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Twitter Arabic Sentiment Analysis to Detect Depression Using Machine Learning

by Dhiaa A. Musleh, Taef A. Alkhales, Reem A. Almakki*, Shahad E. Alnajim, Shaden K. Almarshad, Rana S. Alhasaniah, Sumayh S. Aljameel, Abdullah A. Almuqhim

1 Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam, 31441, Saudi Arabia

* Corresponding Author: Reem A. Almakki. Email: email

(This article belongs to the Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)

Computers, Materials & Continua 2022, 71(2), 3463-3477. https://doi.org/10.32604/cmc.2022.022508

Abstract

Depression has been a major global concern for a long time, with the disease affecting aspects of many people's daily lives, such as their moods, eating habits, and social interactions. In Arabic culture, there is a lack of awareness regarding the importance of facing and curing mental health diseases. However, people all over the world, including Arab citizens, tend to express their feelings openly on social media, especially Twitter, as it is a platform designed to enable the expression of emotions through short texts, pictures, or videos. Users are inclined to treat their Twitter accounts as diaries because the platform affords them anonymity. Many published studies have detected the occurrence of depression among Twitter users on the basis of data on tweets posted in English, but research on Arabic tweets is lacking. The aim of the present work was to develop a model for analyzing Arabic users’ tweets and detecting depression among Arabic Twitter users. And expand the diversity of user tweets, by adding a new label (“neutral”) so the dataset include three classes (“depressed”, “non-depressed”, “neutral”). The model was created using machine learning classifiers and natural language processing techniques, such as Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), K-nearest Neighbors (KNN), AdaBoost, and Naïve Bayes (NB). The results showed that the RF classifier outperformed the others, registering an accuracy of 82.39%.

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Cite This Article

APA Style
Musleh, D.A., Alkhales, T.A., Almakki, R.A., Alnajim, S.E., Almarshad, S.K. et al. (2022). Twitter arabic sentiment analysis to detect depression using machine learning. Computers, Materials & Continua, 71(2), 3463-3477. https://doi.org/10.32604/cmc.2022.022508
Vancouver Style
Musleh DA, Alkhales TA, Almakki RA, Alnajim SE, Almarshad SK, Alhasaniah RS, et al. Twitter arabic sentiment analysis to detect depression using machine learning. Comput Mater Contin. 2022;71(2):3463-3477 https://doi.org/10.32604/cmc.2022.022508
IEEE Style
D. A. Musleh et al., “Twitter Arabic Sentiment Analysis to Detect Depression Using Machine Learning,” Comput. Mater. Contin., vol. 71, no. 2, pp. 3463-3477, 2022. https://doi.org/10.32604/cmc.2022.022508



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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