Incorporating Syntax Enhancement and Noise Reduction for Aspect-Based Sentiment Analysis Model

doi:10.3778/j.issn.1002-8331.2303-0452

Abstract

Abstract: Aspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of a given aspect word in a sentence. Recent research has mainly used dependency syntax information to implicitly associate the sentiment interaction between aspect words and target words. However, combining dependency syntax information lacks the recognition of local context information centered on aspect words. In addition, modeling complex syntax information equivalently introduces noise that can harm model performance. A neural network model that combines syntax enhancement and noise reduction is proposed to address the issues present in previous research to address the issues present in previous research. This neural network model, a neural network model that combines syntax enhancement and noise reduction, is proposed. This method integrates component information based on dependency syntax information, allowing the model to focus on global dependencies between words and not only focus on global dependencies between words but also on local dependencies centered on aspect words. Furthermore, to reduce noise interference from syntactic information, the model weakens noise interference based on the distance information of the dependency syntax tree. Finally, the model is tested on four benchmark datasets and outperforms baseline models on all datasets.

Key words: aspect-based sentiment analysis (ABSA), dependency syntax, component information, position information

摘要： 方面级情感分析的主要目标是判断句子中给定方面词的情感极性。最近的研究主要采用依存句法信息，隐式地关联方面词与目标词之间的情感交互信息。但结合依存句法信息方法，缺乏识别以方面词为中心的局部上下文信息。此外，对繁杂的语法信息进行等价建模，会引入损害模型性能的噪声。针对以往研究中存在的问题，提出了结合语法增强与噪声削减的神经网络模型。该方法在依存句法信息基础上融合了成分信息，使模型不仅能关注词与词之间的全局依赖信息，同时能关注以方面词为中心的局部依赖信息。同时，为了降低语法信息的噪声干扰，模型以依存句法树的距离信息为依据，弱化了远距离的噪声干扰。最后，模型在四个基准数据集上进行了实验，并在所有数据集上的性能都优于基线模型。

关键词: 方面级情感分析, 依存句法, 成分信息, 位置信息

WANG Hongsong, LI Jiazhan, YE Haoxian, TAO Ran. Incorporating Syntax Enhancement and Noise Reduction for Aspect-Based Sentiment Analysis Model[J]. Computer Engineering and Applications, 2024, 60(13): 152-161.

汪红松, 李嘉展, 叶浩贤, 陶然. 结合语法增强与噪声削减的方面级情感分析模型[J]. 计算机工程与应用, 2024, 60(13): 152-161.

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