Sep 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 48 references
TL;DR
A Variational Autoencoder-Based Multi-Granularity Missing-Aware Dual-Path Fusion Network (VMMD) with contrastive learning-driven Cross-Path Semantic Consistency Constraint (CSCC), which mitigates semantic representation inconsistency caused by granularity heterogeneity between supplementary information and proxy features, thereby enhancing sentiment polarity prediction under missing-modality conditions.
This work introduces an adaptive variational information bottleneck to model modality-wise uncertainty and perform quality-aware information compression, thereby suppressing redundant noise in unreliable modalities and designs a reliability-aware cross-sample enhancement strategy that retrieves high-confidence, semanti...
Meng-Hua Jiang, Hao-Kai Gao, Xian-Gui Kang et al.· 0 citations
A new completeness estimation approach is introduced that quantifies the degree of sentiment-relevant information preserved in incomplete data to guide the reconstruction of missing semantics and enables more accurate semantic reconstruction, leading to more precise sentiment prediction.
Han-Jun Choi, Byunggill Joe, Saim Shin et al.· 0 citations
Multimodal sentiment analysis aims to improve cross-modal fusion to understand sentiment better. Most existing methods rely on single-stage fusion or local cross-modal interactions, making it difficult to fully capture relationships among modalities, thereby limiting their sentiment representation capabilities and...
Guang-Yu Mu, Jia-Xiu Dai, Yuan-Yuan Yue et al.· Aslib Journal of Information...· 0 citations
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.
Bin Song, Wenjing Liu, Zhi Liang et al.· Signal, Image and Video Proc...· 0 citations
Multimodal sentiment analysis on social media data presents unique challenges due to label noise, modality conflicts, and class imbalance inherent in annotated image-text datasets. The proposed work presents CLIP-CrossFusion Net, a novel multimodal sentiment analysis framework that integrates the Contrastive Language-I...
B. Harish, C. Roopa, M. S. Kendagannaswamy et al.· International Journal of Adv...· 0 citations
SemMSA, a latent semantic-aided framework that constructs rich sentiment-relevant semantics with LLMs, fully integrating with all modalities via anchor-free spectral alignment, is proposed.
Wen-Hao Li, Zhi-Bin Wu, Chong-Yao Xiao et al.· 0 citations
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