Open Access

ARTICLE

Learning-Related Sentiment Detection, Classification, and Application for a Quality Education Using Artificial Intelligence Techniques

Samah Alhazmi^1,*, Shahnawaz Khan², Mohammad Haider Syed¹

1 College of Computing and Informatics, Saudi Electronic University, Riyadh, 11673, Saudi Arabia
2 Country Bahrain Polytechnic, Isa Town, 33349, Bahrain

* Corresponding Author: Samah Alhazmi. Email:

Intelligent Automation & Soft Computing 2023, 36(3), 3487-3499. https://doi.org/10.32604/iasc.2023.036297

Received 24 September 2022; Accepted 23 November 2022; Issue published 15 March 2023

Abstract

Quality education is one of the primary objectives of any nation-building strategy and is one of the seventeen Sustainable Development Goals (SDGs) by the United Nations. To provide quality education, delivering top-quality content is not enough. However, understanding the learners’ emotions during the learning process is equally important. However, most of this research work uses general data accessed from Twitter or other publicly available databases. These databases are generally not an ideal representation of the actual learning process and the learners’ sentiments about the learning process. This research has collected real data from the learners, mainly undergraduate university students of different regions and cultures. By analyzing the emotions of the students, appropriate steps can be suggested to improve the quality of education they receive. In order to understand the learning emotions, the XLNet technique is used. It investigated the transfer learning method to adopt an efficient model for learners’ sentiment detection and classification based on real data. An experiment on the collected data shows that the proposed approach outperforms aspect enhanced sentiment analysis and topic sentiment analysis in the online learning community.

---

Keywords

---