# KuBERT: Central Kurdish BERT Model and Its Application for Sentiment Analysis

arXiv: https://arxiv.org/abs/2509.16804 · HTML: https://mlsanity.com/paper/2509.16804/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 43 references that are in the map.
- First posted on arXiv 20 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets](https://mlsanity.com/paper/1906.05474/) (2019)
- [The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://mlsanity.com/paper/2103.06678/) (2021)
- [Fast WordPiece Tokenization](https://mlsanity.com/paper/2012.15524/) (2020)
- [Sentiment analysis in Bengali via transfer learning using multi-lingual BERT](https://mlsanity.com/paper/2012.07538/) (2020)
- [Exploiting BERT to improve aspect-based sentiment analysis performance on Persian language](https://mlsanity.com/paper/2012.07510/) (2020)
- [Transfer Learning for Low-Resource Sentiment Analysis](https://mlsanity.com/paper/2304.04703/) (2023)

## Built on by (most cited first, 1 of 1)

- [KurdSTS: The Kurdish Semantic Textual Similarity](https://mlsanity.com/paper/2510.02336/) (2025)

## Abstract (arXiv)

This paper enhances the study of sentiment analysis for the Central Kurdish language by integrating the Bidirectional Encoder Representations from Transformers (BERT) into Natural Language Processing techniques. Kurdish is a low-resourced language, having a high level of linguistic diversity with minimal computational resources, making sentiment analysis somewhat challenging. Earlier, this was done using a traditional word embedding model, such as Word2Vec, but with the emergence of new language models, specifically BERT, there is hope for improvements. The better word embedding capabilities of BERT lend to this study, aiding in the capturing of the nuanced semantic pool and the contextual intricacies of the language under study, the Kurdish language, thus setting a new benchmark for sentiment analysis in low-resource languages.