# Aspect-Level Sentiment Analysis Based on Syntax-Aware and Graph Convolutional Networks
###### Abstract  
Aspect-level sentiment analysis is a task of identifying and understanding the sentiment polarity of specific aspects of a sentence. In recent years, significant progress has been made in aspect-level sentiment analysis models based on graph convolutional neural networks. However, existing models still have some shortcomings, such as aspect-level sentiment analysis models based on graph convolutional networks not making full use of the information of specific aspects in a sentence and ignoring the enhancement of the model by external general knowledge of sentiment. In order to solve these problems, this paper proposes a sentiment analysis model based on the Syntax-Aware and Graph Convolutional Network (SAGCN). The model first integrates aspect-specific features into contextual information, and second incorporates external sentiment knowledge to enhance the model's ability to perceive sentiment information. Finally, a multi-head self-attention mechanism and Point-wise Convolutional Transformer (PCT) are applied to capture the semantic information of the sentence. The semantic and syntactic information of the sentences are considered together. Experimental results on three benchmark datasets show that the SAGCN model is able to achieve superior performance compared to the benchmark methods. aspect-level sentiment analysis; graph convolutional networks; external sentiment commonsense knowledge; multi-head self-attention mechanism; point-wise convolutional transformer +
Footnote †: journal: Medical  
In the early days of sentiment analysis research, machine learning approaches were mainly used, where the input features were based on manual design requiring expertise and experience [2], and the classifiers were trained and optimized using traditional machine learning algorithms, such as support vector machines [3] and decision trees. While these methods perform well in dealing with sentiment analysis problems, they usually require a lot of manual feature design and parameter tuning, and their classification effectiveness depends heavily on the quality of the features [4]. With the rise of deep learning, since they are not deliberately designed for feature engineering, they have shown superior performance in many natural language processing tasks, such as in machine translation, semantic recognition, question answering, text summarization, etc. As for the task of sentiment analysis, recurrent neural networks, long- and short-term memory networks and gated recurrent units have become the mainstream research methods [5] and achieved good results [6]. Attentional mechanisms have also been widely used in this task [7]. Applying pre-trained language models to downstream tasks has been a research hotspot in recent years; pre-trained word embeddings have been usually obtained in previous tasks using GloVe [8] and Word2 vec [9], but the obtained word embeddings cannot represent polysemous words, and when the BERT pre-trained language model was proposed, this problem was effectively solved, and then GPT, RobERTa [10], etc., were proposed, and the word representation capability was further enhanced. Depending on the powerful representation capability of these advanced pre-trained language models, the contextual information and semantic associations in the text can be captured more accurately, which leads to a significant performance improvement in various downstream tasks. Recently, aspect-level sentiment analysis using syntactic information and graph neural networks has been heavily researched and achieved good performance. [PERSON] et al. [11] argued that early models lacked a constraint mechanism to incorporate syntactic information and dependencies between long-distance words, which resulted in the models incorrectly considering syntactically irrelevant context words as cues for determining aspectual sentiments, and therefore used sentence dependencies as neighbors to guide GCN for information aggregation and dissemination and obtained good results. [PERSON] et al.