知识辅助和强化句法驱动的方面级情感分析

doi:10.11896/jsjkx.250600117

摘要/Abstract

摘要： 方面级情感分析旨在对齐方面和其相应的意见表达,以识别特定方面的情感极性。现有的基于依赖树的图神经网络模型在方面级情感分析中取得了显著的性能提升,但大多数研究未充分利用句法依赖树的完整信息,通常忽略了句法依赖距离信息或依赖标签信息。这种忽视可能导致在含有多个方面的句子中,意见词与相应的方面词无法有效对齐。针对上述问题,构造一种知识辅助和强化句法驱动的网络模型。具体来说,首先通过引入外部知识库设计一个意见词感知模块,以增强模型对句子中意见表达的识别能力。然后,利用强化学习指导句法距离图的构建,并将其与基于单词关系和依赖标签构建的动态句法标签图进行启发式集成,从而提高对给定方面捕获相关意见表达的准确性和全面性。此外,采用方面关注注意力机制来更好地处理句法结构不明确的句子。在3个公共数据集上进行广泛的实验,结果验证了该模型的有效性。

关键词: 方面级情感分析, 情感词典, 句法依赖树, 强化学习, 图卷积网络, 注意力机制, 深度学习

Abstract: Aspect-based sentiment analysis aims to align aspects with their corresponding opinion expressions to identify the sentiment polarity of specific aspects.Existing dependency tree-based graph neural network models have achieved significant performance improvements in aspect-based sentiment analysis.However,most studies fail to fully exploit the complete information of the syntactic dependency tree,often overlooking syntactic dependency distance or dependency label information.This limitation may prevent effective alignment between opinion words and their corresponding aspect terms,particularly in sentences containing multiple aspects.To address these issues,a knowledge-assisted and reinforced syntax-driven network model is constructed.Specifically,an opinion word perception module is designed by incorporating external knowledge information to enhance the model’s ability to recognize opinion expressions in sentences.Then,reinforcement learning is employed to guide the construction of the syntactic distance graph.This graph is then heuristically integrated with the dynamic syntactic label graph,which is built based on word relations and dependency labels,thereby improving the accuracy and comprehensiveness of capturing relevant opinion expressions for a given aspect.Additionally,an aspect-focused attention mechanism is employed to better handle sentences with ambiguous syntactic structures.Extensive experiments conducted on three public datasets validate the effectiveness of the proposed model.

Key words: Aspect-based sentiment analysis, Sentiment lexicon, Syntax dependency tree, Reinforcement learning, Graph convolution networks, Attention mechanism, Deep learning

中图分类号:

TP391

郑诚, 班晴晴. 知识辅助和强化句法驱动的方面级情感分析[J]. 计算机科学, 2026, 53(4): 406-414. https://doi.org/10.11896/jsjkx.250600117

ZHENG Cheng, BAN Qingqing. Knowledge-assisted and Reinforced Syntax-driven for Aspect-based Sentiment Analysis[J]. Computer Science, 2026, 53(4): 406-414. https://doi.org/10.11896/jsjkx.250600117

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