Towards Emotion Classification Using Appraisal Modeling

Abstract

The authors studied whether a two-step approach based on appraisal modeling could help in improving performance of emotion classification from sensor data that is typically executed in a one-stage approach in which sensor data is directly classified into a discrete emotion label. The proposed intermediate step is inspired by appraisal models in which emotions are characterized using appraisal dimensions, and subdivides the task in a person-dependent and person-independent stage. In this paper, the authors assessed feasibility of this second stage: the classification of emotion from appraisal data. They applied a variety of machine learning techniques and used visualization techniques to gain further insight into the classification task. Appraisal theory assumes the second step to be independent of the individual. Results obtained are promising, but do indicate that not all emotions can be equally well classified, perhaps indicating that the second stage is not as person-independent as proposed in the literature.