A Review of Affective Computing in Human-Computer Interaction Design
Tao Chen et al.
What the paper says
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.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.