Enhancing Multimodal Aspect-Based Sentiment Analysis with Adaptive Noise and Aspect Graph Association Learning

HUANG Chen; LIU Hui-jie; ZHANG Yan; YANG Chao; SONG Jian-hua

doi:10.12263/DZXB.20250533

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Enhancing Multimodal Aspect-Based Sentiment Analysis with Adaptive Noise and Aspect Graph Association Learning

PAPERS | 更新时间：2025-12-27

- Enhancing Multimodal Aspect-Based Sentiment Analysis with Adaptive Noise and Aspect Graph Association Learning
- ACTA ELECTRONICA SINICA Vol. 53, Issue 9, Pages: 3397-3409(2025)
- 作者机构：
  
  1.湖北大学计算机学院，湖北武汉 430062
  2.智能感知系统与安全教育部重点实验室，湖北武汉 430062
  3.大数据智能分析与行业应用湖北省重点实验室，湖北武汉 430062
  4.湖北省高校人文社科重点研究基地-绩效评价信息管理研究中心，湖北武汉 430062
  5.湖北大学网络空间安全学院，湖北武汉 430062
- 作者简介：
- 基金信息：
  
  Wuhan Knowledge Innovation Special Project(2023BAA018);Major Project of Hubei Province (JD)(2024BAA008);Major Science and Technology Special Project of Hubei Science and Technology Plan(202311901251001)
- DOI：10.12263/DZXB.20250533
  CLC： TP391;TP399
- Received：22 June 2025，
  
  Accepted：27 August 2025，
  
  Published：25 September 2025
- 稿件说明：
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黄辰, 刘会杰, 张龑, 等. 基于自适应噪声和方面图关联学习增强多模态方面级情感分析[J]. 电子学报, 2025, 53(09): 3397-3409.

HUANG Chen, LIU Hui-jie, ZHANG Yan, et al. Enhancing Multimodal Aspect-Based Sentiment Analysis with Adaptive Noise and Aspect Graph Association Learning[J]. Acta Electronica Sinica, 2025, 53(09): 3397-3409.
黄辰, 刘会杰, 张龑, 等. 基于自适应噪声和方面图关联学习增强多模态方面级情感分析[J]. 电子学报, 2025, 53(09): 3397-3409. DOI：10.12263/DZXB.20250533

HUANG Chen, LIU Hui-jie, ZHANG Yan, et al. Enhancing Multimodal Aspect-Based Sentiment Analysis with Adaptive Noise and Aspect Graph Association Learning[J]. Acta Electronica Sinica, 2025, 53(09): 3397-3409. DOI：10.12263/DZXB.20250533

摘要

多模态方面级情感分析（Multimodal Aspect-Based Sentiment Analysis，MABSA）旨在从多模态输入数据中准确识别方面术语并判定其情感极性.现有研究致力于融合多模态信息以提升情感分析性能.然而，在面临多方面和多情感场景时，它们仍然面临两个关键挑战：（1）缺乏对多模态输入数据中方面术语的全面感知；（2）存在情感语义偏差，现有模型倾向于关注与特定方面术语关联性强的情感语义，而忽略了关联性较低但同样重要的情感语义.为了克服这些问题，本文提出了一种结合自适应噪声和方面图关联学习的新型多模态方面级情感分析方法（Adaptive Noise and Aspect Graph Association Learning，ANAGAL），旨在增强多方面和多情感场景下的分析性能.具体而言，通过专门设计的自适应噪声增强模块以补充方面信息，从而增强模型对方面术语的感知能力，并提高模型鲁棒性.此外，设计方面图关联学习模块来关联所有方面术语，并学习与之相关的情感语义.同时，引入额外的参数进行情感校准，使模型能够学习更多常见的情感语义偏差，从而更准确地捕捉方面术语及其对应的情感极性.在公共数据集上的大量实验评估表明，ANAGAL在克服这些挑战方面表现优异.与现有基线模型相比，ANAGAL在Twitter-2015和Twitter-2017数据集上将精确率分别提升了1.46个百分点和1.56个百分点，在MASAD（Multimodal Aspect Sentiment Analysis Dataset）和EmoMeta数据集上将精确率提升了2.48个百分点和1.55个百分点.

Abstract

Multimodal aspect-based sentiment analysis (MABSA) aims to accurately identify aspect terms and determine their sentiment polarity from multimodal input data. Existing studies focus on integrating multimodal information to improve sentiment analysis performance. However

they still face two critical challenges in multi-aspect and multi-sentiment scenarios: (1) a lack of comprehensive perception of aspect terms in multimodal input data; and (2) sentiment semantic bias

where current models tend to focus on sentiment semantics strongly correlated with specific aspect terms

while ignoring weakly associated yet equally important sentiment cues. To address these issues

we propose a novel multimodal aspect-based sentiment analysis method

ANAGAL (Adaptive Noise and Aspect Graph Association Learning)

which integrates adaptive noise handling and aspect-graph association learning to enhance analytical performance in scenarios involving multiple aspects and multiple sentiments. Specifically

an adaptive noise enhancement module is designed to supplement aspect information

thereby improving the model’s aspect perception and robustness. In addition

an aspect graph correlation learning module is introduced to associate all aspect terms and learn related sentiment semantics. Extra parameters are further incorporated to calibrate sentiment representations

enabling the model to capture more generalized sentiment biases and better identify sentiment polarity associated with each aspect term. Extensive experimental evaluations on public datasets demonstrate that ANAGAL performs exceptionally well in addressing these challenges. Compared to existing state-of-the-art MABSA models

ANAGAL improves precision by 1.46 percentage points and 1.56 percentage points on the Twitter-2015 and Twitter-2017 datasets

and by 2.48 percentage points and 1.55 percentage points on the MASAD (Multimodal Aspect Sentiment Analysis Dataset) and EmoMeta datasets.

关键词

Keywords

references

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