Paper
Modelling Emotions is an Elusive Pursuit in Affective Computing
Authors
Anders Rolighed Larsen, Sneha Das, Line Clemmensen
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.
Metadata
Related papers
Vibe Coding XR: Accelerating AI + XR Prototyping with XR Blocks and Gemini
Ruofei Du, Benjamin Hersh, David Li, Nels Numan, Xun Qian, Yanhe Chen, Zhongy... • 2026-03-25
Comparing Developer and LLM Biases in Code Evaluation
Aditya Mittal, Ryan Shar, Zichu Wu, Shyam Agarwal, Tongshuang Wu, Chris Donah... • 2026-03-25
The Stochastic Gap: A Markovian Framework for Pre-Deployment Reliability and Oversight-Cost Auditing in Agentic Artificial Intelligence
Biplab Pal, Santanu Bhattacharya • 2026-03-25
Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA
Saahil Mathur, Ryan David Rittner, Vedant Ajit Thakur, Daniel Stuart Schiff, ... • 2026-03-25
MARCH: Multi-Agent Reinforced Self-Check for LLM Hallucination
Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.23017v1</id>\n <title>Modelling Emotions is an Elusive Pursuit in Affective Computing</title>\n <updated>2026-03-24T10:01:54Z</updated>\n <link href='https://arxiv.org/abs/2603.23017v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.23017v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>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.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='eess.AS'/>\n <published>2026-03-24T10:01:54Z</published>\n <arxiv:primary_category term='eess.AS'/>\n <author>\n <name>Anders Rolighed Larsen</name>\n </author>\n <author>\n <name>Sneha Das</name>\n </author>\n <author>\n <name>Line Clemmensen</name>\n </author>\n </entry>"
}