For fine-grained sentiment analysis in Natural Language Processing （NLP）， in order to explore the influence of Pre-trained Language Models （PLMs） with structural biases on the end-to-end sentiment triple extraction task， and solve the problem of low fault tolerance rate of aspect semantic feature dependence that is common in previous studies， combining aspect-aware attention mechanism and Graph Convolutional Network （GCN）， an Aspect-aware attention Enhanced GCN （AE-GCN） model was proposed for aspect sentiment triple extraction tasks. Firstly， multiple types of relations were introduced for the aspect sentiment triple extraction task. Then， these relations were embedded into the adjacent tensors between words in the sentence by using the double affine attention mechanism. At the same time， the aspect-aware attention mechanism was introduced to obtain the sentence attention scoring matrix， and the aspect-related semantic features were further mined. Next， a sentence was converted into a multi-channel graph through the graph convolutional neural network， to learn a relation-aware node representation by treating words and relation adjacent tensors as edges and nodes， respectively. Finally， an effective word pair representation refinement strategy was used to determine whether word pairs matched， which was used to consider the implicit results of aspect and opinion extraction. Experimental results show that， on ASTE-D1 benchmark dataset， the F1 values of the proposed model on the 14res， 14lap， 15res and 16res sub-datasets are improved by 0.20， 0.21， 1.25 and 0.26 percentage points compared with the Enhanced Multi-Channel Graph Convolutional Network （EMC-GCN） model； on ASTE-D2 benchmark dataset， the F1 values of the proposed model on the 14lap， 15res and 16res sub-datasets are increased by 0.42， 0.31 and 2.01 percentage points compared with the EMC-GCN model. It can be seen that the proposed model has great improvement in precision and effectiveness compared with the EMC-GCN model.

The existing text sentiment classification methods face serious challenges due to the complex semantics of natural language， the multiple sentiment polarities of words， and the long-term dependency of text. To solve these problems， a semantically enhanced sentiment classification model based on multi-level attention was proposed. Firstly， the contextualized dynamic word embedding technology was used to mine the multiple semantic information of words， and the context semantics was modeled. Secondly， the long-term dependency within the text was captured by the multi-layer parallel multi-head self-attention in the internal attention layer to obtain comprehensive text feature information. Thirdly， in the external attention layer， the summary information in the review metadata was integrated into the review features through a multi-level attention mechanism to enhance the sentiment information and semantic expression ability of the review features. Finally， the global average pooling layer and Softmax function were used to realize sentiment classification. Experimental results on four Amazon review datasets show that， compared with the best-performing TE-GRU （Transformer Encoder with Gated Recurrent Unit） in the baseline models， the proposed model improves the sentiment classification accuracy on App， Kindle， Electronic and CD datasets by at least 0.36， 0.34， 0.58 and 0.66 percentage points， which verifies that the proposed model can further improve the sentiment classification performance.