Abstract:
Social networks have been developed as a great point for its users to communicate with their interested friends and share their opinions, photos, and videos reflecting their mood, feelings and sentiments. This creates an opportunity to analyze social network data for user’s feelings and sentiments to investigate their mood and attitude when they are communicating via these online tools. Existing literature reflects on the use of various Application Programming Interfaces (API) such as Graph API, REST API and Streaming API to collect data from social networks and used for depression detection. Although diagnosis of depression using social networks data has picked an established position globally, there are several dimensions that are yet to be detected. In this study, we aim to perform depression analysis on Facebook data collected from an online public source by using machine learning techniques. We have evaluated the efficiency of machine learning techniques using a set of various psycholinguistic and textual features. The result shows that in different experiments Decision Tree (DT) gives the highest accuracy than other ML approaches to find the depression. It is anticipated that the analysis reported in this study would contribute in developing any electronic disease management system both for communities and healthcare professionals groups.
