Aspect-Level Sentiment Analysis Based on Enhanced Syntactic Information and Multi-feature Graph Convolutional Fusion

Aspect-level sentiment analysis, as an important task in the field of affective computing, aims to identify the emotional tendencies about specific aspects in texts. In order to improve the performance in this task, a network model (ESMFGCN) based on enhanced syntactic information and multi-feature graph convolutional fusion is proposed, using dependency trees to represent the grammatical structural relationships between words in sentences. Since the simple use of dependencies tree method will cause irrelevant noise problems when modeling, the phrase structure tree is introduced and the phrase tree is converted into a hierarchical phrase matrix. The adjacency matrix and hierarchical phrase matrix constructed by the dependency tree are merged as the initial matrix of the graph convolution network, used to enhance syntactic information. In order to more precisely capture the association between aspect words and the entire sentence, an attention mechanism is introduced to establish a more refined association between the aspect word context and the entire sentence, and extract semantic information through a graph convolutional network. Finally, a fusion layer is designed to fuse semantic information and syntactic information to improve the accuracy and robustness of aspect-level sentiment analysis. This paper designs comparative experiments, ablation experiments, and sensitivity analysis experiments on the Restaurant, Laptop, and Twitter datasets respectively. Experimental results show that compared with other research methods, the method in this paper has achieved significant performance improvement, proving the effectiveness and superiority of the model.