Support-vector-based emergent self-organising approach for emotional understanding
Nguwi, Yok-Yen, and Cho, Siu-Yeung (2010) Support-vector-based emergent self-organising approach for emotional understanding. Connection Science, 22 (4). pp. 355-371.
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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.
|Item Type:||Article (Refereed Research - C1)|
|Date Deposited:||05 Sep 2012 05:49|
|FoR Codes:||08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 100%|
|SEO Codes:||89 INFORMATION AND COMMUNICATION SERVICES > 8902 Computer Software and Services > 890202 Application Tools and System Utilities @ 100%|