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

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dc.contributor.authorSandiwarno, S.
dc.contributor.authorNiu, Z.
dc.contributor.authorNyamawe, A. S.
dc.date.accessioned2024-08-31T02:15:54Z
dc.date.available2024-08-31T02:15:54Z
dc.date.issued2024
dc.descriptionAbstract. Full-text available at https://doi.org/10.1080/10447318.2024.2356356
dc.description.abstractUnderstanding 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%.
dc.identifier.citationSandiwarno, S., Niu, Z., & Nyamawe, A. S. (2024). SES-Net: A novel multi-task deep neural network model for analyzing E-learning users’ satisfaction via sentiment, emotion, and semantic. International Journal of Human–Computer Interaction, 1-24.
dc.identifier.doi10.1080/10447318.2024.2356356
dc.identifier.otherDOI: https://doi.org/10.1080/10447318.2024.2356356
dc.identifier.urihttps://repository.udom.ac.tz/handle/20.500.12661/4920
dc.language.isoen
dc.publisherInforma UK Limited
dc.relation.ispartofInternational Journal of Human–Computer Interaction
dc.subjecte-learning
dc.subjectusers’ satisfaction
dc.subjectsentiment
dc.subjectsemantic
dc.subjectemotion
dc.subjectdeep neural network
dc.titleSES-Net: A Novel Multi-Task Deep Neural Network Model for Analyzing E-learning Users’ Satisfaction via Sentiment, Emotion, and Semantic
dc.typeArticle
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