Social Media Mining to Detect Mental Health Disorders Using Machine Learning

Abstract

AbstractThe growth of social media has caused people to post a significant content online. They find it easier to communicate in the virtual world rather than to talk to a person and share their problems. This has given rise to a pertinent question—can we use social media to help these youngsters to understand the state of their mental health? If so, how can we do it without manual intervention so that they do not feel uncomfortable to share their problems? The main purpose of this research was to find out a way in which we can detect mental health disorders without manual intervention. Machine learning can be the right solution to this problem. We used social media sites to analyse the mental state of the users. Machine learning models are trained to analyse the mental health of the people. Machine learning algorithms are employed to analyse the mental health disorders based on social media posts. Sentiment analysis can be used to detect the tone of the user from the message. This tone is useful in identifying whether the tweets are positive or negative. The proposed system uses the popular social media platform—Twitter to detect depression in the users. The tweets collected from Twitter users are used to train the machine learning model to detect whether the tweet is positive or negative. We have used logistic regression to train the model. This results in a model that is able to accurately predict whether the Twitter user is depressed or not. This can be used to detect the depression in the users and link to health workers or helplines which help the depressed person cope with their mental illness.KeywordsMachine learningArtificial intelligenceSentiment analysisTwitterNatural language processingMachine learning algorithms