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.