Detecting emotion causes with a linguistic rule-based approach

Yat Mei Lee, Ying Chen, Chu-ren Huang, Shoushan Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

47 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)390-416
Number of pages27
JournalComputational Intelligence
Issue number3
Publication statusPublished - 1 Aug 2013


  • Chinese
  • emotion cause corpus
  • emotion cause detection
  • rule-based system

ASJC Scopus subject areas

  • Computational Mathematics
  • Artificial Intelligence


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