Abstract: Aspect-based sentiment analysis aims to align aspects with their corresponding opinion expressions to identify the sentiment polarity of specific aspects.Existing dependency tree-based graph neural network models have achieved significant performance improvements in aspect-based sentiment analysis.However,most studies fail to fully exploit the complete information of the syntactic dependency tree,often overlooking syntactic dependency distance or dependency label information.This limitation may prevent effective alignment between opinion words and their corresponding aspect terms,particularly in sentences containing multiple aspects.To address these issues,a knowledge-assisted and reinforced syntax-driven network model is constructed.Specifically,an opinion word perception module is designed by incorporating external knowledge information to enhance the model’s ability to recognize opinion expressions in sentences.Then,reinforcement learning is employed to guide the construction of the syntactic distance graph.This graph is then heuristically integrated with the dynamic syntactic label graph,which is built based on word relations and dependency labels,thereby improving the accuracy and comprehensiveness of capturing relevant opinion expressions for a given aspect.Additionally,an aspect-focused attention mechanism is employed to better handle sentences with ambiguous syntactic structures.Extensive experiments conducted on three public datasets validate the effectiveness of the proposed model.

Key words: Aspect-based sentiment analysis, Sentiment lexicon, Syntax dependency tree, Reinforcement learning, Graph convolution networks, Attention mechanism, Deep learning