Research Trends in Machine Learning for Arabic Language Studies: A Bibliometric Analysis - Roobiatul Adawiyah; Luthfatul Qibthiyah (al-Afkar, Journal For Islamic Studies, 2026)
This study aims to map the development of publications on the application of Machine Learning in Arabic language studies through bibliometric analysis. Research data was obtained from the Lens.org database and analyzed using VOSviewer software to identify publication trends, keyword networks, and research opportunities. The results show that the number of publications has increased significantly since 2015 and peaked in the 2020–2025 period. Keyword co-occurrence analysis shows that Machine Learning is a central theme closely related to deep learning, Arabic Natural Language Processing, classification, and Arabic Sentiment Analysis. Overlay visualizations show a shift in research focus from basic topics, such as translation, tagging, and text mining, to more sophisticated themes, such as Arabic hate speech, clustering, and ensemble learning. Meanwhile, density analysis indicates that Arabic Sign Language, FastText, Ensemble Learning, and Arabic Hate Speech still have a low publication density, thus potentially becoming future research directions. The findings of this study provide a comprehensive overview of the trends and developments in Machine Learning research in Arabic language studies and can serve as a reference for researchers in determining research themes and developing artificial intelligence-based innovations in Arabic language learning and processing.