The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach
Abstract
Massive open online courses (MOOCs) are recent innovative approaches in distance education, which provide learning content to participants without age‐, gender‐, race‐, or geography‐related barriers. The purpose of our research is to present an efficient sentiment classification scheme with high predictive performance in MOOC reviews, by pursuing the paradigms of ensemble learning and deep learning. In this contribution, we seek to answer several research questions on sentiment analysis on educational data. First, the predictive performance of conventional supervised learning methods, ensemble learning methods and deep learning methods has been evaluated. Besides, the efficiency of text representation schemes and word‐embedding schemes has been evaluated for sentiment analysis on MOOC evaluations. For the evaluation task, we have analyzed a corpus containing 66,000 MOOC reviews, with the use of machine learning, ensemble learning, and deep learning methods. The empirical analysis indicate that deep learning‐based architectures outperform ensemble learning methods and supervised learning methods for the task of sentiment analysis on educational data mining. For all the compared configurations, the highest predictive performance has been achieved by long short‐term memory networks in conjunction with GloVe word‐embedding scheme‐based representation, with a classification accuracy of 95.80%.
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Cited by
Systematic reviews in sentiment analysis: a tertiary study
Ligthart, A., Catal, C., & Tekinerdogan, B. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09973-3 · Cited by 303
Liu, S., Liu, S., Liu, Z., Peng, X., & Yang, Z. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104461 · Cited by 269
Sentiment analysis and opinion mining on educational data: A survey
Shaik, T., Tao, X., Dann, C., Xie, H., Li, Y., & Galligan, L. (2023) · Natural Language Processing Journal · doi:10.1016/j.nlp.2022.100003 · Cited by 215
A Review on Explainability in Multimodal Deep Neural Nets
Joshi, G., Walambe, R., & Kotecha, K. (2021) · IEEE Access · doi:10.1109/access.2021.3070212 · Cited by 204
Qu, Z., Liu, X., & Zheng, M. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3203791 · Cited by 140
Metadata
- Authors
- ONAN, A.
- Venue
- Computer Applications in Engineering Education
- Publisher
- Wiley
- Published
- 2020-05-04
- Volume
- 29
- Issue
- 3
- Pages
- 572-589
- ISSN
- 1061-3773 (ISSN-L)
- 1099-0542
- OpenAlex
- W3022935508
- Cited by
- 382
- References
- 66
- FWCI
- 22.16
- Type
- journal article
- Language
- en
- Open access
- closed
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