Abstract
This study presents a novel approach to automatic emotion recognition from text. First, emotion generation rules (EGRs) are manually deduced from psychology to represent the conditions for generating emotion. Based on the EGRs, the emotional state of each sentence can be represented as a sequence of semantic labels (SLs) and attributes (ATTs); SLs are defined as the domain-independent features, while ATTs are domain-dependent. The emotion association rules (EARs) represented by SLs and ATTs for each emotion are automatically derived from the sentences in an emotional text corpus using the a priori algorithm. Finally, a separable mixture model (SMM) is adopted to estimate the similarity between an input sentence and the EARs of each emotional state. Since some features defined in this approach are domain-dependent, a dialog system focusing on the students' daily expressions is constructed, and only three emotional states, happy, unhappy, and neutral, are considered for performance evaluation. According to the results of the experiments, given the domain corpus, the proposed approach is promising, and easily ported into other domains.
- Amir, N., Ziv, S., and Cohen, R. 2003. Characteristics of authentic anger in Hebrew speech. In Proceedings of the 8th European Conference on Speech Communication and Technology (Geneva). 713--716.Google Scholar
- Boucouvalas, A. C. 2002. Real time text-to-emotion engine for expressive internet communications. In Emerging Communication: Studies on New Technologies and Practices in Communication. G. Riva et al. eds. IOS Press. 305--318.Google Scholar
- Boucouvalas, A. C., and Zhe, X. 2002. Text-to-emotion engine for real time internet communication. In Proceedings of the International Symposium on CSNDSP 2002 (Staffordshire Univ., July 15--17). 164--168.Google Scholar
- Chan, S. W. K. and Franklin, J. 1998. Symbolic connectionism in natural language disambiguation. IEEE Tran. Neural Network. 9, 739--755. Google Scholar
- Chen, M. J., Yeh, J. F., and Wu, C. H. 2003. Ontology-based dialog management for service integration. In Proceedings of ROCLING XV (Hsinchu, Taiwan). 257--277.Google Scholar
- Chuang, Z. J. and Wu, C. H. 2002. Emotion recognition from textual input using an emotional semantic network. In Proceedings of the International Symposium on Chinese Spoken Language Processing (Denver, CO). 177--180.Google Scholar
- Cohn, J. F. and Katz, G. S. 1998. Bimodal expression of emotion by face and voice. In Proceedings of the Sixth ACM International Multimedia Conference on Face/Gesture Recognition and Their Applications (Bristol, UK). ACM, New York. 41--44. Google Scholar
- Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., and Taylor, J. G. 2001. Emotion recognition in human-computer interaction. IEEE Signal Process. Mag. 18, 1, 32--80.Google Scholar
- De Silva, L. C. and Ng, P. C. 2000. Bimodal emotion recognition. In Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000 (March). 332--335. Google Scholar
- Devillers, L., Vasilescu, I., and Lamel, L. 2002. Annotation and detection of emotion in a task-oriented human-human dialog corpus. In Proceedings of the ISLE Workshop on Dialogue Tagging for Multi-Modal Human-Compute Interaction (Dec. 15--17).Google Scholar
- Devillers, L., Luniel, L., and Vasilescu, I. 2003. Emotion detection in task-oriented spoken dialogues. In Proceedings of the International Conference on Multimedia and Expo (Baltimore, MD, July 6--9). 549--552. Google Scholar
- Dijkstra, K., Zwaan, R. A., Graesser, A. C., and Magliano, J. P. 1994. Character and reader emotions in literary texts. Poetics 23, 139--157.Google Scholar
- Dong, Z., and Dong, Q. 1988. HowNet. http://www.keenage.com/zhiwang/e_zhiwang.html.Google Scholar
- Fellbaum, C. 1998. WordNet: An Electronic Lexical Database. MIT Press. Cambridge, MA.Google Scholar
- Forbes-Riley, K. and Litman, D. J. 2004. Predicting emotion in spoken dialogue from multiple knowledge sources. In Proceedings of Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL, Boston, MA, May 2--7). 201--208.Google Scholar
- Han, J. and Kamber, M. 2001. Data Mining: Concepts and Techniques. Morgan Kaufmamn. Google Scholar
- Hofmann, T. and Puzicha, J. 1998. Statistical models for co-occurrence data. AI Memo 1625; CBCL Memo159, Feb. Google Scholar
- Kwon, O. W., Chan, K., Hao, J., and Lee, T. W. 2003. Emotion recognition by speech signals. In Proceedings of the 8th European Conference on Speech Communication and Technology (Geneva, Sept.). 125--128.Google Scholar
- Lazarus, R. S. and Lazarus, B. N. 1996. Passion and Reason: Making Sense of Our Emotions. Oxford University Press, New York.Google Scholar
- Lee, C. M., Narayanan, S. S., and Pieraccini, R. 2002. Combining acoustic and language information for emotion recognition. In Proceedings of the 7th International Conference on Spoken Language Processing (Denver, CO). 873--876.Google Scholar
- Litman, D. and Forbes, K. 2003. Recognizing emotions from student speech in tutoring dialogues. In Proceedings of the IEEE Automatic Speech Recognition and Understanding Workshop (Virgin Islands, Dec.). 39--52.Google Scholar
- McLachlan, G. J. and Krishnan, T. 1997. The EM Algorithm and Extensions. Wiley, New York.Google Scholar
- Picard, R. W. 1997. Affective Computing. MIT Press. Cambridge, MA. Google Scholar
- Picard, R. W., Vyzas, E., and Healey, J. 2001. Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Trans. Pattern Anal. Mach. Intell. 10, 1175--1191. Google Scholar
- Rahurkar, M. A. and Hansen, J. H. L. 2003. Frequency distribution based weighted sub-band approach for classification of emotional/stressful content in speech. In Proceedings of the 8th European Conference on Speech Communication and Technology (Geneva, Sept. 1--4). 721--724.Google Scholar
- Schuller, B., Rigoll, G., and Lang, M. 2004. Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture. In Proceedings of 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing (Montreal, May 17--21). 17--21.Google Scholar
- Subasic, P. and Huettner, A. 2001. Affect analysis of text using fussy semantic typing. IEEE Trans. Fuzzy Syst. 9, 483--496. Google Scholar
- Tao, J. and Tan, T. 2004. Emotional Chinese talking head system. In Proceedings of the 6th International Conference on Multimodal Interface (Oct. 13--15). 273--280. Google Scholar
- Woods, W. A. 1970. Transition network grammars for natural language analysis. Commun. ACM 13. 591--602. Google Scholar
- Yanaru, T. 1995. An emotion processing system based on fuzzy inference and subjective observations. In Proceedings of the 2nd New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems (Dunedin, N Z., Nov. 20--23). IEEE Computer Society Press, New York, 15--20. Google Scholar
- Yu, F., Chang, E., Xu, Y. Q., and Shum, H. Y. 2001. Emotion detection from speech to enrich multimedia content. In Proceedings of the Second IEEE Pacific-Rim Conference on Multimedia (Beijing, Oct. 24--26). Google Scholar
Index Terms
- Emotion recognition from text using semantic labels and separable mixture models
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