Abstract:

In the aspect-based sentiment analysis task,the syntactic dependency parsing of the text is required first,which is highly dependent on the quality of the dependency parsing and does not take into account the lack of correlation between dependency parsing and semantic knowledge.Therefore,a two-channel graph convolution model based on syntactic perception and knowledge enhancement is proposed for the aspect-based sentiment analysis task.Syntax-perception mechanisms are used to learn sentence dependencies in one channel,and knowledge enhancement is performed in the other channel through a knowledge graph,with the outputs of the two channels correlated through an information interaction mechanism,which allows the model to pay more syntactic and semantic attention to important words associated with aspectual words.In addition,a positional attention mechanism is introduced to adjust the score weights of words with respect to the position,which in turn improves the performance of the aspect-based sentiment analysis task.Experiments are conducted on three public datasets,Rest14,Lap14 and Twitter.Compared to other aspect-based sentiment analysis models,this paper’s model shows a more significant improvement in both accuracy and F1 value.Experiments show that syntactic perception and knowledge enhancement can guide the graph convolutional model to perform deeper semantic learning and reasonable weight allocation,thus improving the performance of aspect-based sentiment analysis tasks.

Key words: sentiment analysis, graph convolutional network, dependency relations, knowledge graph, attention mechanisms