The Arousal video Game AnnotatIoN (AGAIN) Dataset
This provides a resource for researchers in affective computing to investigate general affect modeling, though it is incremental as it focuses on dataset creation rather than novel methods.
The paper tackles the problem of modeling affect across dissimilar tasks by introducing the AGAIN dataset, a large-scale affective corpus with over 1,100 in-game videos from nine games annotated for arousal by 124 participants, resulting in over 37 hours of annotated video and game logs.
How can we model affect in a general fashion, across dissimilar tasks, and to which degree are such general representations of affect even possible? To address such questions and enable research towards general affective computing, this paper introduces The Arousal video Game AnnotatIoN (AGAIN) dataset. AGAIN is a large-scale affective corpus that features over 1,100 in-game videos (with corresponding gameplay data) from nine different games, which are annotated for arousal from 124 participants in a first-person continuous fashion. Even though AGAIN is created for the purpose of investigating the generality of affective computing across dissimilar tasks, affect modelling can be studied within each of its 9 specific interactive games. To the best of our knowledge AGAIN is the largest -- over 37 hours of annotated video and game logs -- and most diverse publicly available affective dataset based on games as interactive affect elicitors.