Exploitation in Affect Detection in Improvisational E-Drama

  • Li Zhang
  • John A. Barnden
  • Robert J. Hendley
  • Alan M. Wallington
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4133)


We report progress on adding affect-detection to a program for virtual dramatic improvisation, monitored by a human director. To aid the director, we have partially implemented emotion detection. within users’ text input. The affect-detection module has been used to help develop an automated virtual actor. The work involves basic research into how affect is conveyed through metaphor and contributes to the conference themes such as building improvisational intelligent virtual agents for interactive narrative environments.


E-drama affect detection intelligent virtual actor and metaphor 


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  1. 1.
    Machado, I., Prada, R., Paiva, A.: Bringing Drama into a Virtual Stage. In: Proceedings of ACM Conference on Collaborative Virtual Environments. ACM Press, New York (2000)Google Scholar
  2. 2.
    Picard, R.W.: Affective Computing. MIT Press, Cambridge (2000)Google Scholar
  3. 3.
    Ortony, A., Clore, G.L., Collins, A.: The Cognitive Structure of Emotions. C.U.P (1988)Google Scholar
  4. 4.
    Prendinger, H., Ishizuka, M.: Simulating Affective Communication with Animated Agents. In: Proceedings of Eighth IFIP TC.13 Conference on Human-Computer Interaction, Tokyo, Japan, pp. 182–189 (2001)Google Scholar
  5. 5.
    Wiltschko, W.R.: Emotion Dialogue Simulator. eDrama learning, Inc. eDrama Front Desk (2003)Google Scholar
  6. 6.
    Mehdi, E.J., Nico, P., Julie, D., Bernard, P.: Modeling Character Emotion in an Interactive Virtual Environment. In: Proceedings of AISB 2004 Symposium: Motion, Emotion and Cognition, Leeds, UK (2004)Google Scholar
  7. 7.
    McCrae, R.R., John, O.P.: An Introduction to the Five Factor Model and Its Application. Journal of Personality 60, 175–215 (1992)CrossRefGoogle Scholar
  8. 8.
    Gratch, J., Marsella, S.: A Domain-Independent Framework for Modeling Emotion. Journal of Cognitive Systems Research 5(4), 269–306 (2004)CrossRefGoogle Scholar
  9. 9.
    Egges, A., Kshirsagar, S., Magnenat-Thalmann, N.: A Model for Personality and Emotion Simulation. In: Proceedings of Knowledge-Based Intelligent Information & Engineering Systems (KES 2003). Lecture Notes in AI. Springer, Berlin (2003)Google Scholar
  10. 10.
    Elliott, C., Rickel, J., Lester, J.: Integrating Affective Computing into Animated Tutoring Agents. In: Proceedings of IJCAI 1997 Workshop on Intelligent Interface Agents (1997)Google Scholar
  11. 11.
    Mateas, M.: Ph.D. Thesis. Interactive Drama, Art and Artificial Intelligence. School of Computer Science, Carnegie Mellon University (2002)Google Scholar
  12. 12.
    Zhe, X., Boucouvalas, A.C.: Text-to-Emotion Engine for Real Time Internet Communication. In: Proceedings of International Symposium on Communication Systems, Networks and DSPs, Staffordshire University, UK, pp. 164–168 (2002)Google Scholar
  13. 13.
    Boucouvalas, A.C.: Real Time Text-to-Emotion Engine for Expressive Internet Communications. In: Riva, G., Davide, F., IJsselsteijn, W. (eds.) Being There: Concepts, Effects and Measurement of User Presence in Synthetic Environments, pp. 305–318 (2002)Google Scholar
  14. 14.
    Craggs, R., Wood, M.: A Two Dimensional Annotation Scheme for Emotion in Dialogue. In: Proceedings of AAAI Spring Symposium: Exploring Attitude and Affect in Text. (2004)Google Scholar
  15. 15.
    Fussell, S., Moss, M.: Figurative Language in Emotional Communication. In: Fussell, S.R., Kreuz, R.J. (eds.) Social and Cognitive Approaches to Interpersonal Communication, pp. 113–142. Lawrence Erlbaum, Mahwah (1998)Google Scholar
  16. 16.
    Kövecses, Z.: Are There Any Emotion-Specific Metaphors? In: Athanasiadou, A., Tabakowska, E. (eds.) Speaking of Emotions: Conceptualization and Expression, pp. 127–151. Mouton de Gruyter, Berlin (1998)CrossRefGoogle Scholar
  17. 17.
    Barnden, J.A., Glasbey, S.R., Lee, M.G., Wallington, A.M.: Varieties and Directions of Inter-domain Influence in Metaphor. Metaphor and Symbol 19(1), 1–30 (2004)CrossRefGoogle Scholar
  18. 18.
    Watson, D., Tellegen, A.: Toward a Consensual Structure of Mood. Psychological Bulletin 98, 219–235 (1985)CrossRefGoogle Scholar
  19. 19.
    Briscoe, E., Carroll, J.: Robust Accurate Statistical Annotation of General Text. In: Proceedings of the 3rd International Conference on Language Resources and Evaluation, Las Palmas, Gran Canaria, pp. 1499–1504 (2002)Google Scholar
  20. 20.
    Metaphone Algorithm,
  21. 21.
    Levenshtein Distance Algorithm,
  22. 22.
    Jess, The Rule Engine for Java Platform (2004),
  23. 23.
    Heise, D.R.: Semantic Differential Profiles for 1,000 Most Frequent English Words. Psychological Monographs 79, 1–31 (1965)Google Scholar
  24. 24.
    WordNet, A Lexical Database for the English Language. Version 2.1 Cognitive Science Laboratory. Princeton University Google Scholar
  25. 25.
    Wallington, A.M., Barnden, J.A., Glasbey, S.R., Lee, M.G.: Metaphorical reasoning with an economical set of mappings. Delta 22(1), 147–171 (2006)Google Scholar
  26. 26.
    Barnden, J.A.: Metaphor, Semantic Preferences and Context-sensitivity. In: Festschrifft. Kluwer, Dordrecht (forthcoming)Google Scholar
  27. 27.
    Moon, R.: Fixed idioms and expressions in English. Clarendon Press, Oxford (1988)Google Scholar
  28. 28.
    Deignan, A.: Metaphor and corpus Linguistics. John Benjamins, Amsterdam (2005)Google Scholar
  29. 29.
    Goatly, A.: The language of metaphors. Routledge, London (1997)Google Scholar
  30. 30.
    Wallington, A.M., Barnden, J.A., Barnden, M.A., Ferguson, F.J., Glasbey, S.R.: Metaphoricity Signals: A Corpus-Based Investigation. Technical Report CSRP-03-5, School of Computer Science, The University of Birmingham, U.K (2003)Google Scholar
  31. 31.
    Wilks, Y.: Making preferences more active. Artificial Intelligence 10, 75–97 (1978)Google Scholar
  32. 32.
    Fass, D.: Processing metaphor and metonymy. Ablex, Greenwich (1997)Google Scholar
  33. 33.
    Mason, Z.J.: CorMet: a computational, corpus-based conventional metaphor extraction system. Computational Linguistics 30(1), 23–44 (2004)CrossRefGoogle Scholar
  34. 34.
    Sharoff, S.: How to Handle Lexical Semantics in SFL: a Corpus Study of Purposes for Using Size Adjectives. In: Systemic Linguistics and Corpus. Continuum, London (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Li Zhang
    • 1
  • John A. Barnden
    • 1
  • Robert J. Hendley
    • 1
  • Alan M. Wallington
    • 1
  1. 1.School of Computer ScienceUniversity of BirminghamBirminghamUK

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