SemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete Data
Understanding emotions from videos even when audio or visuals go missing
When analyzing sentiment in videos, audio, and text together, missing data usually forces systems to guess or fill in gaps poorly. A new method called SemMSA uses language models to build a solid semantic foundation that works with all three types of information simultaneously, avoiding unreliable reconstruction and achieving better accuracy on standard benchmarks.
Video platforms, content moderation systems, and customer feedback analysis often face incomplete data—broken audio, poor video quality, or missing captions. Better sentiment detection with incomplete data means more reliable content understanding without requiring perfect recordings, making automated systems more practical for real-world video analysis.