Dan Wang1, Zhoubin Li1, Yuze Xia1,2,*, Zhenhua Yu1,*
CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5823-5845, 2025, DOI:10.32604/cmc.2025.071656
- 23 October 2025
Abstract Multimodal Aspect-Based Sentiment Analysis (MABSA) aims to detect sentiment polarity toward specific aspects by leveraging both textual and visual inputs. However, existing models suffer from weak aspect-image alignment, modality imbalance dominated by textual signals, and limited reasoning for implicit or ambiguous sentiments requiring external knowledge. To address these issues, we propose a unified framework named Gated-Linear Aspect-Aware Multimodal Sentiment Network (GLAMSNet). First of all, an input encoding module is employed to construct modality-specific and aspect-aware representations. Subsequently, we introduce an image–aspect correlation matching module to provide hierarchical supervision for visual-textual alignment. Building upon these components, More >
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