Abstract
We present further developments in our work on using data from real users to build a probabilistic model of user affect based on Dynamic Bayesian Networks (DBNs) and designed to detect multiple emotions. We present analysis and solutions for inaccuracies identified by a previous evaluation; refining the model’s appraisals of events to reflect more closely those of real users. Our findings lead us to challenge previously made assumptions and produce insights into directions for further improvement.
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© 2005 Springer-Verlag Berlin Heidelberg
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Conati, C., Maclaren, H. (2005). Data-Driven Refinement of a Probabilistic Model of User Affect. In: Ardissono, L., Brna, P., Mitrovic, A. (eds) User Modeling 2005. UM 2005. Lecture Notes in Computer Science(), vol 3538. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527886_7
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DOI: https://doi.org/10.1007/11527886_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27885-6
Online ISBN: 978-3-540-31878-1
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