Eliciting and Annotating Emotion in Virtual Spaces

We propose an online methodology where moment-to-moment affect annotations are gathered while exploring and visually interacting with virtual environments. For this task we developed an application to support this methodology, targeting both a VR and a desktop experience, and conducted a study to evaluate these two media of display. Results show that in terms of usability, both experiences were perceived equally positive. Presence was rated significantly higher for the VR experience, while participant ratings indicated a tendency for medium distraction during the annotation process. Additionally, effects between the architectural design elements were identified with perceived pleasure. The strengths and limitations of the proposed approach are highlighted to ground further work in gathering affect data in immersive and interactive media within the context of architectural appraisal. 


Knowing Your Annotator: Rapidly Testing the Reliability of Affect Annotation

The laborious and costly nature of affect annotation is a key detrimental factor for obtaining large scale corpora with valid and reliable affect labels. Motivated by the lack of tools that can effectively determine an annotator’s reliability, this paper proposes general quality assurance (QA) tests for real-time continuous annotation tasks. Assuming that the annotation tasks rely on stimuli with audiovisual components, such as videos, we propose and evaluate two QA tests: a visual and an auditory QA test. We validate the QA tool across 20 annotators that are asked to go through the test followed by a lengthy task of annotating the engagement of gameplay videos. Our findings suggest that the proposed QA tool reveals, unsurprisingly, that trained annotators are more reliable than the best of untrained crowdworkers we could employ. Importantly, the QA tool introduced can predict effectively the reliability of an affect annotator with 80% accuracy, thereby, saving on resources, effort and cost, and maximizing the reliability of labels solicited in affective corpora. The introduced QA tool is available and accessible through the PAGAN annotation platform.