SDG-MLLM: Injecting Structured Dialogue Graphs into MLLM for Multimodal Conversational Aspect-Based Sentiment Analysis

摘要

Multimodal Conversational Aspect-based Sentiment Analysis (MCA BSA) is a challenging task for multimodal dialogue understanding. Existing works often treat the entire dialogue as a flat sequence and feed it into Large Language Models (LLMs) for pipeline-style generation. However, these methods sometimes accumulate errors and overlook critical discourse structure and fine-grained inter-word relations that are essential for accurate sentiment reasoning. To address these limitations, we propose SDG-MLLM, a unified generative framework that integrates Structured Dialogue Graphs into Multimodal LLM (MLLM) for an end-to-end MCABSA. Specifically, we construct heterogeneous dialogue graphs that capture diverse structural relations, including syntactic dependencies, coreference links, speaker turns, reply flow, semantic role labeling, and sentiment propagation paths. These graphs are encoded using a heterogeneous dialogue graph encoder, and the resulting structure-aware graph features are injected into the embedding layer of LLM. Furthermore, SDG-MLLM incorporates aligned multimodal features such as image, audio, and video cues at the utterance level to enable unified and context-aware multimodal reasoning. Experiments on the MCABSA dataset show that SDG-MLLM significantly outperforms strong baselines across multiple tasks. In addition, our method also achieved top performance in the ACM MM 2025 Grand Challenge of MCABSA. Our code is available at https://github.com/Liuxj-Anya/SDG-MLLM.