  In recent decades, the field of affective computing has made substantial
progress in advancing the ability of AI systems to recognize and express
affective phenomena, such as affect and emotions, during human-human and
human-machine interactions. This paper describes our examination of research at
the intersection of multimodal interaction and affective computing, with the
objective of observing trends and identifying understudied areas. We examined
over 16,000 papers from selected conferences in multimodal interaction,
affective computing, and natural language processing: ACM International
Conference on Multimodal Interaction, AAAC International Conference on
Affective Computing and Intelligent Interaction, Annual Meeting of the
Association for Computational Linguistics, and Conference on Empirical Methods
in Natural Language Processing. We identified 910 affect-related papers and
present our analysis of the role of affective phenomena in these papers. We
find that this body of research has primarily focused on enabling machines to
recognize and express affect and emotion. However, we find limited research on
how affect and emotion predictions might be used by AI systems to enhance
machine understanding of human social behaviors and cognitive states. Based on
our analysis, we discuss directions to expand the role of affective phenomena
in multimodal interaction research.
