Year: 2026
Type: conference-paper
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 userprovided 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.
Author: Rustem Yeshpanov
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166871080