Classifier model based on text mining for discovery of depression on social media

Abstract

Predicting suicidal people in social networks is a real social issue. Suicide due to depression or anxiety has always been a problem with strong socio-economic consequences. However, global provisions and services for identifying, supporting, and treating mental illness of this nature have been considered as insufficient. In this study, the researcher describes a complete process to dynamically collect suspected tweets according to a lexicon of topics persons with general depression and anxiety would usually post and talk about. The researcher provides a mechanism that automatically captures tweets indicating depression risk behaviors based on Sentiment Analysis and Natural Language Processing and each tweet is based on the polarity or sentiment weight of positive, negative or neutral. In building the model, Five Classification methods will be used for training and testing the data set. The Decision Tree algorithm provided the highest accuracy and precision which is followed closely by Naive Bayesian. The model with the highest accuracy will then be used to assist or support mental health test and decision support systems. The model was then tested and evaluated by a practicing psychologist using a same data set used for testing and training.