| title | 100,000+ Movie Reviews from Kazakhstan: Russian, Kazakh, and Code-Switched Texts |
|---|---|
| authors | Rustem Yeshpanov |
| date | 2026-05-09 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.08600 |
| upvotes | 2 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | We present a new publicly available corpus of 100,502 movie reviews from Kazakhstan collected from kino.kz, spanning 2001-2025 and covering 4,943 unique titles. The dataset is multilingual, consisting mainly of Russian reviews alongside Kazakh and code-switched texts. Reviews are manually annotated for language and sentiment polarity, and 11,309 reviews additionally contain explicit user-provided ratings. We define two sentiment tasks -- three-way polarity classification and five-class score classification -- and benchmark classical BoW/TF-IDF baselines against multilingual transformer models (mBERT, XLM-RoBERTa, RemBERT). Experimental results show that transformer models consistently outperform classical baselines on polarity classification, while score classification remains challenging under leakage-controlled evaluation due to severe class imbalance and subtle distinctions between adjacent rating levels. |
cite
@misc{yeshpanov2026movie,
title={100,000+ Movie Reviews from Kazakhstan: Russian, Kazakh, and Code-Switched Texts},
author={Rustem Yeshpanov},
year={2026},
eprint={2605.08600},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.08600}
}