Detection of Depression from Arabic Tweets Using Machine Learning

Areej Alzoubi, Department of Computer Information Systems Jordan University of Science and Technology Jerash, JordanFollow
Ahmad Alaiad, Department of Computer Information Systems Jordan University of Science and Technology, Jerash, JordanFollow
Khaled Alkhattib, Department of Computer Information Systems Jordan University of Science and Technology, Jerash, JordanFollow
Ahed J. Alkhatib, Legal Medicine, Toxicology of Forensic Science and Toxicology Department Jordan University of Science and Technology, Irbid, JordanFollow
Aseel Abu Aqoulah, Department of Computer Information Systems Yarmouk University, Irbid, JordanFollow
Almo’men Bellah Alawnah, Department of Industrial Engineering Jordan University of Science and Technology, Irbid, JordanFollow
Ola Hayajnah, Department of Computer Information Systems Jordan University of Science and Technology, Jerash, JordanFollow

ORCID

Areej Alzoubi Aseel Abu Aqoulah: https://orcid.org/0009-0004-3620-1282

Ahmad Alaiad: https://orcid.org/0009-0002-4477-078X

Khaled Alkhattib: https://orcid.org/0009-0006-2966-1883

Ahed J. Alkhatib: https://orcid.org/0000-0003-3359-8128

Aseel Abu Aqoulah: https://orcid.org/0009-0001-9464-1970

Almo’men bellah Alawnah: https://orcid.org/0000-0003-2001-9422

Ola Hayajnah: https://orcid.org/0009-0003-8492-295X

Keywords

Depression, Twitter, Tweets, Mental Health, Machine Learning

Article Type

Original Article

Abstract

Depression has become the disease of the times and has caused suffering and disruption in the lives of millions of people around the world of all ages. Method: We obtained 16,581 Arabic tweets, whether they express depression or not, and the symptoms they contain for 1439 Arab Twitter users. We classified whether the user is depressed or not. We used many machine learning algorithms: DT, RF, Mutational Naïve Bayes, and AdaBoost , we also used feature extraction like BOW and TF-IDF. The result: Our experiments showed that Mutational Naïve Bayes with TF-IDF had the highest accuracy of 86% when rating tweets. Conclusion: Caring for the mental health of people is very important, as some measures must be taken to maintain the mental health of people in the early stages of infection.

How to Cite

Alzoubi, Areej; Alaiad, Ahmad; Alkhattib, Khaled; Alkhatib, Ahed J.; Aqoulah, Aseel Abu; Alawnah, Almo’men Bellah; and Hayajnah, Ola (2024) "Detection of Depression from Arabic Tweets Using Machine Learning," Sustainable Machine Intelligence Journal: Vol. 6: Iss. 1, Article 3.
DOI: https://doi.org/10.61356/SMIJ.2024.11103