Authors - Xamdamov Utkir Raxmatillaevich, Elov Botir Boltayevich, Alavutdinova Nadira Ganiyevna, Malika Suyunova Odil qizi, Sharipov Soxib Salimovich , Narimova Gulnora Abdumanonovna Abstract - In this article, the architecture of an information system for sentiment analysis of Uzbek-language texts and its key components are examined from both scientific and practical perspectives. The system is based on a multi-layered and microservice architecture, consisting of a user interface (front-end) and a server (back-end) that provides services through a REST API. The back-end components, implemented via a Flask-based RESTful API server, carry out the business logic and sentiment classification. Deep learning models, especially transformer-based architectures (BERT, XLM-RoBERTa), were utilized for analyzing Uzbek texts and demonstrated effective results. The system ensures security, provides integration capabilities, and offers a user-friendly interface to enhance user experience. The modular architecture of the system allows broad scalability and integration with various platforms. As a result of scientific and practical experiments, the system achieved high accuracy (90%) and proved effective for real-time sentiment analysis tasks.
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