Haiyun Ma1This email address is being protected from spambots. You need JavaScript enabled to view it. and Zhonglin Zhang2
1School of Electronic Information and Electrical Engineering, Tianshui Normal University, Tianshui, 741001, China
2School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Received: June 25, 2025 Accepted: August 3, 2025 Publication Date: August 16, 2025
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
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.
Keywords: Social media sentiment analysis; invariant representation learning; structure-driven adaptive fusion.
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