论文标题
基于方面情感分类的调查
A Survey on Aspect-Based Sentiment Classification
论文作者
论文摘要
随着网络上不断增加的评论和其他带有情感的文本，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）允许自动从文本文档或句子中提取高度细粒度的情感信息。在这项调查中，审查了ABSC研究的迅速发展的状态。提出了一种新颖的分类法，将ABSC模型分为三个主要类别：基于知识，机器学习和混合模型。该分类法伴随着概述报告的模型性能，以及对各种ABSC模型的技术和直观解释。讨论了最新的ABSC模型，例如基于变压器模型的模型，以及结合了知识库的混合深度学习模型。此外，还审查了代表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，并就未来的ABSC领域进行了讨论。
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.
