The field of multimodal sentiment analysis is moving towards more efficient and interpretable models, with a focus on integrating multiple modalities such as text, audio, and visual content. Recent developments have highlighted the importance of dynamic fusion processes, adaptive arbitration mechanisms, and parameter-efficient fine-tuning strategies. Noteworthy papers in this area include PGF-Net, which achieves state-of-the-art performance with a lightweight model, and MLLMsent, which demonstrates the potential of multimodal large language models for sentiment reasoning. Additionally, the Structural-Semantic Unifier (SSU) framework has shown promise in integrating modality-specific structural information and cross-modal semantic grounding for enhanced multimodal representations. The M3HG model has also made significant contributions to emotion cause triplet extraction in conversations, introducing a novel multimodal heterogeneous graph to capture emotional and causal contexts.