LINGUISTIC CHALLENGES IN PERFORMING ASPECT-BASED SENTIMENT ANALYSIS IN THE UZBEK LANGUAGE
Keywords:
Aspect-Based Sentiment Analysis, sentiment analysis, Uzbek language, NLPAbstract
This paper analyzes the linguistic, technical, and methodological challenges encountered in performing Aspect-Based Sentiment Analysis (ABSA) for the Uzbek language. The study demonstrates that the agglutinative nature of the Uzbek language, its morphological richness, flexible word order, and context-dependent emotional expressions significantly affect the sentiment analysis process. In addition, the lack of annotated corpora and sentiment lexicons, as well as issues related to encoding, spelling, and mixed writing systems, limit the effectiveness of existing models. To address these challenges, a comprehensive approach is proposed, including the expansion of language resources, the application of preprocessing techniques that account for morphological and semantic features, the use of context-aware deep learning models, and the development of aspect-based analytical methods. The results of this study provide both theoretical and practical foundations for advancing sentiment analysis in the Uzbek language and serve as a methodological basis for future research.
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