A Survey of Knowledge Graph-based Emotion Recognition: Technologies, Models, and Applications

​E​motion recognition (ER) aims to identify affective states from facial expressions, speech, text, and physiological signals. The context-dependent and multimodal nature of emotion presents challenges for systems that rely on statistical associations alone. Knowledge graphs (KGs) organize affective concepts and their relations, providing explicit knowledge for contextual modeling, multimodal integration, and reasoning. This survey reviews KG-based emotion recognition and related affective tasks, covering knowledge extraction and graph construction, unimodal and multimodal systems, representation learning, reasoning, and integration with large language models (LLMs). The synthesis draws on 49 core studies and uses additional literature for theoretical and methodological context. It distinguishes semantic KGs from computational graphs and characterizes graph construction along five dimensions: knowledge scope, semantic foundation, construction mechanism, human involvement, and multimodal representation. Selected within-study comparisons report gains from enabling knowledge-related components, while differences in tasks, protocols, and comparison designs limit conclusions across systems. The survey also examines evaluation datasets, knowledge quality, and the computational and grounding challenges of LLM and KG integration. It identifies research priorities in knowledge validation, cross-domain generalization, privacy, temporal modeling, and multimodal alignment, with evaluation procedures for assessing their contribution to reliable affective systems.