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| Aspect level sentiment analysis based on relation gated graph convolutional network |
Yan-fen CHENG( ),Jia-jun WU,Fan HE |
| School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China |
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Abstract In aspect level sentiment analysis, existing methods struggle to effectively utilize the types of syntactic relations, and the performance of the model is affected by the accuracy of the dependency parsing. To resolve these challenges, an attention augmented relation gated graph convolutional network (ARGCN) model was proposed. The model uses a bidirectional long-short-term memory (BiLSTM) network to learn the sequential feature of sentences, and combines feature with the dependency probability matrix to construct a word graph. Then the model uses a relation gated graph convolutional network (RG-GCN) and an attention augmented network (AAN) to obtain the sentiment features of aspect words from the word graph and the sequential feature of sentences, respectively. Finally, the outputs of RG-GCN and AAN are concatenated as the final sentiment feature of aspect words. Contrastive experiments and ablation experiments were conducted on SemEval 2014 and Twitter datasets. And the results show that the ARGCN model can effectively utilize relation types, reduce the impact of dependency parsing accuracy on its performance, and better establish the connection between aspect words and opinion words. The model accuracy is better than all baseline models.
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Received: 01 April 2022
Published: 31 March 2023
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基于关系门控图卷积网络的方面级情感分析
在方面级情感分析任务中，现有方法难以有效利用句法关系类型且性能依赖依存解析的准确性，为此提出注意力增强的关系门控图卷积神经网络（ARGCN）模型. 该模型将双向长短时记忆（BiLSTM）网络学习得到的句子顺序特征与依存概率矩阵相结合构建单词图；利用关系门控图卷积神经网络（RG-GCN）和注意力增强网络（AAN）分别从单词图和句子的顺序特征中获取方面词的情感特征；拼接RG-GCN和AAN的输出作为方面词最终的情感特征. 在数据集 SemEval 2014 、 Twitter 上进行对比实验和消融实验，结果表明ARGCN模型可以有效地利用关系类型，减小依存解析准确性对模型性能的影响，更好地建立方面词和意见词的联系，模型准确率优于所有基线模型.
关键词：
方面级情感分析,
图卷积网络,
注意力机制,
依存树,
门机制,
自然语言处理
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