Sentiment analysis in low-resource contexts: BERT's impact on Central Kurdish.
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 mi...
| Published in: | Language Resources & Evaluation Vol. 59; no. 3; pp. 2213 - 2244 |
|---|---|
| Main Authors: | , , |
| Format: | Article |
| Published: |
Springer Nature
Sep2025
|
| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=186909064&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 186909064 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2025 vid: 59 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 186909064 10.1007/s10579-024-09805-0 ppf: 2213 ppct: 31 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.8MB tig: atl: Sentiment analysis in low-resource contexts: BERT's impact on Central Kurdish. aug: au: Awlla, Kozhin Muhealddin Veisi, Hadi Abdullah, Abdulhady Abas affil: https://ror.org/03k9q0e81 Computer Science Department, Faculty of Science, Soran University, Soran, Erbil, Kurdistan, Iraq https://ror.org/05vf56z40 School of Intelligent Systems, College of Interdisciplinary Science and Technologies, University of Tehran, Tehran, Iran https://ror.org/00268wk31 Artificial Intelligence and Innovation Centre, University of Kurdistan Hewler, Erbil, Iraq su: Sentiment analysis Low-resource languages Kurds Artificial neural networks Natural language processing Language models sug: subj: Sentiment analysis Low-resource languages Kurds Artificial neural networks Natural language processing Language models keyword: BERT BiLSTM Central Kurdish language Communication and Culture Linguistics Deep learning Language ab: 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. The steps include collecting and normalizing a large corpus of Kurdish texts, pretraining BERT with a special tokenizer for Kurdish, and developing different models for sentiment analysis including Bidirectional Long Short-Term Memory (BiLSTM), Multi-Layer Perceptron (MLP), and finetuning the BERT classifier. The proposed approach consists of 3 classes: positive, negative, and neutral sentiment analysis using a sentiment embedding of BERT in four different configurations. The accuracy of the best-performing classifier, BiLSTM, is 74.09%. For the BERT with an MLP classifier model, the maximum accuracy achieved is 73.96%, while the fine-tuned BERT model tops the others with 75.37% accuracy. Additionally, the fine-tuned BERT model demonstrates a vast improvement when focused on two 2-class sentiment analyses positive and negative with an accuracy of 86.31%. The study makes a comprehensive comparison, highlighting BERT's superiority over the traditional ones based on accuracy and semantic understanding. It is motivated because several results are obtained that the proposed BERT-based models outperform Word2Vec models conventionally used here by a remarkable accuracy gain in most sentiment analysis tasks. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
|---|