Abstract: Aspect sentiment analysis triplet extraction(ASTE) is an emerging task in fine-grained sentiment analysis.Traditional span-level sentiment triple extraction methods have recently achieved remarkable results on the ASTE task,but these methods have not fully exploited the potential of syntactic and semantic dependencies.This study proposes a multi-view edge-enhanced encoder that makes full use of the syntactic and semantic relationships between words to accurately distinguish aspect words and viewpoint words and obtain rich deep information.Specifically,a dual-channel encoder with RoBERTa is first used to obtain basic semantic information,and at the same time,a bi-directional long short-term memory network channel and the edge-enhanced graph neural network are utilized to comprehensively capture semantic and syntactic information.Considering the insufficient sensitivity of traditional span-level models to span boundaries,it introduces a multi-layer graph convolutional network to capture the cross-relationships between spans to effectively identify span boundaries.In addition,this study also uses the mutual difference elimination strategy to eliminate conflicting triples.Through extensive experiments on multiple public benchmark data sets,this method outperforms other baseline models and verifies its effectiveness in the emotional triple extraction task.

Key words: Aspect sentiment analysis, Triplet extraction, Multi-view graph neural network, Edge enhancement, Natural language processing