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

arXiv: https://arxiv.org/abs/1809.08927 · HTML: https://mlsanity.com/paper/1809.08927/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2018.

## Built on by (most cited first, 7 of 7)

- [Domain-Incremental Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition](https://mlsanity.com/paper/2103.08637/) (2021)
- [Morphset:Augmenting categorical emotion datasets with dimensional affect labels using face morphing](https://mlsanity.com/paper/2103.02854/) (2021)
- [Put Chatbot into Its Interlocutor's Shoes: New Framework to Learn Chatbot Responding with Intention](https://mlsanity.com/paper/2103.16429/) (2021)
- [Towards Fair Affective Robotics: Continual Learning for Mitigating Bias in Facial Expression and Action Unit Recognition](https://mlsanity.com/paper/2103.09233/) (2021)
- [Adversarial Training for a Hybrid Approach to Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2111.14988/) (2021)
- [Machine Unlearning for Speaker-Agnostic Detection of Gender-Based Violence Condition in Speech](https://mlsanity.com/paper/2411.18177/) (2024)
- [Cognition-Inspired Dual-Stream Semantic Enhancement for Vision-Based Dynamic Emotion Modeling](https://mlsanity.com/paper/2604.12777/) (2026)

## Abstract (arXiv)

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.