Kipf, T.N. and Welling, M. (2017) Semi-Supervised Classification with Graph Convolutional Networks. The 5th International Conference on Learning Representations, Toulon, 24-26 April 2017, 1-14.

has been cited by the following article:

• TITLE: Aspect-Level Sentiment Analysis of Bi-Graph Convolutional Networks Based on Enhanced Syntactic Structural Information

  AUTHORS: Junpeng Hu, Yegang Li

  KEYWORDS: Aspect-Level Sentiment Analysis, Sentiment Knowledge, Multi-Head Attention Mechanism, Graph Convolutional Networks

  JOURNAL NAME: Journal of Computer and Communications, Vol.13 No.1, January 30, 2025

  ABSTRACT: Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.