Empathy indicates an individual's ability to understand others. Over the past few years, empathy has drawn attention from various disciplines, including but not limited to Affective Computing, Cognitive Science and Psychology. Detecting empathy has potential applications in society, healthcare and education. Despite being a broad and overlapping topic, the avenue of empathy detection leveraging Machine Learning remains underexplored from a systematic literature review perspective. We collected 828 papers from 10 well-known databases, systematically screened them and analysed the final 61 papers. Our analyses reveal several prominent task formulations $-$ including empathy on localised utterances or overall expressions, unidirectional or parallel empathy, and emotional contagion $-$ in monadic, dyadic and group interactions. Empathy detection methods are summarised based on four input modalities $-$ text, audiovisual, audio and physiological signals $-$ thereby presenting modality-specific network architecture design protocols. We discuss challenges, research gaps and potential applications in the Affective Computing-based empathy domain, which can facilitate new avenues of exploration. We further enlist the public availability of datasets and codes. We believe that our work is a stepping stone to developing a robust empathy detection system that can be deployed in practice to enhance the overall well-being of human life.

翻译：共情反映了个体理解他人的能力。过去几年中，共情研究已引起包括情感计算、认知科学与心理学在内的多学科关注。共情检测在社会、医疗及教育领域具有潜在应用价值。尽管该主题具有广泛性与交叉性，但从系统文献综述视角看，基于机器学习的共情检测研究路径仍未得到充分探索。本研究从10个知名数据库中收集了828篇文献，经系统筛选后对最终61篇论文进行了分析。分析揭示了若干典型任务构建范式——包括针对局部话语或整体表达的共情、单向或平行共情以及情绪感染——这些范式覆盖单主体、双主体及群体交互场景。研究根据文本、视听、音频和生理信号四种输入模态对共情检测方法进行了归纳，进而提出了面向特定模态的网络架构设计范式。我们讨论了基于情感计算的共情研究领域面临的挑战、研究空白与潜在应用方向，以促进新的探索路径。同时整理了公开可用的数据集与代码资源。我们相信，本研究将为开发可投入实践应用的鲁棒性共情检测系统奠定基石，从而提升人类生活的整体福祉。