Leveraging Large Language Models for the Identification of Human Emotional States

Leung, Clement; Xu, Zhifei

Home // International Journal On Advances in Life Sciences, volume 16, numbers 3 and 4, 2024 // View article

Leveraging Large Language Models for the Identification of Human Emotional States

Authors:
Clement Leung
Zhifei Xu

Keywords: image emotion recognition; large language model; zero-shot; emotion stability factor; ChatGPT4.

Abstract:
This study explores emotion recognition, classification, and prediction, emphasizing its importance in safety-critical tasks where emotional competence can save lives. We classify emotions into positive (competent) and negative (incompetent) states and develop a stochastic model featuring an emotion stability factor, to measure how quickly individuals return to their baseline emotional state—lower indicates greater emotional stability. Our model provides a foundation for personalized emotional health strategies and tailored psychological treatments. Additionally, we evaluate ChatGPT-4's zero-shot capabilities in image-based sentiment reasoning compared to ResNet-50 and Vision Transformer models. Despite competitive performance, challenges such as unstable predictions underscore the complexity of mental health analysis in image conversations. We propose improvements through enhanced prompt engineering, model fine-tuning, and an ensemble approach combining each model's strengths to create a more accurate emotion classification system with significant implications for mental health applications.

Pages: 112 to 121

Publication date: December 30, 2024

Published in: journal

ISSN: 1942-2660