期刊
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
卷 7, 期 6, 页码 1358-1375出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSS.2020.3033302
关键词
Aspect-based sentiment analysis (ABSA); deep learning; machine learning; opining mining; sentiment analysis
资金
- Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia [GCV19-37-1441]
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.
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