Predicting Depression Using Social Media Posts - Abu Bakar, Fahem; Mohd Nawi, Nazri (Journal of Soft Computing and Data Mining, 2021)
The use of Social Network Sites (SNS) is on the rise these days, particularly among the younger generations. Users can communicate their interests, feelings, and everyday routines thanks to the availability of social media sites. Many studies show that properly utilizing user-generated content (UGC) can aid in determining people’s mental health status. The use of the UGC could aid in the prediction of mental healthparticularly depression where it is a significant medical condition that impairs one’s ability to work, learn, eat, sleep, and enjoy life. However, all of the information about a person’s mood and negativism can be gather from their SNS user profile. Therefore, this study utilize SNS as a data source by using machine learning models to screen and identify users in categorizing users based on their mental health. The performance of three machine learning models are evaluated to classify the UGC which are : Decision Forest, Neural Network and Support Vector Machine (SVM). The resuls shows that the accuracy and recall result of the Neural Network model is the same as the Support Vector Machine (SVM) model which is 78.27% and 0.042 but Neural Network performs better in the average precision value. This proves that the Neural Network model is the best models for making predictions to determine the level of depression by using social media posts.