## 1 Introduction  
The rapid development of the Internet has changed people's way of life. For example, information is exchanged and shared through online service platforms, which generates a large number of comment information. These comments not only contain users' views and attitudes towards news events, which can help the government and other agencies monitor public opinion, but also contain preferences for products, which can help commercial companies quickly complete product analysis and make improvements. These comment data have great social and commercial value. It is of great significance to use sentiment analysis technology to study these comments. Aspect-level sentiment analysis is a subtask in sentiment analysis. It is a fine-grained sentiment analysis task, aiming at judging the sentiment tendency of different aspects of entities in comments. Recently, the syntax-based model has used the dependency tree to extract syntactic information and apply it to the aspect-level sentiment analysis task, which has achieved remarkable results. Dependency trees capture dependencies between aspect words and their corresponding opinion words, which can solve the problem of long-distance dependence [1]. Therefore, they are often used to extract syntactic information. Due to the arbitrary expression of online comments, there is no obvious syntactic structure, which leads to the introduction of noise (irrelevant dependency relation) in the parsing of dependency-tree-based methods, reducing the ability of a dependency tree to capture the sentiment-aware context [2].  
Based on the above observations, we propose a syntactic and semantic enhanced multi-layer graph attention network (SSEMGAT). The dependency tree is used to represent the dependency between words at the word level; the constituent tree is introduced to obtain syntactic information from a higher-level perspective. The attention mechanism is easily disturbed by other aspect words. It uses aspect-aware attention to redistribute the attention weight between specific aspect words and context. Then, the extracted syntactic and semantic features are fed into the multi-layer graph attention module for specific aspects of sentiment classification.  
The main contributions of this paper are as follows:  
1. For the aspect-level sentiment analysis task, we propose a syntactic and semantic enhanced multi-layer graph attention network to extract features from syntactic and semantic perspectives and use pre-training knowledge to integrate syntactic and semantic features extracted to infer specific aspects of sentiment polarity.
2. We introduce a constituent tree to make up for the defect in the dependency tree and combine different levels of syntactic information to align the position of the aspect word and its corresponding opinion word. At the same time, aspect-aware attention and multi-headed attention are used to construct local attention and global attention, respectively, to link sentiment information between specific aspects and contexts.
3. Experimental results on three benchmark datasets show that the performance of the SSEMGAT model exceeds the baseline model and some recent models. Our model incorporates syntactic and semantic feature information well, which indicates that our work is effective.  
The following sections of this paper are arranged as follows: In Section 2, we introduce the relevant work of aspect-level sentiment analysis, which is mainly divided into three categories: attention-based approach, syntax-based approach, and pre-training-based approach. In Section 3, we describe the proposed model in detail. In Section 4, we test our proposed model on the public benchmark datasets and analyze it separately. Finally, in Section 5, we summarize the whole paper and look forward to future work.