Aspect-Based Sentiment Analysis: A Survey of Deep Learning Methods

• Published in: IEEE transactions on computational social systems Vol. 7; no. 6; pp. 1358 - 1375
• Main Authors: Liu, Haoyue, Chatterjee, Ishani, Zhou, MengChu, Lu, Xiaoyu Sean, Abusorrah, Abdullah
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aspect-based sentiment analysis (ABSA); Convolution; Data mining; Deep learning; Digital media; Machine learning; opining mining; Sentiment analysis; Social networking (online); State-of-the-art reviews
• ISSN: 2329-924X; 2373-7476
• DOI: 10.1109/TCSS.2020.3033302

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.