Sentiment Analysis with Abstract Meaning Representation and Dependency Grammar
[Objective]This paper aims to combine the deep semantic representation and surface syntactic structure of natural language sentences.[Methods]We proposed an integration strategy based on semantic and syntactic rule concatenation and utilized it for the aspect-based sentiment analysis.This strategy used the answer set programming language(ASP)to represent abstract meaning representation(AMR),dependency grammar(DEP),and part of speech(POS)as ASP facts.It also integrated the DEP and POS through rule body extension based on AMR rules.Therefore,a sentence's two or more language features were concatenated into the rule body.Based on this strategy,we developed the AMR-DEP-POS-C and AMR-DEP-C models.[Results]We examined the new methods on eight publicly available review datasets.The AMR-DEP-POS-C achieved a complementary relationship between semantics and syntax and performed better than the baseline methods based on semantic,syntactic,and deep learning.[Limitations]Our new models rely on the accuracy of the existing AMR and DEP parsers.[Conclusions]AMR-DEP-POS-C can effectively integrate different language features and bring new research perspectives and tools for aspect-based sentiment analysis.
Abstract Meaning RepresentationDependency GrammarRuleAspect-Based Sentiment Analysis
万方数据
维普
