NEURAL NETWORK ALGORITHMS FOR INTELLIGENT PROCESSING OF STUDENTS’ REVIEWS

This article is devoted to the problem of using neural network algorithms for automated analysis of student reviews. In the modern conditions of multidisciplinary educational institutions and online learning platforms, student performance becomes an important indicator of the quality of the educational process and serves as a basis for further adjustments. Classical approaches, such as manual processing and descriptive statistics, are not always able to answer the question of how deeply students’ opinions can be understood and analyzed. Neural network algorithms, in comparison with traditional text processing methods, include Recurrent Neural Networks (RNN), BERT and transformers, which have a larger volume of text information and can use more effective logical approaches to the study of hidden patterns. The article considers approaches to processing and analyzing reviews, stages of developing neural network algorithms and their possible impact on education. The potential of more advanced neural network methods is discussed, including a method of learning on a large amount of data, contextual understanding, as well as a smaller number of data units. The study of the neural network approach also indicates that it is important to pay attention to ethics and explanation. The article, in subsequent parts, came to the conclusion that the use of neural network algorithms helps to optimize the management of educational courses and increase the level of their demand among students and raises the question of further research on this topic.

1. Samaraskera, D., Ping, Y., Hoon, T. Obtaining Impactful Feedback from Students: A Continuous Quality Improvement Approach to Enhance the Quality of Students’ Feedback. South East Asian Journal of Medical Education, 8, 2–9 (2014). https://doi.org/10.4038/seajme.v8i1.118.

2. Manning, C.D., Raghavan, P., Schütze, H. An Introduction to Information Retrieval. London: MIT Press, 2009, 562 p.

3. Bishop, C.M. Pattern Recognition and Machine Learning. Springer Science Business Media, 2006, 746 р.

4. Salton, G., McGill, M. J. Introduction to Modern Information Retrieval. 2018. URL: https://sigir.org/files/museum/introduction_to_modern_information_retrieval/frontmatter.pdf

5. Breiman, L. Random Forests. Machine Learning, 45(1), 5–32 (2001).

6. Aman’s AI Journal • NLP • Neural Architectures. URL: https://aman.ai/primers/ai/architectures/

7. Vaswani, A., Shard, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I. Attention is All You Need. Advances in Neural Information Processing Systems, 2017. URL: https://arxiv.org/pdf/1706.03762.

8. Mahesh, B. Machine Learning Algorithms – A Review. International Journal of Science and Research (IJSR), 9 (2019). https://doi.org/10.21275/ART20203995.

9. Braun, V., Clarke, V. Thematic analysis. URL: https://www.researchgate.net/publication/269930410_Thematic_analysis.

10. Garvanova, M. The Significance of Reputation in Choosing and Recommendation a University. Test Engineering and Management, 83, 8545–8553 (2023).

11. Wang, Y., Li, Z., Deng, Zh., Song, H., Yang, J. Deep Learning for Natural Language Processing (2023). https://doi.org/10.5772/intechopen.112550.

12. What is LSTM – Long Short Term Memory? – GeeksforGeeks. 2025. URL: https://www.geeksforgeeks.org/deep-learning-introduction-to-long-short-term-memory/

13. BERT. URL: https://huggingface.co/docs/transformers/mo-del_doc/bert

14. Attention is all you need - The Transformer. URL: https://raviteja-ganta.github.io/attention-is-all-youneed-transformers.

15. Agbonifo, O., Olutayo, V., Oluyede, O. Machine learning-based sentiment analysis of product reviews. Journal of Trends and Challenges in Artificial Intelligence, 2, 257–264 (2025).

16. Pinheiro, A., Ferreira, R., Gasevic, D., Freitas, F. Towards Explainable Prediction Feedback Messages Using BERT. International Journal of Artificial Intelligence in Education, 34 (2023). https://doi.org/10.1007/s40593-023-00375-w.

17. Sayeed, Md Sh., Roji, V., Anbananthen, K. BERT: A Review of Applications in Sentiment Analysis. HighTech and Innovation Journal, 4, 453–462 (2023). https://doi.org/10.28991/HIJ-2023-04-02-015.

18. Hegde, S., Madhunandana, H., Mohana. Sentiment Analysis with LSTM Recurrent Neural Network Approach for Movie Reviews using Deep Learning. 2023. https://doi.org/10.1109/ICIMIA60377.2023.10426266.

19. Shad, R., Gracias, A. Natural Language Processing (NLP) for Sentiment Analysis: A Comparative Study of Machine Learning Algorithms. 2024. https://doi.org/10.20944/preprints202410.2338.v1.

20. Nguyen, A., Ngo, H., Hong, Y., Dang, B., Nguyen Bich-Phuong. Ethical principles for artificial intelligence in education. Education and Information Technologies, 28 (2022). https://doi.org/10.1007/s10639-022-11316-w.