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.

该研究介绍了 KazSAnDRA 数据集，这是首个也是最大的公开可用的哈萨克情感分析数据集，由 180,064 份来自不同来源的评论组成，包括从 1 到 5 的数值评分，提供了客户态度的定量表示。该研究还通过开发和评估四个机器学习模型，旨在实现哈萨克情感分类的自动化，包括极性分类和评分分类的培训。实验分析考虑了平衡和不平衡的情况。最成功的模型在测试集上实现了 0.81 的极性分类 F1 得分和 0.39 的评分分类得分。该数据集和优化模型以知识共享署名 4.0 国际许可（CC BY 4.0）的形式开放获取，并可通过我们的 GitHub 存储库进行下载。