[Objective]This paper aims to address the problem in current aspect-based sentiment analysis research,where the use of sentiment knowledge to enhance syntactic dependency graphs overlooks syntactic reachability and positional relationships between words and does not adequately extract semantic information.[Methods]We proposed an aspect-based sentiment analysis model based on a position-weighted reachability matrix and multi-space semantic information extraction.First,we used a reachability matrix to incorporate syntactic reachability relationships between words into the syntactic dependency graph,and we employed position-weighting to adjust the matrix to enhance contextual feature extraction.Then,we integrated the sentiment features with the enhanced dependency graph to extract aspect word features.Third,we use the multi-head self-attention mechanism combined with a graph convolutional network(GCN)to learn contextual semantic information from multiple feature spaces.Finally,we fused feature vectors containing positional information,syntactic information,affective knowledge,and semantic information for sentiment polarity classification.[Results]Compared to the best-performing models,the proposed model improved accuracy on the Lap 14,Rest 14,and Rest 15 datasets by 1.00％,1.25％,and 0.76％.When using BERT,the PRM-GCN-BERT model's accuracy on the Lap14,Rest14,Rest15,and Rest16 datasets increased by 0.50％,0.22％,1.98％,and 0.31％.[Limitations]The proposed model was not applied to Chinese or other language datasets.[Conclusions]The proposed model enhances feature aggregation in graph convolutional networks,improves contextual feature extraction,and boosts semantic learning effectiveness,thereby significantly improving the accuracy of aspect-based sentiment analysis.
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