@inproceedings{db602ade608b4a2193231d1d982143cb,
title = "KazSAnDRA: Kazakh Sentiment Analysis Dataset of Reviews and Attitudes",
abstract = "This paper presents KazSAnDRA, a dataset developed for Kazakh sentiment analysis that is the first and largest publicly available dataset of its kind. KazSAnDRA comprises an extensive collection of 180,064 reviews obtained from various sources and includes numerical ratings ranging from 1 to 5, providing a quantitative representation of customer attitudes. The study also pursued the automation of Kazakh sentiment classification through the development and evaluation of four machine learning models trained for both polarity classification and score classification. Experimental analysis included evaluation of the results considering both balanced and imbalanced scenarios. The most successful model attained an F1-score of 0.81 for polarity classification and 0.39 for score classification on the test sets. The dataset and fine-tuned models are open access and available for download under the Creative Commons Attribution 4.0 International License (CC BY 4.0) through our GitHub repository.",
keywords = "BERT, dataset, Kazakh, KazSAnDRA, polarity, review, sentiment analysis, text classification",
author = "Rustem Yeshpanov and Varol, {Huseyin Atakan}",
note = "Publisher Copyright: {\textcopyright} 2024 ELRA Language Resource Association: CC BY-NC 4.0.; Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024 ; Conference date: 20-05-2024 Through 25-05-2024",
year = "2024",
language = "English",
series = "2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings",
publisher = "European Language Resources Association (ELRA)",
pages = "9657--9667",
editor = "Nicoletta Calzolari and Min-Yen Kan and Veronique Hoste and Alessandro Lenci and Sakriani Sakti and Nianwen Xue",
booktitle = "2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings",
}