KazSAnDRA: Kazakh Sentiment Analysis Dataset of Reviews and Attitudes

Rustem Yeshpanov, Huseyin Atakan Varol

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.
Anthology ID:
2024.lrec-main.844
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
9657–9667
Language:
URL:
https://aclanthology.org/2024.lrec-main.844
DOI:
Bibkey:
Cite (ACL):
Rustem Yeshpanov and Huseyin Atakan Varol. 2024. KazSAnDRA: Kazakh Sentiment Analysis Dataset of Reviews and Attitudes. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 9657–9667, Torino, Italia. ELRA and ICCL.
Cite (Informal):
KazSAnDRA: Kazakh Sentiment Analysis Dataset of Reviews and Attitudes (Yeshpanov & Varol, LREC-COLING 2024)
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https://preview.aclanthology.org/landing_page/2024.lrec-main.844.pdf
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