Focused on the issue that the current Aspect-Based Sentiment Analysis （ABSA） models rely too much on the syntactic dependency tree with relatively sparse relationships to learn feature representations， which leads to the insufficient ability of the model to learn local information， an ABSA model fused with multi-window local information called MWGAT （combining Multi-Window local information and Graph ATtention network） was proposed. Firstly， the local contextual features were learned through the multi-window local feature learning mechanism， and the potential local information contained in the text was mined. Secondly， Graph ATtention network （GAT）， which can better understand the syntactic dependency tree， was used to learn the syntactic structure information represented by the syntactic dependency tree， and syntax-aware contextual features were generated. Finally， these two types of features representing different semantic information were fused to form the feature representation containing both the syntactic information of syntactic dependency tree and the local information， so that the sentiment polarities of aspect words were discriminated by the classifier efficiently. Three public datasets， Restaurant， Laptop， and Twitter were used for experiment. The results show that compared with the T-GCN （Type-aware Graph Convolutional Network） model combined with the syntactic dependency tree， the proposed model has the Macro-F1 score improved by 2.48%， 2.37% and 0.32% respectively. It can be seen that the proposed model can mine potential local information effectively and predict the sentiment polarities of aspect words more accurately.
