A New Advanced Multimodal Sentiment Classification Through Combined Attention Mechanisms: A-MSDA

Interest in multimodal sentiment analysis has grown significantly due to the widespread sharing of text and images on social platforms. Existing approaches often emphasize either sentiment features within textual–visual data or the correlation between modalities, leaving gaps in effectively capturing both aspects simultaneously. To address these limitations, we propose the Advanced Multimodal Sentiment Analysis with Dual Attention (A-MSDA) model, which integrates self-attention and cross-modal attention mechanisms in a unified dual-attention framework. This design enables robust multimodal fusion by extracting salient textual and visual features while modeling their image–text interaction comprehensively. Experimental evaluation on MVSA-Single and MVSA-Multiple datasets demonstrates that A-MSDA achieves notable improvements in accuracy and F1-score, outperforming existing techniques by up to 3.4% in F1-score on MVSA-Multiple, while maintaining competitive performance on MVSA-Single. These results highlight the potential of A-MSDA to advance research in deep multimodality and sentiment analysis.