The Arousal video Game AnnotatIoN (AGAIN) Dataset

TL;DR

The paper introduces the AGAIN dataset, a large, diverse collection of annotated game videos aimed at advancing general affect modeling across different tasks in affective computing.

Contribution

It presents the largest and most diverse affective dataset based on games, enabling research on general affect modeling across dissimilar tasks.

Findings

Over 37 hours of annotated video and game logs.

Includes 1,100+ videos from nine different games.

Annotated for arousal by 124 participants.

Abstract

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.