Dual-Channel Interactive Graph Convolutional Networks for Aspect-Level Sentiment Analysis

Zhouxin Lan, Qing He () and Liu Yang ()
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Zhouxin Lan: College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
Qing He: College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China
Liu Yang: School of Public Administration, Guizhou University, Guiyang 550025, China

Mathematics, 2022, vol. 10, issue 18, 1-14

Abstract: Aspect-level sentiment analysis aims to identify the sentiment polarity of one or more aspect terms in a sentence. At present, many researchers have applied dependency trees and graph neural networks (GNNs) to aspect-level sentiment analysis and achieved promising results. However, when a sentence contains multiple aspects, most methods model each aspect independently, ignoring the issue of sentiment connection between aspects. To address this problem, this paper proposes a dual-channel interactive graph convolutional network (DC-GCN) model for aspect-level sentiment analysis. The model considers both syntactic structure information and multi-aspect sentiment dependencies in sentences and employs graph convolutional networks (GCN) to learn its node information representation. Particularly, to better capture the representations of aspect and opinion words, we exploit the attention mechanism to interactively learn the syntactic information features and multi-aspect sentiment dependency features produced by the GCN. In addition, we construct the word embedding layer by the BERT pre-training model to better learn the contextual semantic information of sentences. The experimental results on the restaurant, laptop, and twitter datasets show that, compared with the state-of-the-art model, the accuracy is up to 1.86%, 2.50, 1.36%, and 0.38 and the Macro-F 1 values are up to 1.93%, 0.61%, and 0.4%, respectively.

Keywords: aspect-level sentiment analysis; graph neural network; attention mechanism; BERT (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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