Detection and Analysis of Mental Health Illness using Social Media

Abstract

Recently social media has been a widely used network that connects people around the world. Not only this but people sharing their life events, thoughts through posts, status updates all gather up as a big data resource. This resource is helpful in conducting various researches, analyses including big data and machine learning. In this study, we analyzed six mental health issues using Reddit’s data. The data obtained summarizes; Depression, Anxiety, Bipolar, Bipolar Disorder, Schizophrenia, Autism and Mental Health which is a general class which discusses mental health. Experimentation is done using various deep learning and NLP techniques applied for classification such as Convolutional Neural Network, Long-short term memory network, Gated Recurrent Unit, Bi- Long-short term memory network and Bi-Gated Recurrent Unit. In addition to these traditional techniques, pre-trained BERT model and RoBERTa model have been applied. Finally a hybrid framework is presented using hierarchical classification and pre-trained RoBERTa fine tuned on the respective mental health data. The last phase compares results of the baseline deep learning models with the presented framework. The results show that the average accuracy of the hierarchical classification with two level hierarchy gives 84% of accuracy on test data.