A Hybrid Recommendation Integrating Semantic Learner Modelling and Sentiment Multi-Classification


  • Rawaa Alatrash Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar – 752054, Odisha, India
  • Rojalina Priyadarshini Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar – 752054, Odisha, India
  • Hadi Ezaldeen Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar – 752054, Odisha, India
  • Akram Alhinnawi Department Computer Science and Engineering, University of Bridgeport, Bridgeport, CT, USA




Hybrid Recommendation, Semantic User Modeling, Contextual Graph, Sentiment Analysis, Word Embeddings, Deep Learning


Enhancing virtual learning platforms need to adapt new intelligent mechanisms so that long-term learner experience can be improved. Sentiment Analysis gives us perception on how a specific scientific material is suitable to be recommended to the learner. It depends on the feedback of a similar learner taking many factors under consideration such as preference, knowledge level, and learning pattern. In this work, a hybrid e-learning recommendation system is proposed based on individualization and Sentiment Analysis. A new approach is provided for modelling the semantic user model based on the generated semantic matrix to capture the learner’s preferences based on their selections of interest. The extracted semantic matrix is used for text representation by utilizing ConceptNet knowledge base which relies on contextual graph and expanded terms to represent the correlation among terms and materials. On the extracted terms from semantic user model, Word Embeddings-Based-Sentiment Analysis (WEBSA) must recommend the learning materials with highest rating to the learners properly. Variant models of (WEBSA) are proposed relying on Natural Language Processing (NLP) to generate effective vocabulary representations along with the use of qualitative customized Convolutional Neural Network (CNN) for sentiment multi-classification tasks. To validate the language model, two datasets are used, a tailored dataset that has been created by scraping reviews of different e-learning resources, and a public dataset. From the experimental results, it has been found that the lowest error rate is achieved with our customized dataset, where the model named CNN-Specific-Task-CBOWBSA outperforms than others with 89.26% accuracy.


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Author Biographies

Rawaa Alatrash, Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar – 752054, Odisha, India

Rawaa Alatrash is a Ph.D. scholar in Computer Science and Engineering at the C. V. Raman Global University (CGU), India. She received her Master’s degree in Computer Science and Engineering from CGU, India. She has more than four years of industrial experience and worked on different Java based projects. Her main research interests include Deep Learning, Knowledge-based Systems, and Natural Language Processing, Sentiment Analysis, Recommendation Systems. She has publications in reputed Journals and International Conferences indexed in SCI and Scopus. She is a web developer, has done several remarkable projects in Web Intelligence and Sentiment Analysis. Reviewed papers in journals like River Publishers and Science Publishing Group.

Rojalina Priyadarshini , Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar – 752054, Odisha, India

Rojalina Priyadarshini: Completed her Ph.D in 2020 and currently is working as an Assistant Professor in the C.V.Raman Global University, Odisha. She has grabbed a Gold medal in her master degree. She has more than a decade of teaching and research experience. Apart from that she is an accredited AWS cloud educator and a certified Java faculty. Dr. R. Priyadarshini has published more than 50 research papers in peer-reviewed International Journals and conferences. Her areas of specialization are cloud and fog computing, Machine Learning and Cyber security.

Hadi Ezaldeen, Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar – 752054, Odisha, India

Hadi Ezaldeen is a Ph.D. in Computer Science and Engineering from C.V. Raman Global University, India. He received his Master’s degree in Web Science (MWS) from the Syrian Virtual University (SVU), Damascus, Syria. He has worked as a tutor (Assistant Professor), and an assistant supervisor of several graduation projects in the Faculty of Information Technology Engineering (ITE), at SVU. His research areas include Machine Learning, Semantic Analysis, Recommendation Systems, Natural Language Processing, and Text Mining. He has publications in reputed Journals and International Conferences indexed in SCI and Scopus. He has achieved interesting projects in the field of Web Science.

Akram Alhinnawi, Department Computer Science and Engineering, University of Bridgeport, Bridgeport, CT, USA

Akram Ahinnawi has graduated from master in Computer Science, School of Engineering at the University of Bridgeport, CT, USA at May 2013. He worked as a Web Developer, QA System Test Engineer, Senior Software Development Test Engineer, Senior Automation Test Engineer and now he has been working as a Senior Security Test Engineer at Fujifilm Medical System Inc. USA at Morrisville Office, NC, USA for 8 years.


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