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

Authors

  • 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

DOI:

https://doi.org/10.13052/jwe1540-9589.2141

Keywords:

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

Abstract

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.

Downloads

Download data is not yet available.

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.

References

K. Mangaroska, M. Giannakos, ‘learning analytics for learning design: A systematic literature review of analytics-driven design to enhance learning’, IEEE Transactions on Learning Technologies, 12(4), 516–534, 2018.

V. Kubik, R. Gaschler, H. Hausman, PLAT 20 (1) 2021: Enhancing Student Learning in Research and Educational Practice, The Power of Retrieval Practice and Feedback, 2021.

S. Benzarti, R. Faiz, EgoTR: Personalized tweets recommendation approach. In Computer Science On-line Conference, pp. 227–238, 2015. Springer, Cham. https://doi.org/10.1007/978-3-319-18503-3_23

L. Boratto, S. Carta, G. Fenu, R. Saia, Semantics-aware content-based recommender systems: Design and architecture guidelines. Neurocomputing, 254, 79–85, 2017. https://doi.org/10.1016/j.neucom.2016.10.079.

Y. Kim, K. Shim, TWILITE: A recommendation system for Twitter using a probabilistic model based on latent Dirichlet allocation. Information Systems, 42, 59–77, 2014. Doi: 10.1016/j.is.2013.11.003.

W. Cui, Y. Du, Z. Shen, Y. Zhou, J. Li, Personalized microblog recommendation using sentimental features. In 2017 IEEE International Conference on Big Data and Smart Computing (BigComp) pp. 455–456, 2017. IEEE. Doi: 10.1109/BIGCOMP.2017.7881756.

X. Zhou, S. Wu, C. Chen, G. Chen, S. Ying, Real-time recommendation for microblogs. Information Sciences, 279, 301–325, 2014. Doi: 10.1016/j.ins.2014.03.121.

H. Ezaldeen, R. Misra, R. Alatrash, R. Priyadarshini, Semantically enhanced machine learning approach to recommend e-learning content. International Journal of Electronic Business, 15(4), 389–413, 2020. https://doi.org/10.1504/IJEB.2020.111095.

F. Abel, Q. Gao, G J. Houben, K. Tao, Semantic enrichment of twitter posts for user profile construction on the social web. In Extended semantic web conference pp. 375–389, 2011, May. Springer, Berlin, Heidelberg. Doi: 10.1007/978-3-642-21064-8_26.

V. de Graaff, A. van de Venis, M. van Keulen, A. Rolf, Generic knowledge-based Analysis of Social Media for Recommendations. In CBRecSys@ RecSys, pp. 22–29, 2015, September.

G. Piao, J G. Breslin, Exploring dynamics and semantics of user interests for user modeling on Twitter for link recommendations. In proceedings of the 12th international conference on semantic systems pp. 81–88, 2016, September. Doi: 10.1145/2993318.2993332.

M. Keshavarz, Y H. Lee, Ontology matching by using ConceptNet. In Proceedings of the Asia Pacific Industrial Engineering & Management Systems Conference Vol. 2012, pp. 1917–1925, 2012.

R. Speer, J. Chin, C. Havasi, Conceptnet 5.5: An open multilingual graph of general knowledge. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 31, No. 1, 2017, February.

Z. Han, J. Wu, C. Huang, Q. Huang, M. Zhao,:A review on sentiment discovery and analysis of educational big-data. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(1), e1328, 2020.

K. Mite-Baidal, C. Delgado-Vera, E. Solís-Avilés, A H. Espinoza, J. Ortiz-Zambrano, E. Varela-Tapia, Sentiment analysis in education domain: A systematic literature review. In International Conference on Technologies and Innovation pp. 285–297, 2018, November. Springer, Cham. https://doi.org/10.1007/978-3-030-00940-3_21.

A. ONAN, Sentiment analysis on massive open online course evaluations: A text mining and deep learning approach. Computer Applications in Engineering Education, 2020. https://doi.org/10.1002/cae.22253.

R. Cobos, F. Jurado, A. Blázquez-Herranz, A Content Analysis System that supports Sentiment Analysis for Subjectivity and Polarity detection in Online Courses. IEEE Revista Iberoamericana de Tecnologías Del Aprendizaje, 14(4), 177–187, 2019. Doi: 10.1109/RITA.2019.2952298.

A. Magdy, L. Abdelhafeez, Y. Kang, E. Ong, M F. Mokbel, Microblogs data management: a survey. The VLDB Journal, 29(1), 177–216, 2020.

M L B. Estrada, R Z. R O. Cabada, Bustillos, M. Graff, Opinion mining and emotion recognition applied to learning environments. Expert Systems with Applications, 150, 113265, 2020. https://doi.org/10.1016/j.eswa.2020.113265.

N. Kiuru, B. Spinath, A L. Clem, K. Eklund, T. Ahonen, R. Hirvonen, The dynamics of motivation, emotion, and task performance in simulated achievement situations. Learning and Individual Differences, 80, 101873, 2020. https://doi.org/10.1016/j.lindif.2020.101873.

C. Salazar, J. Aguilar, J. Monsalve-Pulido, E. Montoya, Affective recommender systems in the educational field. A systematic literature review. Computer Science Review, 40, 100377, 2021.

Y. Kim, Convolutional Neural Networks for Sentence Classification. 2014r08r25, 2014. https://arxiv.org/abs/1408.5882.

R. Johnson, T. Zhang, Semi-supervised convolutional neural networks for text categorization via region embedding. Advances in neural information processing systems, 28, 919, 2015.

A. Conneau, H. Schwenk, L. Barrault, Y. Lecun, Very deep convolutional networks for natural language processing, 2016. arXiv preprint arXiv:1606.01781, 2, 1.

R. Alatrash, H. Ezaldeen, R. Misra, R. Priyadarshini, Sentiment Analysis Using Deep Learning for Recommendation in E-Learning Domain. Progress in Advanced Computing and Intelligent Engineering: Proceedings of ICACIE 2020, 123, 2020. https://doi.org/10.1007/978-981-33-4299-6_10.

Y. Shen, X. He, J. Gao, L. Deng, G. Mesnil, Learning semantic representations using convolutional neural networks for web search. In Proceedings of the 23rd international conference on world wide web, pp. 373–374, 2014, April. https://doi.org/10.1145/2567948.2577348.

N. Kalchbrenner, E. Grefenstette, P. Blunsom, A convolutional neural network for modelling sentences, 2014. arXiv preprint arXiv:1404.2188. http://arxiv.org/abs/1404.2188.

W T. Yih, X. He, C. Meek, Semantic parsing for single-relation question answering. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) pp. 643–648, 2014, June.

R. Collobert, J.Weston, L. Bottou, M. Karlen, K. Kavukcuoglu, P. Kuksa, Natural language processing (almost) from scratch. Journal of machine learning research, 12(ARTICLE), 2493–2537, 2011.

N. Shrestha, F. Nasoz, Deep learning sentiment analysis of amazon. com reviews and ratings, 2019. arXiv preprint arXiv:1904.04096.

K S. Srujan, S S. Nikhil, H R. Rao, K. Karthik, B S. Harish, H K. Kumar, Classification of amazon book reviews based on sentiment analysis. In Information Systems Design and Intelligent Applications pp. 401–411, 2018. Springer, Singapore. https://doi.org/10.1007/978-981-10-7512-4_40.

X. Zhang, J. Zhao, Y. LeCun, Character-level convolutional networks for text classification, 2015. arXiv preprint arXiv:1509.01626.

A M. Qamar, M. Alassaf, Improving Sentiment Analysis of Arabic Tweets by One-Way ANOVA. Journal of King Saud University-Computer and Information Sciences, 2020. https://doi.org/10.1016/j.jksuci.2020.10.023.

R. Kumar, H S. Pannu, A K. Malhi, Aspect-based sentiment analysis using deep networks and stochastic optimization. Neural Computing and Applications, 32(8), pp. 3221–3235, 2020. https://doi.org/10.1007/s00521-019-04105-z.

T. Mikolov, K. Chen, G. Corrado, J. Dean, Efficient estimation of word representations in vector space, 2013. arXiv preprint arXiv:1301.3781.

T. Mikolov, I. Sutskever, K. Chen, G. Corrado, J. Dean, Distributed representations of words and phrases and their compositionality, 2013. arXiv preprint arXiv:1310.4546.

E.C. Too, L. Yujian, P.K. Gadosey, S. Njuki, F. Essaf, Performance analysis of nonlinear activation function in convolution neural network for image classification. International Journal of Computational Science and Engineering, 21(4), pp. 522–535, 2020.

G E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, R R. Salakhutdinov, Improving neural networks by preventing co-adaptation of feature detectors, 2012. arXiv preprint arXiv:1207.0580.

J. Moravec, A Comparative Study: L1-Norm Vs. L2-Norm; Point-to-Point Vs. Point-to-Line Metric; Evolutionary Computation Vs. Gradient Search. Applied Artificial Intelligence, 29(2), 164–210, 2015.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929–1958, 2014.

R. Alatrash, H. Ezaldeen, rawaa123/Dataset GitHub Retrieved from https://github.com/rawaa123/Dataset/, 2021.

P. Bojanowski, E. Grave, A. Joulin, T. Mikolov, Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5, 135–146, 2017. https://doi.org/10.1162/tacl_a_00051.

R. Socher, A. Perelygin, J. Wu, J. Chuang, C D. Manning, A Y. Ng, C. Potts, Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 conference on empirical methods in natural language processing, pp. 1631–1642, 2013, October.

D P. Kingma, J. Ba, Adam: A method for stochastic optimization, 2014. arXiv preprint arXiv:1412.6980.

H. Ezaldeen, R. Misra, R. Alatrash, R. Priyadarshini, Machine Learning Based Improved Recommendation Model for E-learning. In 2019 International Conference on Intelligent Computing and Remote Sensing (ICICRS) (pp. 1–6). IEEE. https://doi.org/10.1109/ICICRS46726.2019.9555866.

X. Ouyang, P. Zhou, C.H. Li, L. Liu, Sentiment analysis using convolutional neural network. In: 2015 IEEE international conference on computer and information technology; ubiquitous computing and communications; dependable, autonomic and secure computing; pervasive intelligence and computing (CIT/ IUCC/DASC/PICOM). IEEE, 2015, pp. 2359–2364. https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.349

B. Pang, L. Lee “Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales,” In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pp. 115–124, 2005.

J. Pennington, R. Socher, and C. D. Manning, Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP) (pp. 1532–1543) (2014, October).

M. Abbasi, G. Montazer, F. Ghrobani, and Z. Alipour, Categorizing E-Learner Attributes in Personalized E-learning Environments: A Systematic Literature Review. Interdisciplinary Journal of Virtual Learning in Medical Sciences, 12(1), 1–21 (2021).

A. Nandi, F. Xhafa, L. Subirats, and S. Fort, Real-time emotion classification using eeg data stream in e-learning contexts. Sensors, 21(5), 1589, (2021). DOI: https://doi.org/10.3390/s21051589.

N. Mejbri, F. Essalmi, M. Jemni, and B. A. Alyoubi, Trends in the use of affective computing in e-learning environments. Education and Information Technologies, 1–23 (2021). DOI: https://doi.org/10.1007/s10639-021-10769-9.

V. Sanh, L. Debut, J. Chaumond, and T. Wolf, DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter, (2019). arXiv preprint arXiv:1910.01108.

Priyadarshini, R., Barik, R. K., and Dubey, H. (2018). Deepfog: Fog computing-based deep neural architecture for prediction of stress types, diabetes and hypertension attacks. Computation, 6(4), 62.

R. Priyadarshini, R. K. Barik, C. Panigrahi, H. Dubey, and B. K. Mishra, An investigation into the efficacy of deep learning tools for big data analysis in health care. In Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications (pp. 654–666). IGI Global, (2020).

R. Priyadarshini, R. K. Barik, H. Dubey. “Fog-SDN: A light mitigation scheme for DDoS attack in fog computing framework.” International Journal of Communication Systems 33, no. 9 (2020): e4389.

Published

2022-04-16

Issue

Section

Articles