An annotation tool for automatically triangulating individuals’ psychophysiological emotional reactions to digital media stimuli

• A software tool for annotating players’ emotional reactions to audio visual game stimuli.
• Design based on the findings of a requirement analysis conducted with a diverse focus group (N = 16).
• A modular architecture for rapid adaptations based on specific user study needs or collected data.
• A case study using physiological data on a popular survival FPS game (Left 4 Dead 2).

Current affective user experience studies require both laborious and time-consuming data analysis, as well as dedicated affective classification algorithms. Moreover, the high technical complexity and lack of general guidelines for developing these affective classification algorithms further limits the comparability of the obtained results. In this paper we target this issue by presenting a tool capable of automatically annotating and triangulating players’ physiologically interpreted emotional reactions to in-game events. This tool was initially motivated by an experimental psychology study regarding the emotional habituation effects of audio-visual stimuli in digital games and we expect it to contribute in future similar studies by providing both a deeper and more objective analysis on the affective aspects of user experience. We also hope it will contribute towards the rapid implementation and accessibility of this type of studies by open-sourcing it. Throughout this paper we describe the development and benefits presented by our tool, which include: enabling researchers to conduct objective a posteriori analyses without disturbing the gameplay experience, automating the annotation and emotional response identification process, and formatted data exporting for further analysis in third-party statistical software applications.