TL;DR
An Aspect-guided dual-branch fusion network (ADFN) to enhance sentiment prediction by incorporating external knowledge and integrating coarse and fine information is proposed, which incorporates syntactic dependency information to complement and enrich the textual semantic representations.
Experimental results on the MVSA-Single and MVSA-Multiple datasets show that the proposed method improves performance in image–text multimodal sentiment classification, thereby validating the effectiveness of combining semantic enhancement with difference-aware modeling.
In multimodal sentiment analysis, textual, acoustic, and visual modalities often contain redundant and noisy information. Such information increases model complexity and weakens core sentiment representations, degrading accuracy and robustness. To address this issue, we propose CLIBN, a multimodal sentiment recognition...
This work introduces an Enhanced Affinity Fusion module that explicitly strengthens aspect–opinion relational modeling by selectively integrating complementary attention mechanisms, and proposes Label-Guided Data Amplification (LGDA), which enhances supervision diversity and domain robustness through label-driven text...
FUSE-Net is proposed, a Factorized and Unified Semantic Enhancement framework that decomposes each modality into shared, specific, and noise subspaces and applies contrastive learning, an information-gain constraint, and duality constraints for structured regularization to preserve task-relevant semantics during factor...
A Modality Dropout strategy is first introduced at the input stage to alleviate over-reliance on a sin-gle modality and improve robustness and the proposed Hierarchical Global-Local Interaction and Refinement framework for Multimodal Sentiment Analysis (HGLIR) is proposed.
MGSI first encodes audio and visual streams at short-, medium-, and long-range temporal scales, preserving both local variations and global affective trends, and applies polarity- and intensity-aware enhancement to better handle ambiguous and near-neutral samples.
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