Emotion recognition from text using semantic labels and separable mixture models

被引：5

作者：

Wu, Chung-Hsien ^{[1
,2
]}

Chuang, Ze-Jing ^{[1
,2
]}

Lin, Yu-Chung ^{[1
,2
]}

机构：

[1] National Cheng Kung University, Tainan

[2] Dept. of Computer Science and Information Engineering, National Cheng Kung University, Tainan

来源：

ACM Transactions on Asian Language Information Processing | 2006年 / 5卷 / 02期

关键词：

Emotion extraction;

D O I：

10.1145/1165255.1165259

中图分类号：

学科分类号：

摘要：

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. © 2006 ACM.

引用

页码：165 / 182

页数：17

共 31 条

[1]

Amir N., Ziv S., Cohen R., Characteristics of authentic anger in Hebrew speech, Proceedings of the 8th European Conference on Speech Communication and Technology (Geneva), pp. 713-716, (2003)

[2]

Boucouvalas A.C., Real time text-to-emotion engine for expressive internet communications, Emerging Communication: Studies on New Technologies and Practices in Communication, pp. 305-318, (2002)

[3]

Boucouvalas A.C., Zhe X., Text-to-emotion engine for real time internet communication, Proceedings of the International Symposium on CSNDSP 2002, pp. 164-168, (2002)

[4]

Chan S.W.K., Franklin J., Symbolic connectionism in natural language disambiguation, IEEE Tran. Neural Network, 9, pp. 739-755, (1998)

[5]

Chen M.J., Yeh J.F., Wu C.H., Ontology-based dialog management for service integration, Proceedings of ROCLING, 15, pp. 257-277, (2003)

[6]

Chuang Z.J., Wu C.H., Emotion recognition from textual input using an emotional semantic network, Proceedings of the International Symposium on Chinese Spoken Language Processing, pp. 177-180, (2002)

[7]

Cohn J.F., Katz G.S., Bimodal expression of emotion by face and voice, Proceedings of the Sixth ACM International Multimedia Conference on Face/Gesture Recognition and Their Applications, pp. 41-44, (1998)

[8]

Cowie R., Douolas-Cowie E., Tsapatsoulis N., Votsis G., Kollias S., Fellenz W., Taylor J.G., Emotion recognition in human-computer interaction, IEEE Signal Process. Mag., 18, 1, pp. 32-80, (2001)

[9]

De Silva L.C., Ng P.C., Bimodal emotion recognition, Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000, MARCH, pp. 332-335, (2000)

[10]

Devillers L., Vasilescu I., Lamel L., Annotation and detection of emotion in a task-oriented human-human dialog corpus, Proceedings of the ISLE Workshop on Dialogue Tagging for Multi-modal Human-compute Interaction, (2002)

← 1 2 3 4 →