Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
It addresses the problem of improving emotional AI systems for researchers and practitioners, but is incremental as it is a review paper.
This paper provides a comprehensive overview of applying adversarial training to affective computing and sentiment analysis, explaining various algorithms and discussing future research directions to facilitate development in these fields.
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.