计算机应用研究2026，Vol.43Issue(6)：1618-1627,10.DOI:10.19734/j.issn.1001-3695.2025.10.0430
模态缺失下基于提示学习的多模态情感分析
Prompt-learning-based multimodal sentiment analysis under missing modalities
摘要
Abstract
To address the issues in multimodal sentiment analysis under missing modalities,such as the complex fine-tuning of Transformer models,the indiscriminate use of completed modality data,and the weakened information complementarity caused by modality missing,this paper proposed a prompt-learning-based multimodal sentiment analysis model under missing modali-ties.The model firstly introduced modality-missing prompts to guide the model in identifying whether the current input modality was missing.Secondly,it constructed a quality evaluation mechanism to suppress the interference of low-quality completed mo-dality data on the model.On this basis,it designed and added modality-missing combination prompts into the Transformer of the backbone network to guide the model to dynamically adjust attention computation and cross-modal interaction,improving the model's adaptability to missing-modality scenarios,while avoiding complex fine-tuning of the Transformer backbone and reducing computational cost.Finally,it added a shared fusion layer to map the features of each modality into a unified shared representation space,learning shared semantic information and enhancing cross-modal semantic consistency and information complementarity.Experimental results show that,under six missing-modality combinations,the model improve the average Acc-2 and F1 scores on the CMU-MOSI,IEMOCAP,and CH-SIMS datasets by 1.04～1.87 percentage points compared with the second-best model.In addition,the trainable parameters account for only 6.3％of the total parameters,verifying the ef-fectiveness,robustness,and parameter efficiency of the proposed model.关键词
多模态情感分析/模态缺失/提示学习/质量评估/共享融合Key words
multimodal sentiment analysis/missing modality/prompt learning/quality assessment/shared fusion分类
信息技术与安全科学引用本文复制引用
郑明洲,缪裕青,刘同来,张万桢,蔡国永..模态缺失下基于提示学习的多模态情感分析[J].计算机应用研究,2026,43(6):1618-1627,10.基金项目
国家自然科学基金资助项目(62366010,62366011) （62366010,62366011）
广东省自然科学基金资助项目(2023A1515011230) （2023A1515011230）
桂林电子科技大学研究生教育创新计划资助项目(2025YCXS076) （2025YCXS076）
