SOURCE-LINKED INTELLIGENCE

SemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete Data

arXiv · AI, language, vision and robotics · article · Sep 24, 2026 · UTC

Recent research on Multimodal Sentiment Analysis (MSA) has focused on learning from language, visual, and acoustic modalities with incomplete data to infer human sentiment. Most studies typically compensate for missing information by reconstructing modality features or designing complicated fusion mechanisms. However, these methods still suffer from spurious generation and noisy guidance due to the lack of high-level semantic grounding in partially observed multimodal evidence. To address these issues, we propose SemMSA, a latent semantic-aided framework that constructs rich sentiment-relevant

Read original source ↗ Open in workspace

recordType

paper

region

Global