# A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis
## 2 Related Work  
Aspect-level sentiment analysis is a subtask of sentiment analysis. Compared with ordinary sentiment analysis tasks, it is a more refined emotional classification task aimed at discovering the emotional polarity of a sentence in a specific aspect. Early sentiment analysis tasks mostly adopted machine learning methods [14]. [PERSON] et al. [15] obtained a large number of analytical attributions for classification by manually constructing features. However, this method is labor-intensive, demands a lot of human resources and time, and loses the dependency between the aspect words and the context. [PERSON] et al. [16] used neural networks to model text, concluding that convolutional neural networks (CNNs) perform well when extracting the local features of sentences in a text. Therefore, CNNs have begun to be widely used in sentiment analysis [17; 18; 19]. The variant model of the recurrent neural network (RNN) is also a common method used in sentiment analysis [20]. For instance, in the case of using aspect word information, [PERSON] et al. [21] introduced target-dependent long short-term memory (TD-LSTM). This  
Figure 2: Examples of SSWs in aspect-level sentiment analysis sentences. approach employs two LSTMs to model the context before and after aspect words, utilizing the aspect words' semantic information to judge the emotional polarity. This method achieved good results in their experiments. In aspect-level sentiment analysis, an attention-mechanism-based approach [22; 23] directs the model's increased attention toward the feature information of aspect words. For instance, [PERSON] et al. [7] input the word vector of aspect words into the structure of an attention mechanism. The obtained attention score represents the weight of each word in this aspect of the sentence. The higher the score, the more important it becomes. Finally, the sentence features are obtained by weighted summation. Their experiments showed that this method is superior to its baseline methods. Similarly, [PERSON] et al. [24] used BiLSTM-SNP to obtain the correlation between aspect words and content words, applying attention mechanisms to determine their weights. The experiments on multiple Chinese and English datasets showed that this method has significant advantages in terms of accuracy. To a certain extent, the attention mechanism can find the semantic correlation between aspect words and other words in a sentence, but when the sentence composition is complex, the attention mechanism may make the model concentrate on the wrong part of the sentence [25]. Unlike traditional deep learning models, graph convolutional networks capture the relationships and feature information between nodes in a graph by defining the convolution operations on the graph structure [26]. Specifically, they first represent nodes in the graph as low-dimensional vectors and aggregate the information of adjacent nodes through matrix multiplication to obtain new feature representations for each node in the graph. Since GCNs address this deficiency better [27], it was used for the first time by [PERSON] et al. [10] for aspect-level sentiment analysis. Their proposed method was named the aspect-specific graph convolutional network (ASGCN). The model established the GCN on the syntactic dependency tree and acquired grammar-dependent information from a sentence. The dual graph convolutional network (DualGCN) [28] was proposed by [PERSON] et al. They also argued for the complementarity of grammatical structure and semantic relations, and combined an attention mechanism with a grammatical dependency tree. In this model, a GCN is employed to obtain the abundant syntactic information on the syntactic dependency tree, and another GCN is applied to obtain the semantic relations using a self-attention mechanism. [PERSON] et al. [29] introduced tight connections into GCNs, enabling GCNs to obtain rich local and global information. A syntactic- and semantic-enhanced graph convolutional network (SSEGCN) was presented by [PERSON] et al. [11].