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Empathy is a social skill that indicates an individual's ability tounderstand others. Over the past few years, empathy has drawn attention fromvarious disciplines, including but not limited to Affective Computing,Cognitive Science and Psychology. Empathy is a context-dependent term; thus,detecting or recognising empathy has potential applications in society,healthcare and education. Despite being a broad and overlapping topic, theavenue of empathy detection studies leveraging Machine Learning remainsunderexplored from a holistic literature perspective. To this end, wesystematically collect and screen 801 papers from 10 well-known databases andanalyse the selected 54 papers. We group the papers based on input modalitiesof empathy detection systems, i.e., text, audiovisual, audio and physiologicalsignals. We examine modality-specific pre-processing and network architecturedesign protocols, popular dataset descriptions and availability details, andevaluation protocols. We further discuss the potential applications, deploymentchallenges and research gaps in the Affective Computing-based empathy domain,which can facilitate new avenues of exploration. We believe that our work is astepping stone to developing a privacy-preserving and unbiased empathic systeminclusive of culture, diversity and multilingualism that can be deployed inpractice to enhance the overall well-being of human life.

Empathy Detection Using Machine Learning on Text, Audiovisual, Audio or Physiological Signals