Support-vector-based emergent self-organising approach for emotional understanding

Yok Yen Nguwi, Siu Yeung Cho

Research output: Journal PublicationArticlepeer-review

8 Citations (Scopus)


This study discusses the computational analysis of general emotion understanding from questionnaires methodology. The questionnaires method approaches the subject by investigating the real experience that accompanied the emotions, whereas the other laboratory approaches are generally associated with exaggerated elements. We adopted a connectionist model called support-vector-based emergent self-organising map (SVESOM) to analyse the emotion profiling from the questionnaires method. The SVESOM first identifies the important variables by giving discriminative features with high ranking. The classifier then performs the classification based on the selected features. Experimental results show that the top rank features are in line with the work of Scherer and Wallbott [(1994), 'Evidence for Universality and Cultural Variation of Differential Emotion Response Patterning', Journal of Personality and Social Psychology, 66, 310-328], which approached the emotions physiologically. While the performance measures show that using the full features for classifications can degrade the performance, the selected features provide superior results in terms of accuracy and generalisation.

Original languageEnglish
Pages (from-to)355-371
Number of pages17
JournalConnection Science
Issue number4
Publication statusPublished - Dec 2010
Externally publishedYes


  • Emotion
  • Self-organisation map
  • Support-vector machine

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Artificial Intelligence


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