Twitter Arabic Sentiment Analysis to Detect Depression Using Machine Learning

作     者：Dhiaa A.Musleh Taef A.Alkhales Reem A.Almakki Shahad E.Alnajim Shaden K.Almarshad Rana S.Alhasaniah Sumayh S.Aljameel Abdullah A.Almuqhim 

作者机构：Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第71卷第5期

页      面：3463-3477页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 

基　　金：This study was supported by Deanship of Scientific Research at Imam Abdulrahman Bin Faisal University under the project No.2021-184-CSIT 

主　　题：Depression sentiment analysis twitter supervised learning machine learning 

摘      要：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 *** Arabic culture,there is a lack of awareness regarding the importance of facing and curing mental health ***,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 *** are inclined to treat their Twitter accounts as diaries because the platform affords them *** 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 *** aim of the present work was to develop a model for analyzing Arabic users’tweets and detecting depression among Arabic Twitter *** 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%.