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.

Aspect-oriented Fine-grained Opinion Extraction （AFOE） extracts aspect terms and opinion terms from reviews in the form of opinion pairs or additionally extracts sentiment polarities of aspect terms on the basis of the above to form opinion triplets. Aiming at the problem of neglecting correlation between the opinion pairs and contexts， an aspect-oriented Adaptive Span Feature-Grid Tagging Scheme （ASF-GTS） model was proposed. Firstly， BERT （Bidirectional Encode Representation from Transformers） model was used to obtain the feature representation of the sentence. Then， the correlation between the opinion pair and local context was enhanced by the Adaptive Span Feature （ASF） method. Next， Opinion Pair Extraction （OPE） was transformed into a uniform grid tagging task by Grid Tagging Scheme （GTS）. Finally， the corresponding opinion pairs or opinion triplet were generated by the specific decoding strategy. Experiments were carried out on four AFOE benchmark datasets adaptive to the task of opinion tuple extraction. The results show that compared with GTS-BERT （Grid Tagging Scheme-BERT） model， the proposed model has the F1-score improved by 2.42% to 7.30% and 2.62% to 6.61% on opinion pair or opinion triplet tasks， respectively. The proposed model can effectively reserve the sentiment correlation between opinion pair and context， and extract opinion pairs and their sentiment polarities more accurately.