Survey on Aspect Category Detection

In recent years, aspect category detection has become popular due to the rapid growth in customer reviews data on e-commerce and other online platforms. Aspect Category Detection, a sub-task of Aspect-based Sentiment Analysis, categorizes the reviews based on the features of a product such as a laptop's display or an aspect of an entity such as the restaurant's ambiance. Various methods have been proposed to deal with such a problem. In this article, we first introduce several datasets in the community that deal with this task and take a closer look at them by providing some exploratory analysis. Then, we review a number of representative methods for aspect category detection and classify them into two main groups: (1) supervised learning and (2) unsupervised learning. Next, we discuss the strengths andweaknesses of different kinds ofmethods, which are expected to benefit both practical applications and future research. Finally, we discuss the challenges, open problems, and future research directions.

Automated summary

In recent years, aspect category detection has gained popularity due to the increase in customer reviews data on e-commerce and online platforms. This task involves categorizing reviews based on product features or entity aspects. Various methods, including supervised and unsupervised learning, have been proposed to tackle this problem. This article provides an overview of datasets, explores the methods used for aspect category detection, analyzes their strengths and weaknesses, and discusses challenges and future research directions.