SES-Net: A Novel Multi-Task Deep Neural Network Model for Analyzing E-learning Users’ Satisfaction via Sentiment, Emotion, and Semantic

Abstract

Understanding users’ satisfaction is fundamental for enhancing the effectiveness and usability of e-learning platforms. The existing approaches for analyzing users’ satisfaction leverage word embedding vectors to represent sentiment information, but they often fail to fully address the complex relationship between emotional and semantic information. Additionally, several emotional and semantic word embedding models are proposed, but they require sentiment information. In this study, we propose a novel multi-task deep neural model, called Sentiment-Emotion-Semantic Network (SES-Net), capable of learning sentiment, emotion, and semantic information simultaneously. The proposed model comprises three main sub-neural tasks: Bidirectional Long Short-Term Memory (BiLSTM) to capture sentiment, BiLSTM to extract semantics, and Convolutional Neural Networks (CNN) to learn emotional features. Experimental results reveal that, SES-Net outperforms the previous approaches by achieving an average F1-score of 90.59%.

Keywords:

• e-learning
• users’ satisfaction
• sentiment
• semantic
• emotion
• deep neural network

Acknowledgments

This work was supported by the Engineering Research Center of Integration and Application of Digital Learning Technology of the Ministry of Education of China under Grant 1331001, and National Natural Science Foundation of China under Grant 62272048.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 https://www.nltk.org/.

2 https://scikit-learn.org/stable/.

3 https://fasilkom.mercubuana.ac.id/moodle-emotions/.

Additional information

Notes on contributors

Sulis Sandiwarno

Sulis Sandiwarno is currently pursuing the PhD degree with the School of Computer Science and Technology, Beijing Institute of Technology, China. His research interests include analysis of information system, e-learning system techniques, data mining, opinion mining, and computer programing.

Zhendong Niu

Zhendong Niu is currently a Professor at the School of Computer Science and Technology, Beijing Institute of Technology, China. His research areas include digital libraries, e-learning techniques, information retrieval, and recommender systems.

Ally S. Nyamawe

Ally S. Nyamawe received a PhD degree in Computer Science and Technology from the Beijing Institute of Technology, China, in 2020. He is currently a Senior Lecturer with the Department of Computer Science and Engineering, The University of Dodoma, Tanzania. His research interests include Software Maintenance, Machine Learning, and Requirements Engineering.