Prompt-matching synthesis model for missing modalities in sentiment analysis

In multimodal sentiment analysis, commentary videos often lack certain sentences or frames, leaving gaps that may contain crucial sentiment cues. Current methods primarily focus on modal fusion, overlooking the uncertainty of missing modalities, which results in underutilized data and less complete and less accurate sentiment analysis. To address these challenges, we propose a prompt-matching synthesis model to handle missing modalities in sentiment analysis. First, we develop unimodal encoders using prompt learning to enhance the model's understanding of inter-modal relationships during feature extraction. Learnable prompts are introduced before textual modalities, while cross-modal prompts are applied to acoustic and visual modalities. Second, we implement bidirectional cross-modal matching to minimize discrepancies among shared features, employing central moment discrepancy loss across multiple modalities. A comparator is designed to infer features based on the absence of one or two modalities, allowing for the synthesis of missing modality features from available data. Finally, the synthesized modal features are integrated with the initial features, optimizing the fusion loss and central moment discrepancy loss to enhance sentiment analysis accuracy. Experimental results demonstrate that our method achieves strong performance on multiple datasets for multimodal sentiment analysis, even with uncertain missing modalities.