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A Linguistic Interpretation of the OCC Emotion Model for Affect Sensing from Text

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Affective Information Processing

Abstract

Numerous approaches have already been employed to ‘sense’ affective information from text; but none of those ever employed the OCC emotion model, an influential theory of the cognitive and appraisal structure of emotion. The OCC model derives 22 emotion types and two cognitive states as consequences of several cognitive variables. In this chapter, we propose to relate cognitive variables of the emotion model to linguistic components in text, in order to achieve emotion recognition for a much larger set of emotions than handled in comparable approaches. In particular, we provide tailored rules for textural emotion recognition, which are inspired by the rules of the OCC emotion model. Hereby, we clarify how text components can be mapped to specific values of the cognitive variables of the emotion model. The resulting linguistics-based rule set for the OCC emotion types and cognitive states allows us to determine a broad class of emotions conveyed by text.

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References

  1. Bartneck, C. (2003) Interacting with and embodied emotional character, InProceedings of DPPI(pp. 55–60). Pittsburgh.

    Google Scholar 

  2. Chengwei, Y.,&Gencai, C. (2001). An emotion development agent model based on OCC model and operant conditioning, InProceedings of ICII(pp. 246–250).

    Google Scholar 

  3. English Vocabularyhttp://www.englishclub.com/vocabulary/

  4. Esuli, A.,&Sebastiani, F. (2005). Determining the semantic orientation of terms through gloss analysis. InProceedings of CIKM(pp. 617–624).

    Google Scholar 

  5. Fellbaum, C. (Ed.) (1999).WordNet: An electronic lexical database, Cambridge, MA: MIT Press.

    Google Scholar 

  6. Fitrianie, S.,&Rothkrantz, L. J. M. (2006). Constructing knowledge for automated text-based emotion expressions, InProceedings of CompSysTech, Tarnovo, Bulgaria.

    Google Scholar 

  7. Hatzivassiloglou, V.,&McKeown, K. R. (1997). Predicting the semantic orientation of adjectives, InProceedings of the 35th Annual Meeting on Association for Computational Linguistics,Madrid (pp. 174–181).

    Google Scholar 

  8. Hatzivassiloglou, V.,&Wiebe, J. M. (2000). Effects of adjective orientation and grabability on sentence subjectivity. InProceedings of the 18th International Conference on Computational Linguistics.Hu, M.,&Liu, B. (2004). Mining and summarizing customer reviews. InProceedings of KDD(pp. 168–177).

    Google Scholar 

  9. Kamps, J.,&Marx, M. (2002). Words with attitude. InProceedings of the First International Conference on Global WordNet, Mysore, India.

    Google Scholar 

  10. Kim, S. M.,&Hovy, E. H. (2006). Identifying and analyzing judgment opinions. InProceedings of HLT-NAACL, ACL (pp. 200–207).

    Google Scholar 

  11. Liu, H., Lieberman, H.,&Selker, T. (2003). A model of textual affect sensing using real-world knowledge, InProceedings of IUI, Miami (pp. 25–132). New York: ACM Press.

    Google Scholar 

  12. Liu, H.,&Singh, P. (2004). ConceptNet: A practical commonsense reasoning toolkit.BT Technology Journal, 22(4): 211–226.

    Article  Google Scholar 

  13. Machinese Syntax (2005). The official Websitehttp://www.connexor.com/connexor/

  14. Mihalcea, R.,&Liu, H. (2006). A corpus-based approach to finding happiness, Computational approaches for analysis of weblogs, InProceedings of the AAAI Spring Symposium.

    Google Scholar 

  15. Nasukawa, T.,&Yi, J. (2003). Sentiment analysis: Capturing favorability using natural language processing, InProceedings of K-CAP(pp. 70–77). New York: ACM Press.

    Google Scholar 

  16. Opinmind (2006). Discovering bloggershttp://www.opinmind.com/

  17. Ortony, A. (2003). On making believable emotional agents believable. In R. Trappl, P. Petta,&S. Payr (Eds.)Emotions in humans and artifacts(pp. 189–211). Cambridge, MA: MIT Press.

    Google Scholar 

  18. Ortony, A., Clore, G. L.,&Collins, A. (1988). The cognitive structure of emotions, Cambridge University Press, New York, USA.

    Google Scholar 

  19. Pang, B.,&Lee, L. (2005). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. InProceedings of ACL(pp. 115–124).

    Google Scholar 

  20. Pang, B., Lee, L.,&Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. InProceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)(pp. 79–86).

    Google Scholar 

  21. Pennebaker, J. W., Mehl, M. R.,&Niederhoffer, K. (2003). Psychological aspects of natural language use: Our words, our selves.Annual Review of Psychology 54: 547–577.

    Article  Google Scholar 

  22. Picard, R. W. (1997).Affective computing. Cambridge, MA: MIT Press.

    Google Scholar 

  23. Polanyi, L.,&Zaenen, A. (2004). Contextual valence shifters. In J. Shanahan, Y. Qu,&J. Wiebe (Eds.), Computing attitude and affect in text: Theory and applicationsThe Information Retrieval Series 20: 1–10.

    Google Scholar 

  24. Prendinger, H.,&Ishizuka, M. (2005). The empathic companion: A Character-based interface that addresses user's affective states.Journal of Applied Artificial Intelligence 19(3–4): 267–285.

    Article  Google Scholar 

  25. Riloff, E., Wiebe, J.,&Wilson, T. (2003). Learning subjective nouns using extraction pattern bootstrapping. InProceedings of CoNLL.

    Google Scholar 

  26. Shaikh, M. A. M., Ishizuka, M.,&Prendinger, H. (2007). Assessing sentiment of text by semantic dependency and contextual valence analysis. InProceedings of the International Conference on Affective Computing and Intelligent InteractionLisbon (pp. 191–202, LNCS 4738, New York:Springer.

    Chapter  Google Scholar 

  27. Shaikh, M. A. M, Prendinger, H.,&Ishizuka, M. (2007). SenseNet: A linguistic tool to visualize numerical-valance based sentiment of textual data. InProceedings of ICON, Hyderabad, India (pp. 147–152).

    Google Scholar 

  28. Shanahan, J. G., Qu, Y.,&Wiebe, J. (Eds.) (2006).Computing attitude and affect in text: Theory and applications. The Netherlands: Springer.

    Google Scholar 

  29. Strapparava, C., Valitutti, A., and Stock, O. (2007). Dances with words. InProceedings of IJCAI,Hyderabad, India. New York: Springer, pp. 1719–1724.

    Google Scholar 

  30. Subasic, P.,&Huettner, A. (2001). Affect analysis of text using fuzzy semantic typingIEEE Transactions on Fuzzy Systems 9(4): 483–496.

    Article  Google Scholar 

  31. Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. InProceedings of the 40th Annual Meeting on Association for Computational Linguistics, Pennsylvania (pp. 417–424).

    Google Scholar 

  32. Turney, P. D.,&Littman, M. L. (2003). Measuring praise and criticism: Inference of semantic orientation from association.ACM Transactions on Information Systems (TOIS) 21(4): 315–346.

    Article  Google Scholar 

  33. Valitutti, A., Strapparava, C.,&Stock, O. (2004). Developing affective lexical resources.Psychology Journal 2(1): 61–83.

    Google Scholar 

  34. Wiebe, J. (2000). Learning subjective adjectives from corpora. InProceedings of the 17th National Conference on Artificial Intelligence and 12th International Conference on InnovativeApplications of Artificial Intelligence, Texas (pp. 735–740).

    Google Scholar 

  35. Wiebe, J., and Mihalcea, R. (2006). Word sense and subjectivity. InProceedings of ACL–06(pp.1065–1072).

    Google Scholar 

  36. Wilson, T., Wiebe, J.,&Hoffmann, P. (2005). Recognizing contextual polarity in phrase-level sentiment analysis. InProceedings of HLT/EMNLP, ACL (pp. 347–354).http://www.zoesis.com/mrbubb/(Mr. Bubb in Space)

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Shaikh, M.A.M., Prendinger, H., Ishizuka, M. (2009). A Linguistic Interpretation of the OCC Emotion Model for Affect Sensing from Text. In: Tao, J., Tan, T. (eds) Affective Information Processing. Springer, London. https://doi.org/10.1007/978-1-84800-306-4_4

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  • DOI: https://doi.org/10.1007/978-1-84800-306-4_4

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84800-305-7

  • Online ISBN: 978-1-84800-306-4

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