现代情报2026，Vol.46Issue(2)：77-90,14.DOI:10.3969/j.issn.1008-0821.2026.02.007
基于对比学习与大语言模型增强的多模态方面级情感分析模型
A Multimodal Aspect-Based Sentiment Analysis Model Enhanced by Contrastive Learning and Large Language Models
摘要
Abstract
[Purpose/Significance]This study addresses challenges such as data sparsity and class imbalance in the field of Multimodal Aspect-Based Sentiment Analysis(MABSA),and explores the application and performance of large lan-guage models in MABSA tasks.[Methods/Process]The paper proposed a multimodal aspect-based sentiment analysis model named HLCL-GLM4,which integrates data enhancement using large language models and HiLo-attention-based contrastive learning.The model leveraged ChatGLM4-Flash to perform data enhancement,extracted textual and visual fea-tures using BART embeddings and Faster R-CNN,respectively,and encoded image features with HiLo attention mecha-nism.The model employed a self-supervised contrastive learning strategy to facilitate multimodal feature learning and fusion,which improves sample diversity and the expressiveness of sentiment semantics.[Result/Conclusion]Experimental results show that HLCL-GLM4 achieves superior performance on both the Twitter-15 and Twitter-17 datasets.Specifi-cally,compared with the best baseline model,HLCL-GLM4 improves the F1-score by 1.6%on Twitter-15 and by 0.8%on Twitter-17.关键词
多模态方面级情感分析/对比学习/大语言模型/提示工程/数据增强Key words
multimodal aspect-based sentiment analysis/contrastive learning/large language models/prompt engineering/data enhancement分类
信息技术与安全科学引用本文复制引用
余传明,蒋展,孙邹驰..基于对比学习与大语言模型增强的多模态方面级情感分析模型[J].现代情报,2026,46(2):77-90,14.基金项目
国家自然科学基金面上项目"基于知识增强的科技文献创新识别与评价模型研究"(项目编号:72374219) （项目编号:72374219）
"面向跨语言观点摘要的领域知识表示与融合模型研究"(项目编号:71974202). （项目编号:71974202）
