计算机应用研究2025，Vol.42Issue(9)：2583-2589,7.DOI:10.19734/j.issn.1001-3695.2025.03.0038
基于多任务联合学习与自适应融合的多模态情感分析模型
Multimodal sentiment analysis model based on multi-task joint learning and adaptive fusion
摘要
Abstract
To address feature redundancy and noise caused by modal heterogeneity in multimodal sentiment analysis,this pa-per proposed a novel model named MTL-SAF.The model built a multi-task sentiment analysis module to process multimodal and single-modal tasks simultaneously.This design captured inter-modal heterogeneous features more comprehensively.It intro-duced a self-adaptive fusion mechanism with dynamic weight allocation to suppress redundant information.It applied a multi-scale feature extraction strategy to integrate both low-level and high-level features,enhancing sentiment representation.Experi-ments on SIMS,MOSI,and MOSEI datasets show that MTL-SAF achieves higher accuracy and F,scores than existing baseline models.Results demonstrate strong performance in handling modal heterogeneity and confirm the model's effectiveness in im-proving feature representation.关键词
多模态情感分析/多任务学习/异质特征/自适应融合/多尺度特征/多模态融合Key words
multimodal sentiment analysis/multi-task learning/heterogeneous characteristics/self-adaptive fusion/multi-scale feature/multimodal fusion分类
信息技术与安全科学引用本文复制引用
樊继冬,仲兆满,韩天乐,李梦晗,崔心如,徐瑾..基于多任务联合学习与自适应融合的多模态情感分析模型[J].计算机应用研究,2025,42(9):2583-2589,7.基金项目
国家自然科学基金资助项目(72174079) （72174079）
江苏省"青蓝工程"大数据优秀教学团队资助项目(2022-29) （2022-29）
连云港市重点研发计划(产业前瞻与关键核心技术)资助项目(CG2323) （产业前瞻与关键核心技术）
