Cang XL, Guerra RR, Bucci P, Guta B, MacLean K, Rodgers L, Mah H, Hsu S, Feng Q, Zhang C, Agrawal A. "Choose or Fuse: Enriching Data Views with Multi-label Emotion Dynamics." In2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII) 2022 Oct 18 (pp. 1-8). IEEE.
Many emotion classification and prediction approaches focus on emotion state, defined as static and single-valued. In contrast, our in-body experience is of sensations that can quickly evolve, consistent with scientific evidence of physiological regulation mechanisms. Can we reframe classification to estimate dynamic emotion parameters at interactive rates? For insight into dynamic emotion characteristics, we developed a multipass labelling protocol to capture controlled yet genuine emotion evolution elicited as 16 participants played a tense video game. We analyze and align multiple self-report outputs, inspect the signals for emotion dynamics, and consider label metaphors of position and angle -- ``where I am'' vs ``where I'm going''. Finally, we reflect on the benefits and drawbacks of such a protocol for developing models of fast-evolving emotion.