Computational Linguistics (COLING 2004), Geneva, CH, pp. 1367–1373 (2004)Google Scholar
  20. 20.
    Andreevskaia, A., Bergler, S.: Mining WordNet for fuzzy sentiment: Sentiment tag extraction from WordNet glosses. In: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2006), Trento, IT, pp. 209–216 (2006)Google Scholar
  21. 21.
    Baroni, M., Vegnaduzzo, S.: Identifying subjective adjectives through Web-based mutual information. In: Proceedings of the 7th Konferenz zur Verarbeitung Natürlicher Sprache (German Conference on Natural Language Processing) (KONVENS 2004), Vienna, AU, pp. 17–24 (2004)Google Scholar
  22. 22.
    Riloff, E., Wiebe, J., Wilson, T.: Learning subjective nouns using extraction pattern bootstrapping. In: Proceedings of the 7th Conference on Natural Language Learning (CONLL 2003), Edmonton, CA, pp. 25–32 (2003)Google Scholar
  23. 23.
    Wiebe, J.: Learning subjective adjectives from corpora. In: Proceedings of the 17th Conference of the American Association for Artificial Intelligence (AAAI 2000), Austin, US, pp. 735–740 (2000)Google Scholar
  24. 24.
    Esuli, A., Sebastiani, F.: SentiWordNet: A publicly available lexical resource for opinion mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation (LREC 2006), Genova, IT, pp. 417–422 (2006)Google Scholar
  25. 25.
    Kennedy, A., Inkpen, D.: Sentiment classification of movie reviews using contextual valence shifters. Computational Intelligence 22, 110–125 (2006)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Miyoshi, T., Nakagami, Y.: Sentiment classification of customer reviews on electric products. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 2028–2033 (2007)Google Scholar
  27. 27.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: HLT 2005: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, Morristown, NJ, USA, pp. 347–354. Association for Computational Linguistics (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Shlomo Argamon
    • 1
  • Kenneth Bloom
    • 1
  • Andrea Esuli
    • 2
  • Fabrizio Sebastiani
    • 2
  1. 1.Linguistic Cognition Laboratory Department of Computer ScienceIllinois Institute of TechnologyChicagoUSA
  2. 2.Istituto di Scienza e Tecnologie dell’InformazioneConsiglio Nazionale delle RicerchePisaItaly

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