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

Document Type

Article

Publication Date

12-1-2020

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.

Identifier

85098796281 (Scopus)

Publication Title

IEEE Transactions on Computational Social Systems

External Full Text Location

https://doi.org/10.1109/TCSS.2020.3033302

e-ISSN

2329924X

First Page

1358

Last Page

1375

Issue

6

Volume

7

Grant

GCV19-37-1441

Fund Ref

King Abdulaziz University

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