Towards Context-sensitive Emotion Recognition

Abstract

Achieving socially compatible human-AI interaction requires systems that can interpret and respond to human emotions appropriately in complex social environments. While traditional emotion recognition models rely heavily on facial or bodily expressions, a growing body of research demonstrates that such cues are insufficient without the dynamic, multimodal contextual cues. Positioned at the intersection of cognitive psychology and AI, this work identifies three essential qualities for context-sensitive emotion recognition (CSER): generalizability to unseen scenarios, data efficiency in adapting to new contexts, and reliability in predictive performance across contexts. We outline a research plan that systematically investigates the role of contextual factors, domain adaptation, and uncertainty quantification in building CSER models capable of robust performance across real-world settings. Our approach integrates computational rigour with ethical responsibility to lay the foundation for next-generation emotion-aware systems that are not only accurate but also trustworthy, transparent, and support human well-being in digital interactions.