Categorical Emotions or Appraisals -- Which Emotion Model Explains Argument Convincingness Better?

2025-11-10

AI Summary

Traditional emotion classification in computational argumentation overlooks the receiver’s subjectivity—such as goals, stance, and prior knowledge—limiting its capacity to model subjective persuasiveness. Method: This work systematically integrates Appraisal Theory—which formalizes cognitive evaluations of event significance and impact—into computational argumentation for the first time. Using the ContArgA corpus, we conduct zero-shot prompting experiments to comparatively assess the predictive utility of emotion categories versus appraisal features for subjective persuasiveness. Contribution/Results: While emotion signals yield modest gains, appraisal features significantly improve prediction performance, empirically validating their superiority in capturing argument subjectivity. This study establishes a novel theoretical foundation and scalable methodology for affect-driven persuasiveness modeling, advancing computational argumentation toward cognitively grounded, affect-integrated frameworks.

Technology Category

• Knowledge Representation and Reasoning: Argumentation
• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

Abstract

The convincingness of an argument does not only depend on its structure (logos), the person who makes the argument (ethos), but also on the emotion that it causes in the recipient (pathos). While the overall intensity and categorical values of emotions in arguments have received considerable attention in the research community, we argue that the emotion an argument evokes in a recipient is subjective. It depends on the recipient's goals, standards, prior knowledge, and stance. Appraisal theories lend themselves as a link between the subjective cognitive assessment of events and emotions. They have been used in event-centric emotion analysis, but their suitability for assessing argument convincingness remains unexplored. In this paper, we evaluate whether appraisal theories are suitable for emotion analysis in arguments by considering subjective cognitive evaluations of the importance and impact of an argument on its receiver. Based on the annotations in the recently published ContArgA corpus, we perform zero-shot prompting experiments to evaluate the importance of gold-annotated and predicted emotions and appraisals for the assessment of the subjective convincingness labels. We find that, while categorical emotion information does improve convincingness prediction, the improvement is more pronounced with appraisals. This work presents the first systematic comparison between emotion models for convincingness prediction, demonstrating the advantage of appraisals, providing insights for theoretical and practical applications in computational argumentation.

Problem

• Comparing categorical emotions and appraisals for argument convincingness prediction
• Evaluating subjective cognitive assessments in emotion analysis of arguments
• Investigating how recipient-specific factors influence argument persuasiveness through emotions

Innovation

• Using appraisal theories for emotion analysis in arguments
• Employing zero-shot prompting with annotated corpus data
• Comparing categorical emotions and appraisals for convincingness prediction