MindTracker: Unveiling Implicit Emotions in Long-Horizon Dialogues

Abstract

Affective computing has achieved notable success in recognizing explicit emotions from short, isolated dialogue segments. However, human emotions are often implicitly expressed, internally regulated, and dynamically evolve over extended interactions. Existing models struggle to disentangle internal emotional states from external expressions, and fail to capture the emotional inconsistency that emerges across long-horizon dialogues. To address this limitation, we introduce Emotional Inconsistency Analysis (EIA), a novel task that aims to identify and reason about discrepancies between implicit and explicit emotions over long-term conversational contexts. To support this task, we construct the MaskDialog dataset carefully curated from television drama and large language models (LLMs). We further propose two LLM-based baseline approaches, i.e., One-shot Self-consistent Inference and Cascaded Multi-step Inference, and conduct comprehensive analyses on dialogue construction strategies and inference behaviors. Extensive experiments across multiple mainstream LLMs reveal that EIA remains highly challenging, particularly in modeling implicit emotional trajectories and cross-turn inconsistency. Overall, EIA reframes emotion understanding from short-term recognition to longitudinal, implicit emotion tracking, with implications for dialogue systems and human–computer interaction.