Reconstructing graph networks by using new target representation for aspect-based sentiment analysis

The purpose of aspect-based sentiment analysis (ABSA) is to identify the sentiment polarity of a given aspect of a sentence. Recent investigations have revealed that incorporating syntactic structures derived from dependency-parsing trees into graph convolutional networks (GCNs) can yield excellent performance. However, these GCN-based methods excessively rely on the quality of the dependency-parsing tree, resulting possibly in suboptimal dependencies between words. Moreover, these GCN-based models fail to adapt properly to informal and complex comments without syntactic dependencies. To alleviate these deficiencies, we proposed a target-based GCN with semantic and syntactic information (TSGCN). In a TSGCN, a new target generation (NTG) module with a dependency attention mechanism is designed to generate a new target representation using explicit semantic information to replace a given aspect. Then, the syntactic structure is reconstructed based on the new target representation to capture the shortest distance between the given aspect and viewpoint words. Finally, the semantic structure generated by the self-attention mechanism was injected into the syntactic structure to complement the semantic dependencies between words. The experimental findings on five benchmark datasets indicated that the TSGCN outperformed the other baseline models.