2022
Paolo Rosso Siva Uday Sampreeth Chebolu, Sudipta Kar
Survey on Aspect Category Detection Conference
ACM Computing Surveys (CSUR), ACM, 2022.
Abstract | Links | BibTeX | Tags: explanatory analysis
@conference{CSUR,
title = {Survey on Aspect Category Detection},
author = {Siva Uday Sampreeth Chebolu, Paolo Rosso, Sudipta Kar, Thamar Solorio},
url = {https://dl.acm.org/doi/abs/10.1145/3544557},
year = {2022},
date = {2022-01-01},
booktitle = {ACM Computing Surveys (CSUR)},
publisher = {ACM},
abstract = {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 paper, 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 and weaknesses of different kinds of methods, which are expected to benefit both practical applications and …},
keywords = {explanatory analysis},
pubstate = {published},
tppubtype = {conference}
}
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 paper, 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 and weaknesses of different kinds of methods, which are expected to benefit both practical applications and …