Sentiment Analysis In Uzbek Texts In The Restaurant Field

Sentiment analysis plays a crucial role in natural language processing, particularly in evaluating user comments and extracting valuable insights for classification tasks. This analysis can significantly impact customer satisfaction and influence a company's future development. In our study, we focused on feedback from restaurants in Tashkent, collected via Google Maps. We constructed a dataset that reflects user sentiments and applied logistic regression models for analysis. Through our evaluation, we found that proper preprocessing steps, such as stemming-especially important for agglutinative languages like Uzbek-greatly enhance model performance. These steps help in reducing words to their root forms, allowing the model to better understand the underlying sentiments. Our best-performing model achieved an impressive accuracy of 91%, demonstrating the effectiveness of our approach. The results highlight the importance of sentiment analysis in the restaurant industry, providing actionable insights that can help businesses improve their services and customer experiences. By leveraging user feedback, companies can make informed decisions that foster growth and enhance customer loyalty. Overall, our research underscores the potential of sentiment analysis in driving business success through careful data handling and analysis techniques.

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