Deep learning for aspect-based sentiment analysis: A comparative review

Hai Ha Do, P. W.C. Prasad, Angelika Maag, Abeer Alsadoon

Research output: Contribution to journalReview articlepeer-review

317 Citations (Scopus)


The increasing volume of user-generated content on the web has made sentiment analysis an important tool for the extraction of information about the human emotional state. A current research focus for sentiment analysis is the improvement of granularity at aspect level, representing two distinct aims: aspect extraction and sentiment classification of product reviews and sentiment classification of target-dependent tweets. Deep learning approaches have emerged as a prospect for achieving these aims with their ability to capture both syntactic and semantic features of text without requirements for high-level feature engineering, as is the case in earlier methods. In this article, we aim to provide a comparative review of deep learning for aspect-based sentiment analysis to place different approaches in context.

Original languageEnglish
Pages (from-to)272-299
Number of pages28
JournalExpert Systems with Applications
Early online date06 Oct 2018
Publication statusPublished - 15 Mar 2019


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