Therapy Recommendation Based on Level of Depression Using Social Media Data

Social media is a massive platform with currently over 100 million registered users. It is a platform where individuals express themselves along with their interests. These expressions of individual can be used to identify their mental status. That being said, depression and anxiety are the dominant cause for illness and ill-health across the world. Studies show that user’s mental health can be predicted by their everyday use of language. This paper examines the tweets for analyzing the linguistic and behavioral features for classifying the levels of depression among the users. In order to classify the levels of depression, a knowledge base of the words that are associated with depression/anxiety has been created. The model evaluated this using simple text mining techniques to measure the mental health status of the users and provide appropriate recommendations.