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

Paper recorded by Signals 4 on 2026-09-24 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-24 on arXiv · recorded by Signals 4 on 2026-09-25

Category: cs.CL · 自然语言处理 · first seen 2026-09-25

Abstract

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 th

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#2 most recent of 252 cs.CL papers we have recorded · ↑ newer: JevOut: Natural Context Can Flip Decision Models · ↓ older: ARGUS: Role-Aware Event Knowledge Graphs for U.S. Employment-Discrimin
Cite this page: SemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete Data: the #2 most recent of 252 cs.CL papers we have recorded (as of 2026-09-24). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/semmsa-latent-semantic-aided-robust-multimodal-sentiment-analysis-with-incomplet.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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