Towards a Deeper Modeling of Emotions: The Deep Method and its Application on Shame

Abstract

Understanding emotions is key to Affective Computing. Emotion recognition focuses on the communicative component of emotions encoded in social signals. This view alone is insufficient for deeper understanding and computational representation of the internal, subjectively experienced component of emotions. This paper presents the Deep method as a starting point for a deeper computational modeling of internal emotions. The method includes how to query individual internal emotional experiences, and it shows an approach to represent such information computationally. It combines social signals, verbalized introspection information, context information, and theory-driven knowledge. We apply the Deep method exemplary on the emotion shame and present a schematic dynamic Bayesian network for modeling it.