Depression Detection from Bangla Facebook Status using Machine Learning Approach
Depression is a mood disorder. This mental illness is a silent killer. It badly affects how human feels, thinks, and acts. Every year, all over the world huge number of people commit suicide due to depression[1] In Asia region, most of the Bengali speaking people use Facebook rather than Twitter for their social communication and share most of their emotions there. That is why Facebook is a potential source for our research. Although detecting depression is a psychological matter, in this paper there was an attempt to predict depression using machine learning. Here, a model is proposed to predict depressed or not depressed using machine learning from Bangla Facebook Status. The work is done with 50 Facebook users' data, among them 17 people had committed suicide. Naive Bayes[2] Multinomial Naive Bayes[3] Logistic Regression[4] Linear SVC[5] and some other classifiers used for this work. The work achieved satisfying accuracy as the first work in Bangla language. General Terms Machine learning, Supervised Learning, Bangla Facebook Status, Depression Detection, Sentiment Analysis.
International Journal of Computer Applications