基于知识增强的方面级情感分析方法

doi:10.3969/j.issn.1006-2475.2023.10.001

摘要/Abstract

摘要： 方面级情感分析能够准确判断出句子中方面词的情感极性，在社交、电子商务等领域发挥着重要的作用。现有的方法大多通过序列表示或者注意力机制建模上下文和目标词间的关系，忽略了文本的背景知识以及方面词之间的概念链接，导致学习到的语义关系不够充分。针对上述问题，提出一种基于知识增强的方面级情感分析模型（Aspect Based Sentiment Analysis Model Based on Knowledge Enhancement， ABSA-KE）。首先，通过预训练模型BERT提取特征并得到对应的词向量，并使用解析器获取文本对应的依存关系树，利用BiLSTM和图注意力网络联合建模来学习节点嵌入表示并获得文本向量；其次，使用外部知识库引入不同语境下的方面词知识向量来增强方面级情感分析模型；最后，进行情感分类任务。通过与已有模型对比的实验结果表明，本文所提出的模型在方面级情感分析任务上是有效且合理的。

关键词: 方面级情感分析, 图注意力网络, 外部知识库, BERT, 依存树

Abstract: Aspect based sentiment analysis can accurately determine the emotional polarity of aspect words in sentences， and plays an important role in social networking， e-commerce and other fields. Most of the existing methods model the relationship between context and target words through sequence representation or attention mechanism， but ignore the background knowledge of text and the conceptual links between aspect words， resulting in insufficient semantic relationships learned. To solve the above problems， the Aspect Based Sentiment Analysis Model Based on Knowledge Enhancement （ABSA-KE） is proposed. First， the features are extracted and the corresponding word vector is obtained through the pre-training model BERT， and the dependency tree corresponding to the text is obtained using the parser. Then， the joint modeling of BiLSTM and graph attention network is used to learn the node embedded representation and obtain the text vector. Second， the external knowledge base is used to introduce the aspect word knowledge vector in different contexts to enhance the aspect level emotion analysis model， and finally the emotion classification task is carried out. Compared with the existing models， the experimental results show that the proposed model is effective and reasonable in aspect level emotion analysis tasks.

Key words: , aspect based sentiment analysis； graph attention network； external knowledge base； BERT； dependency tree

中图分类号:

TP391

李诗月, 孟佳娜, 于玉海, 李雪莹, 许英傲. 基于知识增强的方面级情感分析方法[J]. 计算机与现代化, 2023, 0(10): 1-8.

LI Shi-yue, MENG Jia-na, YU Yu-hai, LI Xue-ying, XU Ying-ao. Aspect Based Sentiment Analysis Model Based on Knowledge Enhancement[J]. Computer and Modernization, 2023, 0(10): 1-8.

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