Abstract:Fine-grained sentiment analysis is widely used in fields such as opinion mining and text filtering to achieve more accurate text understanding and result determination. The Aspect Sentiment Triplet Extraction (ASTE) task is a representative fine-grained sentiment analysis task, and most of the related research is based on either the pipeline model or end-to-end model. However, the pipeline model suffers from error propagation as a two-stage model, and the end-to-end model does not make full use of the connections between the constituents in a sentence and lacks the ability to capture high-level semantic relations. To address the these issues, this paper features complementary syntactic and semantic knowledge and proposes a sentiment triplet extraction method based on semantic enhancement and guided routing mechanisms (ASTE-SEGRM). Firstly, the syntactic features and lexical features of the source text are learned based on Key-Value Pair Neural Network (KVMN). Secondly, inspired by iterative routing mechanism, a guided routing mechanism is introduced to build a neural network that uses a priori knowledge to guide the extraction of sentiment triplets. Finally, experimental results on four benchmark datasets demonstrate that the proposed approach outperforms several baseline models.