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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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A Sentiment-Based Approach to Predict Learners’ Perceptions Towards YouTube Educational Videos
Faizi, R. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-96299-9_52 · Cited by 6
SETSum: Summarization and Visualization of Student Evaluations of Teaching
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Sorted by citations
Large language models (LLMs): survey, technical frameworks, and future challenges
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Sentiment analysis methods, applications, and challenges: A systematic literature review
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A Large Language Model Approach to Educational Survey Feedback Analysis
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DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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