The authors address the problem of incomplete multimodal data in sentiment analysis, where modalities like text, audio, and video may be partially missing.
Latent semantics from LLMs improve sentiment analysis with missing data
SemMSA aligns LLM-derived semantic states with all modalities via spectral alignment, achieving state-of-the-art results on three benchmarks.
Academic
Wenhao Li · Zhibin Wu · Chong Xiao · Qiangchang Wang
Shandong University · Shenzhen Loop Area Institute
Research Digest··3 min read
Li et al.
Why this paper
From Shandong University and Shenzhen Loop Area Institute
In one line
SemMSA uses LLM-derived semantics and spectral alignment for robust multimodal sentiment analysis with incomplete data.
What we could check
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