This study proposes a computational framework for providing affective labels to real-life situations. We first define an affective situation as a specific arrangement of affective entities relevant to emotion elicitation in an affective situation. Then, an affective situation is represented as a set of labels in the valence-arousal emotion space. Based on physiological behaviors in response to a situation, the proposed framework quantifies the expected emotion evoked by the interaction with a stimulus event. The accumulated result in a spatial-temporal situation is represented as a polynomial curve called affective curve. It bridges the semantic gap between cognitive and affective perception in real-world situations. We show the efficacy of the curve on reliable emotion labels in real-world experiments, concerning 1) a comparative study between the results from our system and existing explicit assessment on
measuring emotion, 2) physiological distinctiveness in emotional states, and 3) physiological characteristics correlated to continuous labels.
1. BH Kim, S Jo, S Choi, A-Situ: a computational framework for affective labeling from psychological behaviors in real-life situations, Scientific Reports, 10, 15916, Sept 2020 [LINK]