SemMSA: Robust Multimodal Sentiment Analysis with Incomplete Data
Read this because This framework solves the common problem of missing modalities in MSA by using latent semantics instead of reconstruction, offering a robust alternative for real-world applications.
SemMSA uses LLMs and spectral alignment to improve sentiment analysis when data is missing, achieving state-of-the-art results.