文本方面级情感分类方法综述

随着深度学习的发展，方面级情感分类已经在单领域和单一语言中取得了大量的研究成果，但是在多领域的研究还有提升的空间。通过对近年来文本方面级情感分类方法进行归纳总结，介绍了情感分类的具体应用场景，整理了方面级情感分类常用的数据集，并对方面级情感分类的发展进行了总结与展望，提出未来可在以下领域开展深入研究：1）探索基于图神经网络的方法，弥补深度学习方法存在的局限性；2）学习融合多模态数据，丰富单一文本的情感信息；3）开展更多针对多语言文本和低资源语言的研究。

A survey of text aspect-based sentiment classification

With the development of deep learning, aspect-based sentiment classification has achieved a lot of results in a single field and a single language, but there is room for improvement in multi-fields. By summarizing up the methods of text aspect-based sentiment classification in recent years, the specific application scenarios of sentiment classification were introduced, and the commonly used data sets of aspect-based sentiment classification were categorized. The development of aspect-based sentiment classification were summarized and prospected, and further research can be carried out in the following areas: exploring methods based on graph neural networks to make up for the limitations of deep learning methods; learning to fuse multi-modal data to enrich the emotional information of a single text; developing more targeted research work on multilingual texts and low-resource languages.