Modeling Users' Mood State to Improve Human-Machine-Interaction

The detection of user emotions plays an important role in Human-Machine-Interaction. By considering emotions, applications such as monitoring agents or digital companions are able to adapt their reaction towards users' needs and claims. Besides emotions, personality and moods are eminent as well. Standard emotion recognizers do not consider them adequately and therefore neglect a crucial part of user modeling. The challenge is to gather reliable predictions about the actual mood of the user and, beyond that, represent changes in users' mood during interaction. In this paper we present a model that incorporates both the tracking of mood changes based on recognized emotions and different personality traits. Furthermore we present a first evaluation on realistic data.

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