Knowledge-Based Emotion Recognition Using Large Language Models

• Published in: International Conference on Affective Computing and Intelligent Interaction and workshops pp. 1 - 9
• Main Authors: Han, Bin, Yau, Cleo, Lei, Su, Gratch, Jonathan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 15.09.2024
• Subjects: Accuracy; Affective computing; Bayes methods; bayesian cue integration; Context modeling; Emotion recognition; facial emotion recognition; Knowledge based systems; Large language models; Long short term memory; Probabilistic logic; Psychology
• ISSN: 2156-8111
• DOI: 10.1109/ACII63134.2024.00005

Emotion recognition in social situations is a complex task that requires integrating information from both facial expressions and the situational context. While traditional approaches to automatic emotion recognition have focused on decontextualized signals, recent research emphasizes the importance of context in shaping emotion perceptions. This paper contributes to the emerging field of context-based emotion recognition by leveraging psychological theories of human emotion perception to inform the design of automated methods. We propose an approach that combines emotion recognition methods with Bayesian Cue Integration (BCI) to integrate emotion inferences from decontextualized facial expressions and contextual knowledge inferred via Large-language Models. We test this approach in the context of interpreting facial expressions during a social task, the prisoner's dilemma. Our results provide clear support for BCI across a range of automatic emotion recognition methods. The best automated method achieved results comparable to human observers, suggesting the potential for this approach to advance the field of affective computing.