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Extracting useful information for sentiment analysis and classificationproblems from a big amount of user-generated feedback, such as restaurantreviews, is a crucial task of natural language processing, which is not onlyfor customer satisfaction where it can give personalized services, but can alsoinfluence the further development of a company. In this paper, we present awork done on collecting restaurant reviews data as a sentiment analysis datasetfor the Uzbek language, a member of the Turkic family which is heavily affectedby the low-resource constraint, and provide some further analysis of the noveldataset by evaluation using different techniques, from logistic regressionbased models, to support vector machines, and even deep learning models, suchas recurrent neural networks, as well as convolutional neural networks. Thepaper includes detailed information on how the data was collected, how it waspre-processed for better quality optimization, as well as experimental setupsfor the evaluation process. The overall evaluation results indicate that byperforming pre-processing steps, such as stemming for agglutinative languages,the system yields better results, eventually achieving 91% accuracy result inthe best performing model

Uzbek Sentiment Analysis based on local Restaurant Reviews