The aspect-level sentiment analysis aims to determine the sentiment polarity of a given aspect of a sentence.The existing graph neural network-based aspect-level sentiment analysis has two shortcomings:first,it ignores different types of syntactic dependencies and word co-occurrence information in the corpus;second,it cannot accurately control the flow of sentiment information to a given aspect.To address these problems,this study proposes an aspect-level sentiment analysis model that combines dual graph convolution and a Gated Linear Unit(GLU).The model first uses the global vocabulary map to encode word co-occurrence information in the corpus,and thereafter uses the classification summary structure to distinguish the frequency of co-occurrence of various words and different types of syntactic dependencies on the vocabulary and syntax maps.Double-layer convolution is thereafter performed on the two graphs,and the BiAffine transform module is used as a bridge to effectively exchange relevant features between the two Graph Convolution Network(GCN)modules,thus effectively integrating syntactic and lexical information.Finally,the GLU is used to control the flow of sentiment information to a given aspect such that the model can focus more on analyzing the sentiment information related to this aspect and avoid irrelevant sentiment information from affecting the sentiment analysis results of a given aspect,thus improving the accuracy of the analysis.The experimental results demonstrate that on the four datasets of Twitter,Laptop14,Restaurant15,and Restaurant16,the accuracy of the model reached 74.82％,77.61％,82.29％,and 89.81％,respectively,and the F1 value reached 72.97％,73.52％,67.72％,and 73.37％,respectively.The aspect-level sentiment classification performance is significantly better than those of the other baseline models.
aspect-level sentiment analysisword co-occurrence informationdouble graph convolutioninformation interactionGated Linear Unit(GLU)
维普
万方数据
