Short text classification of social groups focused on courses

• Antonio Leandro Martins Candido UECE / IFCE http://orcid.org/0000-0001-5839-0273
• Jose Everardo Bessa Maia UECE https://orcid.org/0000-0002-4983-1724

DOI: https://doi.org/10.5753/eniac.2025.10530

Abstract

The ubiquitous possession of communication devices enhances a Social IoT whose most visible face is Social Networks. Social networks, also called relationship networks, are an important source for collecting unbiased feelings or mental states of groups of students in relation to a course in which they participate. These groups in social networks, often isolated and without the knowledge of the school administration, function as a discussion forum and the analysis of the texts posted there can enhance educational management with preventive measures to mitigate factors of course failure. This work develops the classification of sentiment in texts of groups of students with a view to detecting the need for intervention by educational management. The accuracy of the method is reinforced by a domain adaptation technique to take advantage of labeled data from other related domains. The results obtained are compatible with the state of the art of sentiment classification in other application domains.

References

Agrawal, A., Venkatraman, J., Leonard, S., and Paepcke, A. (2015). Youedu: Addressing confusion in mooc discussion forums by recommending instructional video clips. In Proceedings of the 8th International Conference on Educational Data Mining, EDM 2015, pages 297–304.

Antunes, M., Gomes, D., and Aguiar, R. L. (2018). Towards iot data classification through semantic features. Future Generation Computer Systems, 86:792–798.

Candido, A. L. M. and Maia, J. E. B. (2023). Detecting urgent instructor intervention need in learning forums with a domain adaptation. In Intelligent Systems Design and Applications, pages 502–512. Springer Nature.

Capuano, N. (2021). Transfer learning techniques for cross-domain analysis of posts in massive educational forums. In Intelligent Systems and Learning Data Analytics in Online Education, pages 133–152. Elsevier.

Dogra, V., Verma, S., Kavita, Chatterjee, P., Shafi, J., Choi, J., and Ijaz, M. F. (2022). A complete process of text classification system using state-of-the-art nlp models. Computational Intelligence and Neuroscience, 2022(1):1883698.

Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., and Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(1):2096–2030.

Kumar, A. (2022). Contextual semantics using hierarchical attention network for sentiment classification in social internet-of-things. Multimedia Tools and Applications, 81(26):36967–36982.

Lima, J. M. C. and Maia, J. E. B. (2018). A topical word embeddings for text classification. In Anais do XV Encontro Nacional de Inteligência Artificial e Computacional, pages 25–35. SBC.

Luo, W. and Zhang, L. (2022). Question text classification method of tourism based on deep learning model. Wireless communications and mobile computing, 2022(1):4330701.

Luo, X., Yu, Z., Zhao, Z., Zhao, W., and Wang, J.-H. (2022). Effective short text classification via the fusion of hybrid features for iot social data. Digital Communications and Networks, 8(6):942–954.

Meng, L. (2024). The convolutional neural network text classification algorithm in the information management of smart tourism based on internet of things. IEEE Access.

Mohammed, A. H. K., Jebamikyous, H.-H., Nawara, D., and Kashef, R. (2021). Iot text analytics in smart education and beyond. Journal of Computing in Higher Education, 33(3):779–806.

Qiang, Y., Kumar, S. T. S., Brocanelli, M., and Zhu, D. (2022). Tiny rnn model with certified robustness for text classification. In 2022 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE.

Sun, B. and Saenko, K. (2016). Deep coral: Correlation alignment for deep domain adaptation. European conference on computer vision, pages 443–450.

Wang, Z. (2024). Construction of an internet of things industry chain classification model based on irfa and text analysis. Journal of Information Processing Systems, 20(2):215–225.

Zhang, Y. and Rao, Z. (2020). Hierarchical attention networks for grid text classification. In 2020 IEEE International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), volume 1, pages 491–494. IEEE.