Survey on Aspect Category Detection

被引：13
作者：
Chebolu, Siva Uday Sampreeth [1 ]
Rosso, Paolo [2 ]
Kar, Sudipta [3 ]
Solorio, Thamar [1 ]
机构：
[1] Univ Houston, 4800 Calhoun Rd, Houston, TX 77004 USA
[2] Univ Politecn Valencia, DSIC, Edificio 1F,Campus Vera, E-46022 Valencia, Spain
[3] Amazon Alexa AI, Houston, TX USA
来源：
ACM COMPUTING SURVEYS | 2023年 / 55卷 / 07期
关键词：
SemEval; SentiHood; deep learning; neural networks; aspect category detection survey; aspect category detection; aspect-based sentiment analysis; survey;
D O I：
10.1145/3544557
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
引用
收藏
页数：37
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