HEART: Emotionally Grounded Video Captioning via Hierarchical Emotion-Aligned Representation

Emotional Video Captioning (EVC) seeks to generate video descriptions that are both factually accurate and emotionally expressive. However, existing approaches often lack structured semantic grounding and fine-grained temporal modeling, leading to incomplete or emotionally inconsistent captions. To address these issues, we propose HEART (Hierarchical Emotion-Aligned Representation with Temporal structure), a unified framework that jointly models hierarchical visual semantics and multi-scale temporal context. Specifically, HEART introduces a Hierarchical Semantic Extraction Module that decomposes visual content into entity-, action-, and event-level representations, providing a rich foundation for multi-level emotional alignment. A Temporal Pyramid Module captures short- and long-range temporal dependencies through multi-scale convolution, enabling temporally coherent captioning. Together, these components enable HEART to generate captions that are both emotionally grounded and temporally complete. To support this framework, we construct EmoStruct, a new benchmark dataset with fine-grained emotional annotations at the subject and predicate levels. Experiments on EmoStruct and public datasets demonstrate that HEART significantly outperforms prior methods in both semantic and emotional dimensions.