Modelling Emotions is an Elusive Pursuit in Affective Computing

TL;DR

This paper discusses the limitations of categorical emotion labels in affective computing and advocates for continuous dimensional models to better capture emotional nuance and improve AI applications.

Contribution

It highlights the need to shift from categorical to dimensional emotion models to enhance emotional understanding in affective computing.

Findings

Categorical labels obscure emotional nuance.

Continuous models can better represent emotional complexity.

Adopting dimensional models could reduce uncertainty in AI emotional detection.

Abstract

Affective computing - combining sensor technology, machine learning, and psychology - have been studied for over three decades and is employed in AI-powered technologies to enhance emotional awareness in AI systems, and detect symptoms of mental health disorders such as anxiety and depression. However, the uncertainty in such systems remains high, and the application areas are limited by categorical definitions of emotions and emotional concepts. This paper argues that categorical emotion labels obscure emotional nuance in affective computing, and therefore continuous dimensional definitions are needed to advance the field, increase application usefulness, and lower uncertainties.