Aspect Sentiment Analysis Based on Local-Global Context Guidance
Sentiment analysis is one of the important directions in the field of natural language processing.Existing researches on the influ-ence of context of exploration still has insufficient challenges such as difficulty in syntax information capture,loss of semantic information,and lack of semantic context.Aiming at these problems,propose a novel combination of local global context guidance network to improve the performance and expression ability in the aspect-based sentiment analysis.Specifically,in this method,a dependency syntax parsing tree is constructed firstly to introduce more diversified information features for the model;Then,by introducing the context focusing mechanism,the characteristics of the original text and dependency syntax parsing tree are refined.At the same time,the local feature vector of the refining is interacted with the global feature vector so as to retain the context feature information of the words effectively.Finally,the characteristic aggre-gation module is aggregated to the local global characteristics,which improves the accuracy of the model in emotional polarity prediction.The experimental results on the four benchmark datasets show that compared with the baseline models,the accuracy of the proposed model increas-es by 1.67%,1.67%,0.7%and 0.16%respectively,and the F1 value increases by 2.55%,2.03%,1.57%and 2.08%respectively.
sentiment analysisnatural language processinglocal contextdependency syntax parsing treeinformation features
国家科技期刊平台
NSTL
万方数据
维普
