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Textual Affect Detection in Human Computer Interaction

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 194)

Abstract

In this paper we focus on the affect detection of the short text pervasively used in human computer interaction. The research intends to render the interaction more emotionally expressive. In order to estimate the affect in the short text, we construct an affect lexicon firstly. Then a set of extraction rules (ERs) is built to extract the semantic representation of each word. Finally, the affect state of the short text is represented with PAD values and computed through the manually made affect generation rules (AGRs). The evaluation of the results corresponds with the human subjective appraisal.

Keywords

textual affect semantic PAD values 

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References

  1. 1.
    Peris, R., Gimeno, M.A., Pinazo, D., Ortet, G., Carrero, V., Sanchiz, M., Ibanez, I.: Online chat rooms: virtual spaces of interaction for socially oriented people. Cyber Psychology and Behavior, 43–51 (2002)Google Scholar
  2. 2.
    Fabrizio, S.: Machine learning in automated text categorization. ACM Computing Surveys 34(1), 1–47 (2002)CrossRefGoogle Scholar
  3. 3.
    Ovesdotter, A.C., Roth, D., Sproat, R.: Emotions from text: machine learning for text-based emotion prediction. In: HLT- EMNLP, Canada, pp. 579–586 (2005)Google Scholar
  4. 4.
    Boucouvalas, A.C.: Real Time Text-to-Emotion Engine for Expressive Internet Communications. In: Being There: Concepts, Effects and Measurement of User Presence in Synthetic Environments, pp. 306–318. Ios Press (2003)Google Scholar
  5. 5.
    Liu, H., Lieberman, H., Selker, T.: A Model of Textual Affect Sensing using Real-World Knowledge. In: Proc. the 8th International Conference on Intelligent User Interfaces, pp. 125–132. ACM Press (2003)Google Scholar
  6. 6.
    Singh, P., Lin, T., Mueller, E.T., Lim, G., Perkins, T.: The public acquisition of commonsense knowledge, pp. 1223–1237. Springerlink Press (2002)Google Scholar
  7. 7.
    Neviarouskaya, A., Prendinger, H., Ishizuka, M.: EmoHeart: Automation of Expressive Communication of Emotions in Second Life. In: Proc. International Conference on Online Communities and Social Computing, pp. 584–592. Springerlink Press (2009)Google Scholar
  8. 8.
    Das, D., Bandyopadhyay, S.: Identifying Emotion Expressions, Intensities and Sentence Level Emotion Tags using a Supervised Framwork. In: PACLIC, Japan, pp. 95–105 (2010)Google Scholar
  9. 9.
    James, W.: What is an emotion? In: Calhoun, C., Solomon, R.C. (eds.) Classic Readings in Philosophical Psychology, pp. 127–141. Oxford University Press, NewYork (1984)Google Scholar
  10. 10.
    Dong, Z.D., Dong, Q.: HowNet, http://www.keenage.com
  11. 11.
    Liu, B., Ren, F., Wang, C.: The Building of Chinese Emotion Thesaurus Using How Net Based on the Main Sememe. In: Proc. 4th International Conference on Natural Computation, pp. 91–95. IEEE Press (2008)Google Scholar
  12. 12.
    Mehrabian, A.: Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament. Current Psychology 14, 261–292 (1996)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Zhang, H.P., Yu, H.K., Xiong, D.Y., Liu, Q.: HHMM-based Chinese Lexical Analyzer ICTCLAS. In: Proc. 2nd SIGHAN Workshop Affiliated with 41th ACL, pp. 184–187 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.The School of Electronic and Information EngineeringBeihang UniversityBejingChina

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