Cross-modal Contrastive Fusion Network for Sentiment Analysis with Dynamic Semantic Diffusion

As the importance of public engagement monitoring grows in the face of complex social challenges, analyzing social media data from multiple perspectives has become crucial for understanding diverse public sentiments. Current methods often fall short in effectively supporting decision-making due to their inability to dynamically adapt to the evolving nature of social media discussions. They rely on static strategies that fail to capture the intricate correlations between features across different views, making it difficult to identify sentiment patterns that emerge through complex dependencies in user-generated content. To address these shortcomings, we propose a novel deep multi-view contrastive fusion network (SMOM) designed for comprehensive public opinion monitoring in social media. SMOM features a view-specific feature extractor that captures inherent information within each view. It then employs cross-view contrastive learning to maximize mutual information between view-specific representations, ensuring consistency between views and bridging semantic gaps from an information theory perspective. Furthermore, SMOM implements structure-driven adaptive fusion by combining gate strategies and graph neural networks, enabling the adaptive integration of complementary information. These components work together seamlessly to uncover sentiment patterns, achieving thorough and accurate monitoring of public opinions in social media. Experimental evaluations on social media datasets demonstrate SMOM’s superior performance in detecting nuanced public sentiments.