计算机工程2025，Vol.51Issue(9)：80-90,11.DOI:10.19678/j.issn.1000-3428.0069705
结合局部感知与多层次注意力的多模态方面级情感分析
Multimodal Aspect-Based Sentiment Analysis Combining Local Perception and Multi-Level Attention
摘要
Abstract
Multimodal Aspect-Based Sentiment Analysis(MABSA)aims to analyze the sentiment polarity of aspect words derived from text and image pairs.Existing methods primarily focus on extracting emotional features from both images and texts.However,the various features of images and texts may not necessarily be effective for the final sentiment analysis.Both images and text often contain a large amount of redundant and noisy information outside the areas related to aspect words,and different regions of images and text may be related to different aspect words.In the process of approximately establishing image text feature extraction,noise is introduced into multimodal aspect-level sentiment analysis tasks.In addition,the sentiment polarity related to aspect words in images and text may still be the opposite,implying an interactive information between the two.To address these issues,this paper proposes a multimodal aspect-level sentiment analysis model that combines local perception and multilevel attention.Specifically,the local perception module is designed to simultaneously select text content and image regions that are semantically relevant to aspect words.Subsequently,to improve the accuracy of sentiment aggregation,a multilevel attention module is introduced into the model,which uses a bottleneck attention mechanism to extract modal interaction information.The experimental results show that the model achieves State-Of-The-Art(SOTA)performance on the Twitter2015,Twitter2017,and Multi-ZOL datasets,significantly outperforms similar models.关键词
多模态方面级情感分析/局部感知/多层次注意力/局部上下文/瓶颈注意力Key words
Multimodal Aspect-Based Sentiment Analysis(MABSA)/local perception/multi-level attention/local context/bottleneck attention分类
信息技术与安全科学引用本文复制引用
曾碧卿,姚勇涛,谢梁琦,陈鹏飞,邓会敏,王瑞棠..结合局部感知与多层次注意力的多模态方面级情感分析[J].计算机工程,2025,51(9):80-90,11.基金项目
广东省普通高校人工智能重点领域专项(2019KZDZX1033) （2019KZDZX1033）
广东省基础与应用基础研究基金(2021A1515011171) （2021A1515011171）
广州市基础研究计划基础与应用基础研究项目(202102080282). （202102080282）
