Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

TL;DR

This paper reviews recent advances in adversarial training techniques applied to affective computing and sentiment analysis, emphasizing their potential to enhance emotional AI systems and outlining future research directions.

Contribution

It provides a comprehensive overview of adversarial training methods in affective computing and sentiment analysis, highlighting challenges and future research opportunities.

Findings

Summarizes key adversarial training algorithms used in emotional AI.

Identifies challenges in applying adversarial training to affective computing.

Suggests future research directions for advancing emotional AI systems.

Abstract

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities.