结合句法增强与图注意力网络的方面级情感分类

doi:10.11896/jsjkx.230200189

Abstract

Abstract: Aspect-level sentiment classification aims to identify the emotional polarity of a given aspect text.In this field,the combination of graph neural network and syntactic dependency parsing is one of the current hot research directions.Based on the relationship between them,the graph structure is constructed and input into the graph neural network to obtain the emotional polarity.If the syntax parser makes a parsing error,the impact on the graph-based graph neural network model will be huge.In order to enhance the parsing results of the syntactic dependency tree generated by the parser,a syntactically enhanced graph attention network is proposed.By fusing the parsing results of multiple parsers,the parsing accuracy of syntactic dependencies is improved,and a more accurate dependency syntactic graph is obtained.A densely connected mechanism is used in graph attention networks to capture richer features,which are more suitable for enhanced syntactic graphs,and the aspect attention mechanism is introduced to capture aspect semantic features.Experimental results verify the effectiveness of the syntactic enhancement method.The classification accuracy on the three benchmark datasets has been improved,and it has a better performance in the field of aspect-level sentiment analysis.

Key words: Aspect-level sentiment analysis, Dependency parsing, Syntax enhancement, Graph attention network, Dense connection

CLC Number:

TP391

ZHANG Zebao, YU Hannan, WANG Yong, PAN Haiwei. Combining Syntactic Enhancement with Graph Attention Networks for Aspect-based Sentiment Classification[J].Computer Science, 2024, 51(5): 200-207.

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Combining Syntactic Enhancement with Graph Attention Networks for Aspect-based Sentiment Classification

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