Software System for Hybrid Sentiment Analysis of Uzbek Texts using Named Entities

Abstract

This article presents a software package for automatic sentiment analysis of Uzbek texts. The system relies on a hybrid approach, combining a transformer model, a named entity extraction (NER) module, and a specially compiled sentiment dictionary of the Uzbek language. The relevance of this development is due to the growing volume of informal texts on social networks and the lack of ready-made tools for processing them. The package implements a full processing cycle: text cleaning and normalization, entity extraction, sentiment detection and keyword detection, and visualization of the results in a built-in web interface. The models and dictionary are adapted to the agglutinative and orthographic features of the Uzbek language, increasing resilience to colloquial and mixed forms of writing. The package's architecture, main software modules and their interactions, as well as the operating principle of the application interface (REST API) are briefly described. Examples of the system's application for analyzing user reviews and messages are provided, confirming its suitability for applied opinion monitoring tasks. Based on the results of initial experiments, a significant improvement in quality is achieved compared to basic text models without taking into account NER and lexicon.

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