A Hybrid CNN-LSTM Model for Opinion Mining and Classification of Course Reviews

Hatem Majouri, Olfa Gaddour, Yessine Hadj Kacem

2025

Abstract

Automatic analysis of online course reviews is a critical task that has garnered significant interest, particularly for improving the quality of e-learning platforms. The challenge lies in accurately classifying user feedback in order to generate actionable insights for educators and learners. In this work, we investigate the effectiveness of a hybrid CNN-LSTM model compared to several state-of-the-art deep learning models, including BERT, LSTM, GRU, and CNN, for analyzing reviews collected from the FutureLearn platform. Our experiments demonstrate that the proposed model achieves superior performance in classifying user reviews, with an accuracy of 0.95. These results highlight the potential of advanced deep learning techniques in extracting meaningful insights from user feedback, offering valuable guidance for course developers and learners.

Paper Citation

in Harvard Style

Majouri H., Gaddour O. and Kacem Y. (2025). A Hybrid CNN-LSTM Model for Opinion Mining and Classification of Course Reviews. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 734-741. DOI: 10.5220/0013177900003890

in Bibtex Style

@conference{icaart25,
author={Hatem Majouri and Olfa Gaddour and Yessine Kacem},
title={A Hybrid CNN-LSTM Model for Opinion Mining and Classification of Course Reviews},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={734-741},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013177900003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - A Hybrid CNN-LSTM Model for Opinion Mining and Classification of Course Reviews
SN - 978-989-758-737-5
AU - Majouri H.
AU - Gaddour O.
AU - Kacem Y.
PY - 2025
SP - 734
EP - 741
DO - 10.5220/0013177900003890
PB - SciTePress