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Cited by 382

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

ONAN, Aytuğ·2021·Computer Applications in Engineering Education
DOI 10.1002/cae.22253
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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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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
DOI
10.1002/cae.22253
OpenAlex
W3022935508
Cited by
382
References
66
FWCI
22.16
Type
journal article
Language
en
Open access
closed

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