Empathy Detection from Text, Audiovisual, Audio or Physiological Signals: A Systematic Review of Task Formulations and Machine Learning Methods

TL;DR

This systematic review analyzes recent machine learning approaches for empathy detection across various modalities and interaction types, highlighting task formulations, challenges, and future research directions to improve human-centric applications.

Contribution

It provides a comprehensive overview of empathy detection methods, task formulations, and datasets across multiple modalities, identifying research gaps and proposing future exploration avenues.

Findings

Identified key task formulations like localized and overall empathy detection.

Summarized modality-specific network architectures for text, audiovisual, audio, and physiological signals.

Discussed challenges and potential applications in affective computing and related fields.

Abstract

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 849 papers from 10 well-known academic databases, systematically screened them and analysed the final 82 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. This paper, therefore, provides a structured overview of recent advancements and remaining challenges towards developing a robust empathy detection system that could meaningfully contribute to enhancing human well-being.