This paper is an extended version of Lee et al. (2010b) at the Seventh International Conference on Language Resources and Evaluation (LREC-10) and Lee et al. (2010a) at NAACL-HLT-10 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text.
DETECTING EMOTION CAUSES WITH A LINGUISTIC RULE-BASED APPROACH1
Article first published online: 4 SEP 2012
© 2012 Wiley Periodicals, Inc.
Special Issue: Computational Approaches to Analysis of Emotion in Text Guest Editors: Diana Inkpen and Carlo Strapparava
Volume 29, Issue 3, pages 390–416, August 2013
How to Cite
Lee, S. Y. M., Chen, Y., Huang, C.-R. and Li, S. (2013), DETECTING EMOTION CAUSES WITH A LINGUISTIC RULE-BASED APPROACH. Computational Intelligence, 29: 390–416. doi: 10.1111/j.1467-8640.2012.00459.x
- Issue published online: 4 AUG 2013
- Article first published online: 4 SEP 2012
- Received 5 August 2010; Revised 5 May 2011; Accepted 2 July 2012
- emotion cause corpus;
- emotion cause detection;
- rule-based system;
Most theories of emotion treat recognition of a triggering cause event as an integral part of emotion processing. This paper proposes emotion cause detection as a new research area in emotion processing. As a first step toward fully automatic inference of emotion-cause correlation, we propose a text-driven, rule-based approach to emotion cause detection in Chinese. First, we constructed a Chinese emotion cause annotated corpus based on our proposed annotation scheme. Next, we analyzed the corpus data, which yielded the identification of seven groups of linguistic cues and two sets of generalized linguistic rules for the detection of emotion causes. We then developed a rule-based system for emotion cause detection based on the linguistic rules. In addition, we proposed an evaluation scheme with two phases for performance assessment. The results of our experiments show that our system achieved a promising performance for cause occurrence detection, as well as for cause event detection. The current study should lay the groundwork for future research on the inferences of implicit information and the discovery of new information based on cause-event relation.