## 2 Related Work  
Traditional sentiment analysis tasks are mainly for document-level and sentence-level classification tasks. In contrast, the aspect-level sentiment analysis task is an entity-orientedsentiment analysis task and is also a fine-grained sentiment analysis task. The goal is to extract, for several aspects of an entity, the sentiment polarity of each aspect. Commonly used methods include a sentiment dictionary, machine learning, and deep learning. We compare the advantages and disadvantages of these methods in Table 1. The specific methods are detailed as follows:  
Using a sentiment dictionary method to solve an aspect-level sentiment analysis task is closer to the solution idea of the traditional sentiment analysis method, and it is necessary to use the marked sentiment dictionary to determine the sentiment polarity. Common sentiment dictionaries are: SentiWordNet [21], NTUSD [22], and How Net [23]. [PERSON] et al. [24] further optimized the dictionary-based approach, achieving better performance through the kernel recognition aspect of the tree connected to the opinion. [PERSON] et al. [25] proposed a dictionary-based method that extracts the corresponding sentiment polarity for both explicit and implicit aspects. Compared with sentiment dictionary methods, machine learning methods are used more in the field of aspect-level sentiment analysis, while traditional machine algorithms mainly include: Naive Bayes (NB), Maximum Entropy (ME), Logistic Regression (LR), and the Support Vector Machine (SVM), etc. [PERSON] et al. [26] used a movie review text as a corpus to analyze the text and found that the support vector machine (SVM) and Bayesian algorithm (Bayes) have better classification results. [PERSON] et al. [27] used Bayesian and SVM to analyze movie reviews with a semantic-based SO-PMI-IR algorithm. [PERSON] et al. [28] used a machine learning method to perform a sentiment analysis on the review text data in the catering field using the Bayesian, C4,5 method. Deep learning methods are widely used in the field of aspect-level sentiment analysis, and its main research can be roughly divided into three categories: simple neural networks, attention-based neural networks, and graph convolutional networks. Simple neural networks mainly include: the Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) network, etc. [PERSON] [29] proposed the TextCNN neural network model, and applied the CNN network model to the field of text classification for the first time, dividing sentences into word-level processing, so that the text features could be extracted by the model itself, but the sentence context information could not be learned. [PERSON] et al. [30] used a recurrent neural network (RNN) to train sentence vectors of fixed dimensions for sentences of different lengths, adding sequence features of words and enriching the representation of sentence vectors, but this model exhibits the phenomenon of exploding gradient and vanishing gradient. [PERSON] [31] and others proposed using an LSTM network for sentiment classification, which can effectively solve the defect of RNN's loss of sentence information due to distance, but cannot receive parallel input of text. The emergence of attention-based neural networks has promoted further progress in sentiment analysis tasks. The main idea is to capture and establish the connection between aspects and opinion words through the attention mechanism. Strictly speaking, attention-based neural network models can also be used as a way to use sentence