Cited by 215Sentiment Analysis and Opinion MiningOnline Learning and AnalyticsEducational Technology and Pedagogy

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Sentiment analysis and opinion mining on educational data: A survey

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Shaik, Thanveer, Tao, Xiaohui, Dann, Christopher, Xie, Haoran, Li, Yan, Galligan, Linda·2023·Natural Language Processing Journal

DOI 10.1016/j.nlp.2022.100003

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Abstract

Sentiment analysis AKA opinion mining is one of the most widely used NLP applications to identify human intentions from their reviews. In the education sector, opinion mining is used to listen to student opinions and enhance their learning–teaching practices pedagogically. With advancements in sentiment annotation techniques and AI methodologies, student comments can be labelled with their sentiment orientation without much human intervention. In this review article, (1) we consider the role of emotional analysis in education from four levels: document level, sentence level, entity level, and aspect level, (2) sentiment annotation techniques including lexicon-based and corpus-based approaches for unsupervised annotations are explored, (3) the role of AI in sentiment analysis with methodologies like machine learning, deep learning, and transformers are discussed, (4) the impact of sentiment analysis on educational procedures to enhance pedagogy, decision-making, and evaluation are presented. Educational institutions have been widely invested to build sentiment analysis tools and process their student feedback to draw their opinions and insights. Applications built on sentiment analysis of student feedback are reviewed in this study. Challenges in sentiment analysis like multi-polarity, polysemous, negation words, and opinion spam detection are explored and their trends in the research space are discussed. The future directions of sentiment analysis in education are discussed.

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Authors: Shaik, T., Tao, X., Dann, C., Xie, H., Li, Y., & Galligan, L.
Affiliations: University of Southern Queensland (AU)
Lingnan University (HK)
Corresponding author: Shaik, Thanveer
ORCID: Shaik, Thanveer
Tao, Xiaohui
Dann, Christopher
Xie, Haoran
Li, Yan
Galligan, Linda
Venue: Natural Language Processing Journal
Publisher: Elsevier BV
Published: 2023-03
Volume: 2
Pages: 100003
ISSN: 2949-7191 (ISSN-L)
DOI: 10.1016/j.nlp.2022.100003
OpenAlex: W4312001989
Cited by: 215
References: 121
Citations by year: 2026: 48 · 2025: 77 · 2024: 58 · 2023: 32
FWCI: 13.69
Type: journal article
Language: en
Update notices: erratum (2026-09-01)
Open access: gold
doi.org
Licence: cc-by-nc-nd
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Natural Language Processing Journal (publishedVersion, cc-by-nc-nd)
arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Social Sciences › Education › Educational Technology and Pedagogy
Keywords: sentiment analysis, educational data, student feedback, sentiment annotation, aspect-based sentiment analysis, sentiment polarity, negation handling, opinion spam detection, educational decision-making

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Sentiment analysis and opinion mining on educational data: A survey

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