With the constantly growing number of reviews and other sentiment-bearing texts on the Web, the demand for automatic sentiment analysis algorithms continues to expand. Aspect-based sentiment classification (ABSC) allows for the automatic extraction of highly fine-grained sentiment information from text documents or sentences. In this survey, the rapidly evolving state of the research on ABSC is reviewed. A novel taxonomy is proposed that categorizes the ABSC models into three major categories: knowledge-based, machine learning, and hybrid models. This taxonomy is accompanied with summarizing overviews of the reported model performances, and both technical and intuitive explanations of the various ABSC models. State-of-the-art ABSC models are discussed, such as models based on the transformer model, and hybrid deep learning models that incorporate knowledge bases. Additionally, various techniques for representing the model inputs and evaluating the model outputs are reviewed. Furthermore, trends in the research on ABSC are identified and a discussion is provided on the ways in which the field of ABSC can be advanced in the future.

随着网络上评论和其他情感文本的数量不断增加，对自动情感分析算法的需求不断扩大。基于方面的情感分类 (ABSC) 允许从文本文档或句子中自动提取高度细粒度的情感信息。在本次调查中，回顾了 ABSC 研究的快速发展状态。提出了一种新的分类法，将 ABSC 模型分为三大类：基于知识的模型、机器学习模型和混合模型。该分类法附有报告模型性能的概述，以及各种 ABSC 模型的技术和直观解释。讨论了最先进的 ABSC 模型，例如基于变压器模型的模型，以及包含知识库的混合深度学习模型。此外，还回顾了表示模型​​输入和评估模型输出的各种技术。此外，确定了 ABSC 研究的趋势，并讨论了 ABSC 领域未来的发展方式。