The question of representing emotion computationally remains largely unanswered: popular
approaches require annotators to assign a magnitude (or a class) of some emotional
dimension, while an alternative is to focus on the relationship between two or more options.
Recent evidence in affective computing suggests that following a methodology of ordinal
annotations and processing leads to better reliability and validity of the model. This paper
compares the generality of classification methods versus preference learning methods
in predicting the levels of arousal in two widely used affective datasets. Findings of this
initial study further validate the hypothesis that approaching affect labels as ordinal data
and building models via preference learning yields models of better validity.peer-reviewe
