A Bayesian CNN-LSTM Model for Sentiment Analysis in Massive Open Online Courses MOOCs

Authors

  • Khaoula Mrhar IPSS, FSR, Mohammed V University in Rabat, Morocco
  • Lamia Benhiba ENSIAS, Mohammed V University in Rabat, Morocco
  • Samir Bourekkache Smart computer science laboratory (LINFI), Computer science department, University of Biskra, Algeria
  • Mounia Abik ENSIAS, Mohammed V University in Rabat, Morocco

DOI:

https://doi.org/10.3991/ijet.v16i23.24457

Keywords:

MOOCs, Sentiment Analysis, Deep Learning

Abstract


Massive Open Online Courses (MOOCs) are increasingly used by learn-ers to acquire knowledge and develop new skills. MOOCs provide a trove of data that can be leveraged to better assist learners, including behavioral data from built-in collaborative tools such as discussion boards and course wikis. Data tracing social interactions among learners are especially inter-esting as their analyses help improve MOOCs’ effectiveness. We particular-ly perform sentiment analysis on such data to predict learners at risk of dropping out, measure the success of the MOOC, and personalize the MOOC according to a learner’s behavior and detected emotions. In this pa-per, we propose a novel approach to sentiment analysis that combines the advantages of the deep learning architectures CNN and LSTM. To avoid highly uncertain predictions, we utilize a Bayesian neural network (BNN) model to quantify uncertainty within the sentiment analysis task. Our em-pirical results indicate that: 1) The Bayesian CNN-LSTM model provides interesting performance compared to other models (CNN-LSTM, CNN, LSTM) in terms of accuracy, precision, recall, and F1-Score; and 2) there is a high correlation between the sentiment in forum posts and the dropout rate in MOOCs.

Downloads

Published

2021-12-08

How to Cite

Mrhar, K., Benhiba, L., Bourekkache, S., & Abik, M. (2021). A Bayesian CNN-LSTM Model for Sentiment Analysis in Massive Open Online Courses MOOCs. International Journal of Emerging Technologies in Learning (iJET), 16(23), pp. 216–232. https://doi.org/10.3991/ijet.v16i23.24457

Issue

Section

Papers