文本方面级情感分类方法综述

A survey of text aspect-based sentiment classification

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。

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.