Controlling Decision Drift in Multimodal Sentiment Analysis with Missing Modalities

ArXi:2605.16889v1 Announce Type: new Multimodal sentiment analysis relies on textual, acoustic, and visual signals, yet real-world data often suffer from modality missing and quality imbalance. Existing methods generate features for modality missing from available ones, but differences in expression mechanisms and sentiment dynamics across modalities may cause the generated features to deviate from true distributions and mislead prediction.