Syntactically Enhanced Dependency-POS Weighted Graph Convolutional Network for Aspect-Based Sentiment Analysis

Jinjie Yang, Anan Dai, Yun Xue, Biqing Zeng and Xuejie Liu ()
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Jinjie Yang: School of Electronics and Information Engineering, South China Normal University, Foshan 528225, China
Anan Dai: School of Electronics and Information Engineering, South China Normal University, Foshan 528225, China
Yun Xue: School of Electronics and Information Engineering, South China Normal University, Foshan 528225, China
Biqing Zeng: School of Software, South China Normal University, Foshan 528225, China
Xuejie Liu: School of Electronics and Information Engineering, South China Normal University, Foshan 528225, China

Mathematics, 2022, vol. 10, issue 18, 1-13

Abstract: Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis that presents great benefits to real-word applications. Recently, the methods utilizing graph neural networks over dependency trees are popular, but most of them merely considered if there exist dependencies between words, ignoring the types of these dependencies, which carry important information, as dependencies with different types have different effects. In addition, they neglected the correlations between dependency types and part-of-speech (POS) labels, which are helpful for utilizing dependency imformation. To address such limitations and the deficiency of insufficient syntactic and semantic feature mining, we propose a novel model containing three modules, which aims to leverage dependency trees more reasonably by distinguishing different dependencies and extracting beneficial syntactic and semantic features to further enhance model performance. To enrich word embeddings, we design a syntactic feature encoder (SynFE). In particular, we design Dependency-POS Weighted Graph Convolutional Network (DPGCN) to weight different dependencies by a graph attention mechanism we proposed. Additionally, to capture aspect-oriented semantic information, we design a semantic feature extractor (SemFE). Extensive experiments on five popular benchmark databases validate that our model can better employ dependency information and effectively extract favorable syntactic and semantic features to achieve new state-of-the-art performance.

Keywords: aspect-based sentiment analysis; graph neural networks; dependency trees; dependency types; graph attention mechanism; syntactic; semantic (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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