# Datasets for Valence and Arousal Inference: A Survey

arXiv: https://arxiv.org/abs/2510.00738 · HTML: https://mlsanity.com/paper/2510.00738/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [From Affect Prediction to Affect Forecasting: Evidence for Distinct Information Sources in Longitudinal Text](https://mlsanity.com/paper/2606.16687/) (2026)

## Abstract (arXiv)

Understanding human affect can be used in robotics, marketing, education, human-computer interaction, healthcare, entertainment, autonomous driving, and psychology to enhance decision-making, personalize experiences, and improve emotional well-being. This work presents a comprehensive overview of affect inference datasets that utilize continuous valence and arousal labels. We reviewed 25 datasets published between 2008 and 2024, examining key factors such as dataset size, subject distribution, sensor configurations, annotation scales, and data formats for valence and arousal values. While camera-based datasets dominate the field, we also identified several widely used multimodal combinations. Additionally, we explored the most common approaches to affect detection applied to these datasets, providing insights into the prevailing methodologies in the field. Our overview of sensor fusion approaches shows promising advancements in model improvement for valence and arousal inference.