Context over Categories: Implementing the Theory of Constructed Emotion with LLM-Guided User Analysis

Abstract

Emotion analysis is a critical research area with applications in content moderation and personalized systems. Many existing approaches rely on Ekman’s universal emotions theory, which reduces emotions to static categories, neglecting their complexity and contextual variability. This work introduces a novel, context-aware approach based on Lisa Feldman Barrett’s Theory of Constructed Emotion. A key contribution is the development of the “context sphere,” a personalized construct derived from user behavior data. To our knowledge, this is the first operationalization for computational methods. A context-aware emotion analysis pipeline was developed, incorporating advanced Large Language Model (LLM) prompting strategies like role-play and controlled generation. A case study in content moderation demonstrates how the “context sphere” enables contextually aware emotion analyses. Future directions include refining the framework, advancing LLM methodologies, and conducting user studies. This research lays the foundation for more human-centered, ethical, and effective emotion analysis systems.