计算机应用研究2026，Vol.43Issue(3)：664-671,8.DOI:10.19734/j.issn.1001-3695.2025.07.0269
模态缺失场景下基于生成重构和交互式自挖掘的多模态情感分析
Multimodal sentiment analysis based on generative reconstruction and interactive self-mining in scenario of missing modalities
摘要
Abstract
In multimodal sentiment analysis tasks,real-world applications often face modality missing issues.Existing methods for missing modality generation heavily depend on automatically generated modality representations,which amplifies generation errors and limits generalization ability.To address this,this paper proposed the PRM framework.In both single-modal and dual-modal missing scenarios,the framework firstly used generative prompts and available modality information to estimate the mis-sing modality.It then designed a dual-modality-supported reconstruction mechanism that reduced single-source generation errors effectively.In the fusion phase,the framework introduced a self-mining operator to explicitly learn deep semantic features from non-missing modalities,and used a zero-slot insertion strategy to aggregate global contextual information.Experimental results show that,in both single-modal and dual-modal missing scenarios on the CMU-MOSI and CMU-MOSEI datasets,the PRM model improves accuracy and F1 by approximately 1％～3％on average.Moreover,the model demonstrates robust generaliza-tion ability in dynamic missing and cross-dataset transfer experiments,confirming its effectiveness and robustness in complex missing scenarios.关键词
多模态情感分析/模态缺失/提示生成/重构/自挖掘/零向量位Key words
multimodal sentiment analysis/missing modality/generative prompts/reconstruction/self-mining/zero-slot in-sertion分类
信息技术与安全科学引用本文复制引用
冯广,周科栋,伍文燕,黄俊辉,林忆宝,刘馨婷,赵志文,苏旭..模态缺失场景下基于生成重构和交互式自挖掘的多模态情感分析[J].计算机应用研究,2026,43(3):664-671,8.基金项目
国家自然科学基金重点项目(62237001) （62237001）
广东工业大学教育信息化教改专项资助项目(211230073) （211230073）
