Multimodal Sentiment Analysis (MSA), a pivotal task in affective computing, aims to enhance sentiment understanding by integrating heterogeneous data from modalities such as text, images, and audio. However, existing methods continue to face challenges in semantic alignment, modality fusion, and interpretability. To address these limitations, we propose VBCSNet, a hybrid attention-based multimodal framework that leverages the complementary strengths of Vision Transformer (ViT), BERT, and CLIP architectures. VBCSNet employs a Structured Self-Attention (SSA) mechanism to optimize intra-modal feature representation and a Cross-Attention module to achieve fine-grained semantic alignment across m... More >