  Methods that detect user states such as emotions are useful for interactive
systems. In this position paper, we argue for model-based approaches that are
trained on user behaviour and self-reported user state as ground truths. In an
application context, they record behaviour, extract relevant features, and use
the models to predict user states. We describe how this approach can be
implemented and discuss its benefits in comparison to solely self-reports in an
application and to models of behaviour without the selfreport ground truths.
Finally, we discuss shortcomings of this approach by considering its drawbacks
and limitations.
