EvoGraph-Former: Affective Narrative Evolution Modeling for Unified Sentiment-Emotion Analysis in Multimodal Conversation

Multimodal sentiment analysis (MSA) and emotion recognition in conversation (ERC) are two crucial tasks for understanding human affective behavior, aiming to detect sentiment polarity and emotion category from multimodal cues in conversational videos. Despite recent advances leveraging dialogue-level graph structures and Transformer-based frameworks to capture cross-modal contextual dependencies, existing approaches still struggle with two persistent challenges: the fragmentation in modeling narrative-affective evolution and the semantic inconsistency caused by heterogeneous modality drift. To address these issues, we propose EvoGraph-Former, a unified framework for multimodal dialogue affective understanding across both MSA and ERC tasks. Specifically, EvoGraph-Former constructs a bi-channel evolutionary graph that explicitly integrates narrative and affective trajectories to capture complex intra-dialogue affective dynamics. Furthermore, an affect-anchor alignment mechanism is introduced to perform dual-stage semantic calibration on multimodal features, both before and after graph construction, thereby mitigating modality discrepancy at its source. Extensive experiments on four widely used benchmarks demonstrate that EvoGraph-Former outperforms prior state-of-the-art models across MSA and ERC, highlighting its effectiveness and generalizability.