Unexpected Emotional Triggers: Mining Outlier Emotional Shifts in Persuasive Dialogues

While S. Villata et al. (2017) explored correlations between emotions, engagement, and argumentation, their focus was on general patterns rather than anomalies. My idea is to systematically detect and analyze outlier emotional responses—cases where a participant’s emotional state shifts in an unexpected direction (e.g., from neutral to highly positive after a weak argument, or from angry to calm following a concession). By leveraging advanced NLP emotion detection (see Singh et al., 2025), we could mine large datasets of negotiation and persuasive conversations for these anomalies. The novelty here is in focusing on these emotional “jolts” as potential signals of hidden persuasive mechanisms or misalignments between argument quality and participant response. This could reshape how we understand persuasion tactics, help design emotionally intelligent negotiation agents, and provide new tools for argumentation mining that go beyond rational content analysis.

References:

1. Emotions and personality traits in argumentation: An empirical evaluation. S. Villata, Elena Cabrio, I. Jraidi, Mohamed S. Benlamine, Maher Chaouachi, C. Frasson, Fabien L. Gandon (2017). Argument Comput..
2. A Review Article for Argumentation Mining of Text through Machine Learning Techniques and Strategies. Rudrendra Bahadur Singh, Shobhit Sinha, Ankita Singh (2025). Journal of Information Systems Engineering & Management.