The DOI matches its Crossref record; 118 of the 121 references it cites were found in a registry or an index.
Sentiment analysis and opinion mining on educational data: A survey
Open accessAbstract
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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“LSA, employing a vector space methodology, enables the extraction of word-word, passage-passage, and passage-passage relationships (Shaik et al. 2022).”
From Large language models (LLMs): survey, technical frameworks, and future challenges (2024) · cites this paper as Shaik et al. 2022
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1 retracted
Sangeetha, K., & Prabha, D. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-01791-9 · Cited by 103
RetractedSMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0082 · Cited by 12,683
VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
He, H., Bai, Y., Garcia, E. A., & Shutao Li, A. (2008) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2008.4633969 · Cited by 5,271
Sentiment Analysis and Opinion Mining
Liu, B. (2012) · Synthesis lectures on human language technologies · doi:10.2200/s00416ed1v01y201204hlt016 · Cited by 3,995
Recognizing contextual polarity in phrase-level sentiment analysis
Wilson, T., Wiebe, J., & Hoffmann, P. (2005) · doi:10.3115/1220575.1220619 · Cited by 3,388
CROWDSOURCING A WORD–EMOTION ASSOCIATION LEXICON
Mohammad, S. M., & Turney, P. D. (2012) · Computational Intelligence · doi:10.1111/j.1467-8640.2012.00460.x · Cited by 2,901
SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining
Baccianella, S., Esuli, A., & Sebastiani, F. (2010) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2jktnrykui9s · Cited by 2,689
Research works printed but not linked: 1
- Lang, 1980, Behavioral treatment and bio-behavioral assessment: Computer applications, Technol. Mental Health Care Deliv. Syst., 119
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Items 201–214 of 214
A Multi-Domain LGAM Model for Aspect–Opinion–Sentiment Triplet ExtractionPreprint
Mohammadi, A., Pajoohana, M., & Bidoki, A. M. Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7196886
Thangavel, M., Kalpana, A., & Ananth, S. (2026) · Serbian Journal of Electrical Engineering · doi:10.2298/sjee2602257t
-, B. P., Deshpande, U. U., & Michael, G. K. O. (2026) · Discover Computing · doi:10.1007/s10791-026-10471-9
Siagian, M. V., Sitanggang, K. G. M., Purwanto, E. S., & Nabiilah, G. Z. (2026) · International Conference on Electronics Representation and Algorithm (ICERA) · doi:10.1109/icera72709.2026.11666760
An AI-Driven Multi-Level Faculty Evaluation Through Dual Sentiment Analysis and Decision Rules
Tanquis, J. Q., Feliscuzo, L. S., & Romana, C. L. C. S. (2026) · International Conference on Cloud Computing and Big Data Analytics (ICCCBDA) · doi:10.1109/icccbda69573.2026.11676562
Research on Sentiment Analysis and Opinion Mining for Educational Texts Based on Machine Learning
Yu, Z., & Wen, Y. (2026) · International Conference on Educational Digitalization and Computer Science in the Guangdong-Hong Kong-Macao Greater Bay Area · doi:10.1145/3828183.3828274
Muthuraj, A., Sudhakaran, P., Jeyaseelan, W. R. S., & Fahamitha, J. (2026) · World Wide Web · doi:10.1007/s11280-026-01431-w
Rizqi, A. S. M., Lidiawaty, B. R., Rochmah, W. Y., Arismawati, P., & Pratiwi, I. D. (2026) · International Electronics Symposium (IES) · doi:10.1109/ies71033.2026.11702554
Shao, T., Wang, X., Li, S., Matsuno, K., & Goto, M. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100685
Loewel, S., Khogres, W., Telaumbanua, J. E., Sudjono, S., Rahardjo, B., & Madyatmadja, E. D. (2026) · Journal of risk and financial management · doi:10.3390/jrfm19100763
Solórzano, R. O., Mejia, C. V. D., Rupaylla, V. C., & Lara, N. F. V. (2026) · Revista Simón Rodríguez · doi:10.62319/simonrodriguez.v.6i13.235
Data-Driven Closed-Loop System for Precise Ideological and Political Education in Higher Education
Zhang, J. (2026) · International Journal of Web-Based Learning and Teaching Technologies · doi:10.4018/ijwltt.426067
Diño, M. J. S., Bahaynon, P. S., Beniopa, J., Villaluz, M. F., Maravillas, S. M., Cruz, L. E., Zhou, J., Lanuza, E., Maroma, P., & Shattell, M. M. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1949516
Mukala, P., Muka, G., & Yamba, L. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02675-1
Metadata
- 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
- Venue
- Natural Language Processing Journal
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 2
- Pages
- 100003
- ISSN
- 2949-7191 (ISSN-L)
- 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
- 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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