Contextual Graph Attention Network for Aspect-Level Sentiment Classification

Yuqing Miao, Ronghai Luo, Lin Zhu, Tonglai Liu, Wanzhen Zhang, Guoyong Cai and Ming Zhou
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Yuqing Miao: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
Ronghai Luo: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
Lin Zhu: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
Tonglai Liu: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Wanzhen Zhang: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Guoyong Cai: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
Ming Zhou: Guilin Hivision Technology Company, Guilin 541004, China

Mathematics, 2022, vol. 10, issue 14, 1-12

Abstract: Aspect-level sentiment classification aims to predict the sentiment polarities towards the target aspects given in sentences. To address the issues of insufficient semantic information extraction and high computational complexity of attention mechanisms in existing aspect-level sentiment classification models based on deep learning, a contextual graph attention network (CGAT) is proposed. The proposed model adopts two graph attention networks to aggregate syntactic structure information into target aspects and employs a contextual attention network to extract semantic information in sentence-aspect sequences, aiming to generate aspect-sensitive text features. In addition, a syntactic attention mechanism based on syntactic relative distance is proposed, and the Gaussian function is cleverly introduced as a syntactic weight function, which can reduce computational complexities and effectively highlight the words related to aspects in syntax. Experiments on three public sentiment datasets show that the proposed model can make better use of semantic information and syntactic structure information to improve the accuracy of sentiment classification.

Keywords: aspect-based sentiment analysis; syntactic relative distance; attention mechanism; graph attention network; BERT; deep learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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