(untitled) (Eureka Journal of Artificial Intelligence and Data Innovation, 2026)
This article studies a modern approach to sentiment (negative, neutral, positive) analysis of Uzbek texts. In the study, text placement vectors are obtained using the BERT model (editor/editor-bert-base) adapted to the Uzbek language and their classification is performed using a Convolutional Neural Network (CNN) and BiGRU. The model is tested on a mini Uzbek sentiment analysis dataset and evaluated using weighted F1-score, confusion matrix, and classification reports. The experimental results show that the combined architecture of BERT-based embedding vectors + CNN + BiGRU provides effective results in classifying Uzbek texts. This approach can be especially useful for resource-limited Uzbek datasets.