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.

Qun Gu

Zhidong Wang

Hai Zhang

Siyi Sui

Rui Wang

Applied Sciences

Applied Sciences (ISSN 2076-3417) provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.

The journal "Applied Sciences (Switzerland)" (ISSN 2076-3417) provides an advanced forum for all aspects of applied natural sciences. It publishes reviews, research papers, and communications, aiming to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There are no restrictions on the length of the papers, and full experimental details must be provided to ensure reproducibility. If the complete details of calculations or experimental procedures cannot be published in the usual manner, electronic files and software can be deposited as supplementary electronic material.

《Applied Sciences (Switzerland)》期刊（ISSN 2076-3417）为应用自然科学的各个方面提供了一个先进的论坛。期刊发表综述、研究论文和通讯，旨在鼓励科学家尽可能详细地发布他们的实验和理论结果。对论文的长度没有限制，必须提供完整的实验细节，以便他人能够重复实验。如果无法以正常方式发布的计算或实验程序的完整细节，电子文件和软件可以作为补充电子材料存放。

Applied Sciences (Switzerland)

Aspect-Level Sentiment Analysis Based on Syntax-Aware and Graph Convolutional Networks

A sentiment analysis model based on the Syntax-Aware and Graph Convolutional Network (SAGCN) that first integrates aspect-specific features into contextual information, and second incorporates external sentiment knowledge to enhance the model’s ability to perceive sentiment information.

Category	Quartile
综合性期刊	4
综合性期刊, 化学综合	3
综合性期刊, 工程综合	4
综合性期刊, 材料科学综合	4
综合性期刊, 物理应用	4

Aspect-Level Sentiment Analysis Based on Syntax-Aware and Graph Convolutional Networks

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Category	Quartile
PHYSICS, APPLIED	2