Prediction and detection of emotional tone in online social media mental disorder groups using regression and recurrent neural networks.

Online social media networks have become a significant platform for persons with mental illnesses to discuss their struggles and obtain emotional and informational assistance in recent years. One such platform is Reddit, where sub-groups called 'subreddits' exist, based on a variety of topics including mental illnesses such as anxiety or depression. We analyse the user's interactions to calculate the mental health status by formulating and using a parameter called 'emotional tone' representing the user's emotional state. VADER sentiment analysis and TextBlob are used to categorise emotional tone and find distribution of emotional polarity and subjectivity of comments. For final tone prediction, RNN and State-Of-The-Art word embedding techniques are used to develop a predictive model. The resultant model provides end-to-end categorization and prediction of emotional tone. We obtain results with respect to Weighted L1 Loss that deals with extreme responses. The MODEL transcends all the baselines by at least 12.1% and the final emotional status of the authors is positive.