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Building a New Sentiment Analysis Dataset for Uzbek Language and Creating Baseline Models

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CITIC, Grupo LYS, Departamento de Computación. Facultade de Informática, Campus de Elviña, Universidade da Coruña, 15071 A Coruña, Spain
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Applied Mathematics and Computer Analysis Department, National University of Uzbekistan, University Str. 4, Tashkent 100174, Uzbekistan
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Author to whom correspondence should be addressed.
Presented at the 2nd XoveTIC Congress, A Coruña, Spain, 5–6 September 2019.
Proceedings 2019, 21(1), 37; https://doi.org/10.3390/proceedings2019021037
Published: 2 August 2019
(This article belongs to the Proceedings of The 2nd XoveTIC Conference (XoveTIC 2019))
Making natural language processing technologies available for low-resource languages is an important goal to improve the access to technology in their communities of speakers. In this paper, we provide the first annotated corpora for polarity classification for Uzbek language. Our methodology considers collecting a medium-size manually annotated dataset and a larger-size dataset automatically translated from existing resources. Then, we use these datasets to train sentiment analysis models on the Uzbek language, using both traditional machine learning techniques and recent deep learning models.
Keywords: sentiment analysis dataset; Uzbek language; sentiment classification; Natural Language Processing; deep learning sentiment analysis dataset; Uzbek language; sentiment classification; Natural Language Processing; deep learning
MDPI and ACS Style

Kuriyozov, E.; Matlatipov, S. Building a New Sentiment Analysis Dataset for Uzbek Language and Creating Baseline Models. Proceedings 2019, 21, 37.

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