O‘ZBEK TILIDAGI MATNLAR SENTIMENT TAHLILI UCHUN AXBOROT TIZIMINI LOYIHALASH VA ISHLAB CHIQISH
DOI:
https://doi.org/10.67227/ag6r4190Kalit so‘zlar:
sentiment tahlil, o‘zbek tili, tabiiy tilni qayta ishlash, axborot tizimi, matnlarni tasniflash, mashinali o‘qitish, transformer modellar, mikroxizmatlar arxitekturasi, REST API, sun’iy intellekt.Abstrak
Ushbu maqolada o‘zbek tilidagi matnlarni avtomatik sentiment tahlil qilishga mo‘ljallangan axborot tizimini loyihalash va ishlab chiqish masalalari yoritilgan. Tadqiqotda sentiment tahlilning nazariy asoslari, mavjud yondashuvlari hamda o‘zbek tilining lingvistik xususiyatlari tahlil qilindi. Tizim arxitekturasi ko‘p qatlamli yondashuv asosida ishlab chiqilib, uning tarkibiga ma’lumotlarni yig‘ish va tayyorlash, matnlarni oldindan qayta ishlash, vektorlash, klassifikatsiya hamda natijalarni vizuallashtirish modullari kiritildi. Shuningdek, tizimning mikroxizmatlar arxitekturasi, REST API asosida integratsiyalash imkoniyatlari va xavfsizlik mexanizmlari ko‘rib chiqildi. Taklif etilgan yechim o‘zbek tilidagi matnlarni sentiment jihatdan avtomatik tahlil qilish, foydalanuvchi fikrlarini baholash hamda turli axborot tizimlariga integratsiyalash uchun amaliy asos bo‘lib xizmat qiladi.
Yuklashlar
Havolalar
1. Shah M., Hazarika A.V., Malhotra M., Patil S.C., Mohanty J. Bridging Emotions and Architecture: Sentiment Analysis in Modern Distributed Systems // arXiv. – 2025. – arXiv:2503.18260.
2. Pang B., Lee L., Vaithyanathan S. Thumbs up? Sentiment Classification Using Machine Learning Techniques // arXiv. – 2002. – arXiv:cs/0205070.
3. Wang X., Xu G., Zhang J., Sun X., Wang L., Huang T. Syntax-Directed Hybrid Attention Network for Aspect-Level Sentiment Analysis // IEEE Access. – 2018. – Vol. 7. – P. 5014–5025.
4. Kokab S.T., Asghar S., Naz S. Transformer-Based Deep Learning Models for the Sentiment Analysis of Social Media Data // Array. – 2022. – Vol. 14. – Article 100157.
5. Abdullah T., Ahmet A. Deep Learning in Sentiment Analysis: Recent Architectures // ACM Computing Surveys. – 2022. – Vol. 55, No. 8. – P. 1–37.
6. Vaswani A., Shazeer N., Parmar N., Uszkoreit J., Jones L., Gomez A.N., Polosukhin I. Attention Is All You Need // Advances in Neural Information Processing Systems. – 2017. – Vol. 30.
7. Yadav A., Vishwakarma D.K. Sentiment Analysis Using Deep Learning Architectures: A Review // Artificial Intelligence Review. – 2020. – Vol. 53, No. 6. – P. 4335–4385.
8. Prottasha N.J., Sami A.A., Kowsher M., Murad S.A., Bairagi A.K., Masud M., Baz M. Transfer Learning for Sentiment Analysis Using BERT-Based Supervised Fine-Tuning // Sensors. – 2022. – Vol. 22, No. 11. – Article 4157.
9. Aliyu Y., Sarlan A., Danyaro K.U., Rahman A.S. Comparative Analysis of Transformer Models for Sentiment Analysis in Low-Resource Languages // International Journal of Advanced Computer Science and Applications. – 2024. – Vol. 15, No. 4.
10. Magueresse A., Carles V., Heetderks E. Low-Resource Languages: A Review of Past Work and Future Challenges // arXiv. – 2020. – arXiv:2006.07264.
11. Ali A., Khan M., Khan K., Khan R.U., Aloraini A. Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning // Computers, Materials & Continua. – 2024. – Vol. 79, No. 1.
12. Elova D., Allanazarova S. O‘zbek tili matnlarida sentiment tahlil usullari // Uzbekistan: Language and Culture. – 2023. – Vol. 1, No. 1.
13. Matlatipov S.G., Rajabov J., Kuriyozov E., Aripov M. UzABSA: Aspect-Based Sentiment Analysis for the Uzbek Language // Proceedings of the 3rd Annual Meeting of the Special Interest Group on Under-resourced Languages @ LREC-COLING 2024. – 2024. – P. 394–403.
14. Allanazarova S., Elova D. Uzbek Texts Sentiment Analysis: Database Development.
15. Elov B., Abdullayev A., Xudayberganov N. O‘zbek tili matnlarini Naive Bayes usuli asosida sentiment tahlil qilish // Digital Transformation and Artificial Intelligence. – 2025. – Vol. 3, No. 2. – P. 153–159.
16. Thuraya Z., Ibrahim A., Utama Y., Indah D.R. Assessing Academic Information System Performance Through Sentiment Analysis // SISTEMASI. – 2025. – Vol. 14, No. 3. – P. 1270–1280.
17. Mao Y., Liu Q., Zhang Y. Sentiment Analysis Methods, Applications, and Challenges: A Systematic Literature Review // Journal of King Saud University – Computer and Information Sciences. – 2024. – Vol. 36, No. 4. – Article 102048.
18. Lasri I., Riadsolh A., Elbelkacemi M. Real-Time Twitter Sentiment Analysis for Moroccan Universities Using Machine Learning and Big Data Technologies // International Journal of Emerging Technologies in Learning. – 2023. – Vol. 18, No. 5. – P. 42–61.
19. Ganguly P., Parihar G. Real-Time Big Data Analysis Using Web Scraping in Apache Spark Environment: Case Study – Mobile Data.
20. Malik S., El-Sayed H., Khan M.A., Alexander H. Application of Containerized Microservice Approach to Airline Sentiment Analysis // 2020 14th International Conference on Innovations in Information Technology (IIT). – IEEE, 2020. – P. 215–220.
21. Di Martino B., Bombace V., D’Angelo S., Esposito A. A Microservices Based Architecture for the Sentiment Analysis of Tweets // International Conference on Advanced Information Networking and Applications. – Cham: Springer International Publishing, 2022. – P. 121–130.
22. Goyal B. Improving Sentiment Analysis Using Containerized Microservices Approach: Doctoral Dissertation. – Dublin: National College of Ireland, 2022.
23. Vasconcelos A., Sousa P., Tribolet J. Information System Architectures: Representation, Planning and Evaluation // Proceedings of the International Conference on Computer, Communication and Control Technologies. – Orlando, USA, 2003. – Vol. 1. – P. 78–84.
