Guilt Detection

Guilt detection research focuses on automatically identifying expressions of guilt in text, a complex emotion previously understudied in natural language processing. Current efforts utilize advanced machine learning models, including transformer-based architectures like BERT and RoBERTa, along with traditional methods, to analyze textual data and classify instances of guilt. This research is significant for advancing our understanding of emotion recognition in artificial intelligence and has implications for applications such as ethical AI development and improving human-computer interaction. Furthermore, investigations into the evolutionary basis of guilt are exploring the interplay between social and non-social forms of this emotion in multi-agent systems.