Detection of Expressions of Violence Targeting Health Workers with Natural Language Processing Techniques
Yazarlar (4)
Dr. Öğr. Üyesi Merve Varol Arısoy Burdur Mehmet Akif Ersoy Üniversitesi, Türkiye
Dr. Öğr. Üyesi Mehmet Ali YALÇINKAYA Kırşehir Ahi Evran Üniversitesi, Türkiye
Doç. Dr. Remzi Gürfidan Isparta Uygulamalı Bilimler Üniversitesi, Türkiye
Dr. Öğr. Üyesi Ayhan Arısoy Burdur Mehmet Akif Ersoy Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Applied Sciences Switzerland (Q2) | ||
| Dergi ISSN | 2076-3417 Dergi Bilgileri (2025) | ||
| Dergi Tarandığı Indeksler | SCI-Expanded | ||
| Makale Dili | İngilizce | Basım Tarihi | 02-2025 |
| Cilt / Sayı / Sayfa | 15 / 4 / 1715– | DOI | 10.3390/app15041715 |
| Makale Linki | https://doi.org/10.3390/app15041715 | ||
| UAK Araştırma Alanları |
Yapay Zeka
Makine Öğrenmesi
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| Özet |
| The aim of this study is to detect expressions of violence against healthcare workers using natural language processing techniques. Experiments on various NLP models have shown that violent expressions can be successfully classified using textual data. The RAG-ECE model performed the best in this study with a 97.97% accuracy rate and a 97.67% F1 score. The model provided a strong balancing performance in the “no violence” class with 97.71% precision and 97.67% recall rates. In the “violence present” class, it reached 97.67% accuracy and was evaluated as a reliable classifier with both low false positive (3.92%) and low false negative (2.78%) rates. In addition to RAG-ECE, the GPT model provided a milder alternative with 96.19% accuracy and a 96.26% F1 score. The study also compared the performances of other models, such as GPT, BERT, SVM, and NB, and stated that they are considered suitable alternatives due to their low computational costs, especially in small- and medium-sized datasets. The findings of the study show that NLP-based systems offer an effective solution for the early detection and prevention of expressions of violence against healthcare workers. |
| Anahtar Kelimeler |
| natural language processing | text classification | violence detection | violence in health |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Web of Science | 2 |
| Scopus | 2 |
| Google Scholar | 5 |