A Review of Affective Computing in Human-Computer Interaction Design

Affective computing aims to enable machines to recognize and simulate human emotions, forming a critical component of intuitive human-computer interaction. This study presents a systematic bibliometric review of 225 high-impact publications (2014–2024) from the Web of Science Core Collection. Utilizing tools like Bibliometrix and CiteSpace, this analysis maps the field's evolution, identifying a paradigm shift from basic emotion recognition to deep learning-based multi-modal fusion, with generative models and large language models for affective synthesis emerging as a new frontier. Persistent challenges include integrating multi-modal context for personalization, fulfilling real-time processing requirements, and addressing ethical issues like bias. To bridge the gap between emotion recognition and the development of genuinely adaptive, context-aware systems, the study highlights the urgent need for generative affective frameworks and neuroscience-informed lightweight models. This review synthesizes the developmental trajectory of affective computing in human-computer interaction to guide future research.