Machine Learning-based Smart Students' Complaint Resolution System
Abstract
For enhancing the quality of any educational institution, student feedback and complaints play an important role. Effectively handling complaints is crucial for ensuring student happiness and expediting the resolution of issues. Traditional manual complaint handling processes, however, are frequently cumbersome and ineffective, resulting in delays that irritate employees and students alike. Our research presents an academic facilitation system that automatically categorizes student complaints by department and particular aspect to address these issues. Our study focuses only on complaints pertaining to the academic setting, in contrast to many other approaches that use supervised and unsupervised learning techniques for common complaint management. We are manually an-notated the data across four departments and forty-two expectations after conducting a survey to gather actual student input because there was no previous dataset of academic complaints accessible. Afterwards, machine learning and deep learning models for classification and aspect identification were trained using this annotated dataset. Bidirectional Encoder Representations from Transformers (BERT), Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forest (RF), Decision Trees (DT), and One Vs All classifiers were among the several algorithms we tested with. Standard performance measures were used to evaluate the models. Among these, the Random Forest model performed well, with 65% accuracy across all our test data, and the One Vs All classifier reached 94% accuracy in both department classification and aspect identification. The study did have several drawbacks, though. The medium-length complaint letters that made up our dataset did not specifically address latent meanings. In the future work, we intend to increase the size of the dataset, incorporate longer complaint texts, and investigate cutting-edge methods for identifying hidden or implicit meanings in student complaints.
References
Banga, D., & Peddireddy, K. (2023). Artificial intelligence for customer complaint management. International Journal of Computer Trends and Technology, 71(3), 1–6.
Bastani, K., Namavari, H., & Shaffer, J. (2019). Latent Dirichlet allocation (LDA) for topic modeling of the CFPB consumer complaints. Expert Systems with Applications, 127, 256–271.
Bencke, L., Cechinel, C., & Munoz, R. (2020). Automated classification of social network messages into smart cities dimensions. Future Generation Computer Systems, 109, 218–237.
Feng, L., Senapati, J., & Liu, B. (2022). TaDaa: Real time ticket assignment deep learning auto advisor for customer support, help desk, and issue ticketing systems. arXiv preprint arXiv:2207.11187.
Fuchs, S., Drieschner, C., & Wittges, H. (2022). Improving support ticket systems using machine learning: A literature review. In Proceedings of the 55th Hawaii International Conference on System Sciences (pp. 1893–1902).
HaCohen-Kerner, Y., Miller, Y., & Yigal, G. G. (2019). Automatic classification of complaint letters according to service provider categories. Information Processing & Management, 56(6), 102102.
Herbold, S., Hautli-Janisz, A., Heuer, U., Reinke, C., & Grabowski, J. (2023). A large-scale comparison of human-written versus ChatGPT-generated essays. Scientific Reports, 13, 18617.
Kgomo, T. (2024). Classification of customer complaints using machine learning algorithms. University of Cape Town, Faculty of Science, Department of Statistical Sciences.
Mandal, A., Malhotra, N., Agarwal, S., Ray, A., & Sridhara, G. (2019). Automated dispatch of helpdesk email tickets: Pushing the limits with AI. In Proceedings of the AAAI Conference on Artificial Intelligence, 33, 9381–9388.
Munir, K., Ismail, F., Ghalwash, A., & El-Bakry, H. (2022). AgroSupportAnalytics: A cloud-based complaints management and decision support system for sustainable farming in Egypt. Egyptian Informatics Journal, 23(1), 73–82.
Naik, P. K., Prashanth, T., Chandru, S., Jaganath, S., & Balan, S. (2023). Consumer complaints classification using machine learning & deep learning. International Research Journal on Advanced Science Hub, 5(05S), 116–122.
Paramesh, S., & Shreedhara, K. (2018). Automated IT service desk systems using machine learning techniques. In Data analytics and learning: Proceedings of DAL 2018 (pp. 331–346). Springer.
Rosid, M. A., Fitrani, A. S., Astutik, I. R. I., Mulloh, N. I., & Gozali, H. A. (2020). Improving text preprocessing for student complaint document classification using Sastrawi. IOP Conference Series: Materials Science and Engineering, 874, 012017.
Singh, A., Jha, S. K., Bansal, P., & Bharti, K. (2022). Multitask learning for complaint identification and sentiment analysis. Cognitive Computation, 14(1), 212–227.
Suleymanov, U., & Rustamov, S. (2018). Automated news categorization using machine learning methods. IOP Conference Series: Materials Science and Engineering, 459, 012006.
