## 2 Related Work  
Sentiment analysis (SA) is an important research direction in opinion mining. It is the process of using natural language processing technology (NLP) to analyze and summarize text content containing sentiment. Sentiment analysis is divided into sentence-level [3; 4], chapter-level [5; 6], and aspect-level analysis. The sentence level aims at comment text, which needs to judge its whole sentiment tendency and provide corresponding sentiment values, generally including positive, neutral, and negative. Chapter level refers to a document, which judges the overall sentiment tendency and provides the same sentiment value as the sentence level. Both methods judge the whole and generally only provide sentiment value, which belongs to coarse-grained sentiment. Aspect level aims at the multiple aspects of the entity contained in the review text; each aspect can be composed of different sentiment values, and different aspects can have different sentiment values, even conflict, while the sentence level and chapter level only have one direction of sentiment. Existing studies on aspect-level sentiment analysis can be broadly split into three categories:  
1. Attention-based methods: The attention mechanism models the dependency relationship between an aspect term and its corresponding opinion words. However, there may be several different aspect terms in a sentence. There have been studies to judge the sentiment of a particular aspect. [PERSON] et al. [7] captured the importance of different contextual information to a given aspect word through the attention mechanism, and the attention mechanism and LSTM are combined to model the semantics of sentences and solve the problem of aspect-level sentiment analysis. [PERSON] et al. [8] proposed an interactive attention network (IAN), which uses the attention mechanism to link the target and context for multi-level semantic classification. [PERSON] et al. [9] used multiple attention mechanisms to capture connections between long-distance sentiment features, with strong robustness to irrelevant information. [PERSON] et al. [10] introduced an attention-over-attention (AOA) module to capture the connection between aspects and context words. [PERSON] et al. [11] proposed a multi-grained attention network (MGAN) to combine coarse-grained and fine-grained attention to capture the interaction of aspect and context at the word level. The attention-based approach achieves attractive results. However, due to its defect, the attention mechanism is easily affected by the noise in the sentence, thus misjudging the sentiment polarity. 2. Syntax-based methods: Some work explicitly uses dependency trees of a sentence to extract syntactic information. [PERSON] et al. [12] first proposed building a graph convolutional neural network on a dependency tree to learn the dependencies between nodes. [PERSON] et al. [13] utilized the representation of sentence features learned from the bidirectional LSTM and enhanced embedding with the graph convolutional network. [PERSON] et al. [14] constructed a hierarchical syntactic graph and lexical graph via convolution on GNN embedding and BiLSTM embedding, respectively, and a bi-level interactive network was designed to learn information interaction. [PERSON] et al. [15] combined information from the latent graph and the dependency graph via a gated attention mechanism. For the situation where the current node of the dependency tree pays average attention to adjacent nodes, [PERSON] et al. [16] constructed an aspect-oriented dependency tree structure (R-GAT) by extending the graph attention network to encode graphs with labeled edges. Most syntax-based models only make use of dependency, without considering the type of dependency. [PERSON] et al. [17] proposed T-GCN, which uses an attention mechanism to distinguish different edges in a graph and uses attention layer ensemble to comprehensively learn different layers of T-GCN. The use of syntactic knowledge only cannot obtain the best results, and some researchers have studied the use of other knowledge.