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Detection of Expressions of Violence Targeting Health Workers with Natural Language Processing Techniques      
Yazarlar (4)
ARISOY MERVE VAROL
Dr. Öğr. Üyesi Mehmet Ali YALÇINKAYA Dr. Öğr. Üyesi Mehmet Ali YALÇINKAYA
Kırşehir Ahi Evran Üniversitesi, Türkiye
REMZİ GÜRFİDAN
AYHAN ARISOY
Devamını Göster
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
Makale Türü Özgün Makale
Makale Alt Türü SCOPUS dergilerinde yayımlanan tam makale
Dergi Adı Applied Sciences
Dergi ISSN 076-3417
Dergi Tarandığı Indeksler SCI-Expanded
Makale Dili İngilizce
Basım Tarihi 02-2025
Cilt No 15
Sayı 4
Doi Numarası 10.3390/app15041715
Makale Linki https://doi.org/10.3390/app15041715