Deep learning for aspect-based sentiment analysis: a review

User-generated content on various Internet platforms is growing explosively, and contains valuable information that helps decision-making. However, extracting this information accurately is still a challenge since there are massive amounts of data. Thereinto, sentiment analysis solves this problem by identifying people's sentiments towards the opinion target. This article aims to provide an overview of deep learning for aspect-based sentiment analysis. Firstly, we give a brief introduction to the aspect-based sentiment analysis (ABSA) task. Then, we present the overall framework of the ABSA task from two different perspectives: significant subtasks and the task modeling process. Finally, challenges are proposed and summarized in the field of sentiment analysis, especially in the domain of aspect-based sentiment analysis. In addition, ABSA task also takes the relations between various objects into consideration, which is rarely discussed in the previous work.

Automated summary

This article provides an overview of deep learning for aspect-based sentiment analysis. It introduces the ABSA task and presents the overall framework from the perspectives of significant subtasks and the task modeling process. The challenges in sentiment analysis, particularly in the field of aspect-based sentiment analysis, are discussed. Additionally, the ABSA task's consideration of relations between various objects is highlighted.