100,000+ Movie Reviews from Kazakhstan: Russian, Kazakh, and Code-Switched Texts
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866913115225980928 |
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| author | Yeshpanov, Rustem |
| author_facet | Yeshpanov, Rustem |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_08600 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | 100,000+ Movie Reviews from Kazakhstan: Russian, Kazakh, and Code-Switched Texts Yeshpanov, Rustem Computation and Language 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. |
| title | 100,000+ Movie Reviews from Kazakhstan: Russian, Kazakh, and Code-Switched Texts |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2605.08600 |