2026 EACL EACL 2026

Appraisal Trajectories in Narratives Reveal Distinct Patterns of Emotion Evocation

Abstract

AbstractUnderstanding emotion responses relies on reconstructing how individuals appraise events. While prior work has studied emotion trajectories and inherent correlations with appraisals, it has considered appraisals only in a snapshot analysis. However, because appraisal is a complex, sequential process, we argue that it should be analyzed based on how it unfolds throughout a narrative. In this study, we investigate whether trajectories of appraisals are distinctive for different emotions in five-event stories – narratives where each of five sentences describes an event. We employ zero-shot prompting with a large language model to predict appraisals on sub-sequences of a narrative. We find that this approach is effective in identifying relevant appraisals in narratives, without prior knowledge of the evoked emotion, enabling a comprehensive analysis of appraisal trajectories. Furthermore, we are the first to quantitatively identify typical patterns of appraisal trajectories that distinguish emotions. For example, a rising trajectory for self-responsibility indicates trust, while a falling trajectory suggests anger.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Natural Language Processing
🧭 Keyword Pioneer — emotion trajectory
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio