# A Graph Convolutional Network Based on Sentiment Support for Aspect-Level Sentiment Analysis
## 1 Introduction  
Sentiment analysis involves extracting viewpoints and judging emotional tendencies in subjective texts. Aspect-level sentiment analysis is more refined, particularly when judging the emotional polarity of texts in terms of given aspect words (e.g., positive, negative, and neutral) [1, 2, 3]. As shown in Figure 1, the sentence has two sides: \"price\" and \"screen\". The emotional polarity of the sentence is negative with regard to \"price\" and positive in terms of \"screen\". The main task of aspect-level sentiment analysis is to accurately find the opinions and emotional tendencies of sentences according to the identified aspects. In this way, the statement is partially understood. This is currently an important tool for online opinion gathering for governments and businesses concerning policies or business strategies [4]. For example, it can be used to analyze a company's blockchain statements and obtain a short-term value evaluation of the company [5]. It can also be employed to analyze the behavior of users in the blockchain [6]. Attention mechanisms can improve the structure of deep learning by concentrating on the important points. [PERSON] et al. [7] presented the application of attention mechanisms to long short-term memory (LSTM) [8]. They used an attention mechanism to acquire the semantic information between words in a text and aspect words, obtaining crucial parts ofthe sentences to obtain a more accurate aspect sentiment analysis. [PERSON] et al. [9] introduced interactive attention networks (IANs) based on LSTM and attention mechanisms to realize the interactive learning of attention based on goals and contexts. However, many studies have used attention mechanisms to obtain the relation between words and aspect words in sentences from a semantic perspective [10; 11; 12]. When a sentence has several aspect words and the emotional polarity of these words is different, the attention mechanism can easily focus on the wrong part or a part that is unrelated to the aspect words. As shown in Figure 1, if the attention mechanism mistakenly concentrates on the opinion word \"good\" for the aspect of \"price\", it is likely to cause errors in the emotional polarity. In order to enable the model to accurately concentrate on opinion words that correspond to the aspect words, it is important to introduce the grammatical relations between words in sentiment analysis. Graph convolutional networks (GCNs) [12] combined with syntactic dependency trees can better obtain grammatical features. [PERSON] et al. [10] employed graph convolutional networks in aspect-level sentiment analysis, and their results showed excellent model performance. In the following study, [PERSON] et al. [11] joined the grammatical structure and semantic correlation of aspect words. They presented an aspect perception attention mechanism combined with a self-attention mechanism, and their work shows promising results. It is evident that a sentence not only contains aspect words but also other types of words. If sentiment analysis was only performed with the combination of grammatical and semantic features of aspect words, some important information in the sentence would be lost. Therefore, we introduced sentiment support words (SSWs; proposed in our previous study [13]) to supplement a sentiment analysis in order to obtain more complete grammatical and semantic features of sentences. SSWs are words that influence an aspect word in a sentence, often supporting the emotional polarity of the whole sentence in one aspect. Through a grammatical dependency tree, we can obtain the aspect of sentiment support words. Figure 2 shows a concrete example of this. From the figure, we can see that the aspect words are attributes of the object entities in the sentence. For example, an aspect can be the price of computers or computer screens, while sentiment support words directly evaluate part of the target information. For example, when the aspect is the price of computers, sentiment support words evaluate the price of computers as \"expensive\".