Mental Health Disorder Predication Using Machine Learning for Online Social Media

Mental diseases are frequently undiagnosed, resulting in a serious problem that affects all aspects of society. Using popular social networking websites, recurring psychological patterns can be identified. These patterns might be used to communicate one's feelings and thoughts in everyday life. Our study uses sentiment analysis techniques to detect persons who may be suffering from mental illnesses and categories them based on the intensity of language usage and distinct behavioral elements. To address the growing problem of mental illnesses, we propose a unique data extraction approach that focuses on the research of sentiment analysis, through which mental illnesses such as depression and anxiety disorders may be recognized. Our technique may be used not just to identify people, but also to track their improvement over time by tracking them on Twitter. This might someday allow medical professionals and public health experts to monitor the symptoms and progression of mental illnesses among social media users.