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.

本文对 ABSC 领域的三种主要模型进行了分类：基于知识、机器学习和混合模型，并对基于 transformer 模型和深度学习模型的最新技术进行了讨论，摘要了不同模型的性能表现，其技术和直观解释，还总结了各种模型输入技术和输出评估技术，并提出了该领域的未来研究趋势。