Empathy Detection Using Machine Learning on Text, Audiovisual, Audio or
  Physiological Signals
Md Rakibul Hasan Md Zakir Hossain Shreya Ghosh Susannah Soon Tom Gedeon
Empathy is a social skill that 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. 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, the
avenue of empathy detection studies leveraging Machine Learning remains
underexplored from a holistic literature perspective. To this end, we
systematically collect and screen 801 papers from 10 well-known databases and
analyse the selected 54 papers. We group the papers based on input modalities
of empathy detection systems, i.e., text, audiovisual, audio and physiological
signals. We examine modality-specific pre-processing and network architecture
design protocols, popular dataset descriptions and availability details, and
evaluation protocols. We further discuss the potential applications, deployment
challenges and research gaps in the Affective Computing-based empathy domain,
which can facilitate new avenues of exploration. We believe that our work is a
stepping stone to developing a privacy-preserving and unbiased empathic system
inclusive of culture, diversity and multilingualism that can be deployed in
practice to enhance the overall well-being of human life.