Abstract:

Target sentiment analysis aims to analyze the sentiment tendency corresponding to different targets in the review text.At present,graph neural network based methods use the dependency syntactic tree to incorporate dependency syntactic relations.On the one hand,these methods mostly ignore the fact that dependency relations lack distinction.On the other hand,without considering the dependency relations provided by the dependency syntactic tree,there is a lack of relations between target and sentiment words.Therefore,a dual graph attention network(DGAT) model is proposed.First,the model uses a bidirectional long short-term memory network to obtain word node representation with semantic information,and then constructs a syntactic graph attention network based on the word node representation according to the dependency syntactic tree,so as to distinguish the importance of dependency syntactic relations,more effectively establish the relation between target and sentiment words,and obtain a more accurate representation of target sentiment features.At the same time,according to the undirected complete graph of sentences,a global graph attention network is used to mine lacking relations between target and sentiment words,so as to further improve the performance of the model.Experimental results show that compared with existing models,the DGAT model has a better accuracy and macro-average F1 value on different datasets.

Key words: natural language processing, target sentiment analysis, dependency relations, graph attention network, attention mechanism