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Abstract
Understanding user experience is crucial for business success, yet analyzing user reviews in low-resource languages presents significant challenges due to the scarcity of annotated data. To address this gap, we conducted an in-depth analysis of 27,985 Uzbek reviews from the Google Play Store, focusing on the six key aspects of the User Experience Honeycomb model. Our study meticulously annotated these reviews, comprising a total of 43,712 sentences, to assess the sentiment polarity across these six dimensions. To benchmark this task, we propose an integrated framework that leverages pre-trained models along with GCN to capture semantic relationships, thereby enhancing the accuracy of sentiment analysis. Our approach demonstrated superior performance, achieving an absolute improvement of 0.30 in the F1 score for multi-classification tasks and 0.43 for binary classification tasks compared to existing baseline methods. These results underscore the effectiveness of our proposed framework in understanding user experience in low-resource language contexts, offering valuable insights for businesses and researchers alike.
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