Predicting Depression from Social Networking Data using Machine Learning Techniques

Abstract

In this era of COVID-19 pandemic, as more people self-isolate themselves, psychological health issues like depression, anxiety, and stress is an increasing concern all over the world. The purpose of this study is to investigate the data from social forums, where we found communities of depressed people sharing their thoughts and emotions in the forums, these forums also receive advices and support. In this paper, we will analyse the "depressed" text; by manipulating the data, extracting features, categorising, and try to understand what are the attributes of "depressed" text, and how we can "predict" whether a text should be marked as depressed or not. Using text analysis and text data mining techniques, the text obtained from the social forums was analysed and three different machine learning algorithms were used to predict depression. After cross validation overall accuracy of 99.69% was obtained as the best score using the proposed system. This study definitively answers the question regarding using human basic language and communication of personal experiences, for the prediction of depression and can be reached easily. Furthermore, not only actions, habits and behaviour of a person, text too can be used for accurate diagnosis of depression.