Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 7-Day Trial for You or Your Team.

Learn More →

Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach

Sentiment analysis on massive open online course evaluations: A text mining and deep learning... 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%. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Computer Applications in Engineering Education Wiley

Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach

Download PDF
18 pages

Loading next page...
 
/lp/wiley/sentiment-analysis-on-massive-open-online-course-evaluations-a-text-0gPJVz3We5

References (78)

Publisher
Wiley
Copyright
© 2021 Wiley Periodicals LLC
ISSN
1061-3773
eISSN
1099-0542
DOI
10.1002/cae.22253
Publisher site
See Article on Publisher Site

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%.

Journal

Computer Applications in Engineering Education – Wiley

Published: May 1, 2021

Keywords: ; ; ;

There are no references for this article.