TY - JOUR
T1 - Aspect-Based Sentiment Analysis
T2 - A Survey of Deep Learning Methods
AU - Liu, Haoyue
AU - Chatterjee, Ishani
AU - Zhou, Mengchu
AU - Lu, Xiaoyu Sean
AU - Abusorrah, Abdullah
N1 - Funding Information:
Manuscript received June 23, 2020; revised October 9, 2020; accepted October 17, 2020. Date of publication November 16, 2020; date of current version January 13, 2021. This work was supported by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia, under Grant GCV19-37-1441. (Corresponding author: MengChu Zhou.) Haoyue Liu, Ishani Chatterjee, MengChu Zhou, and Xiaoyu Sean Lu are with the Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA (e-mail: hl394@njit.edu; ic53@njit.edu; zhou@njit.edu; xl267@njit.edu).
Publisher Copyright:
© 2014 IEEE.
PY - 2020/12
Y1 - 2020/12
N2 - Sentiment analysis is a process of analyzing, processing, concluding, and inferencing subjective texts with the sentiment. Companies use sentiment analysis for understanding public opinion, performing market research, analyzing brand reputation, recognizing customer experiences, and studying social media influence. According to the different needs for aspect granularity, it can be divided into document, sentence, and aspect-based ones. This article summarizes the recently proposed methods to solve an aspect-based sentiment analysis problem. At present, there are three mainstream methods: lexicon-based, traditional machine learning, and deep learning methods. In this survey article, we provide a comparative review of state-of-the-art deep learning methods. Several commonly used benchmark data sets, evaluation metrics, and the performance of the existing deep learning methods are introduced. Finally, existing problems and some future research directions are presented and discussed.
AB - Sentiment analysis is a process of analyzing, processing, concluding, and inferencing subjective texts with the sentiment. Companies use sentiment analysis for understanding public opinion, performing market research, analyzing brand reputation, recognizing customer experiences, and studying social media influence. According to the different needs for aspect granularity, it can be divided into document, sentence, and aspect-based ones. This article summarizes the recently proposed methods to solve an aspect-based sentiment analysis problem. At present, there are three mainstream methods: lexicon-based, traditional machine learning, and deep learning methods. In this survey article, we provide a comparative review of state-of-the-art deep learning methods. Several commonly used benchmark data sets, evaluation metrics, and the performance of the existing deep learning methods are introduced. Finally, existing problems and some future research directions are presented and discussed.
KW - Aspect-based sentiment analysis (ABSA)
KW - deep learning
KW - machine learning
KW - opining mining
KW - sentiment analysis
UR - http://www.scopus.com/inward/record.url?scp=85098796281&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098796281&partnerID=8YFLogxK
U2 - 10.1109/TCSS.2020.3033302
DO - 10.1109/TCSS.2020.3033302
M3 - Review article
AN - SCOPUS:85098796281
SN - 2329-924X
VL - 7
SP - 1358
EP - 1375
JO - IEEE Transactions on Computational Social Systems
JF - IEEE Transactions on Computational Social Systems
IS - 6
M1 - 9260162
ER -