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

Haoyue Liu, Ishani Chatterjee, Mengchu Zhou, Xiaoyu Sean Lu, Abdullah Abusorrah

Research output: Contribution to journalReview articlepeer-review

1 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number9260162
Pages (from-to)1358-1375
Number of pages18
JournalIEEE Transactions on Computational Social Systems
Volume7
Issue number6
DOIs
StatePublished - Dec 2020

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Social Sciences (miscellaneous)
  • Human-Computer Interaction

Keywords

  • Aspect-based sentiment analysis (ABSA)
  • deep learning
  • machine learning
  • opining mining
  • sentiment analysis

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