Detection of Depression from Arabic Tweets Using Machine Learning

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.