Harnessing social media sentiments for accurate mental health diagnosis across multiple conditions

Social media growth has given people a space to expose their feelings and mental health statuses. The present study explores the application of sentiment analysis in diagnosing and classifying psychological illnesses using social media posts. We identify mental health statuses such as Normal, Depression, Suicidal, Anxiety, Stress, Bipolar, and Personality Disorder by analyzing a large multi-class dataset collected from platforms like Reddit and Twitter. Data extraction was followed by pre- processing to mitigate noise and finally applying sentiment analysis algorithms in order to detect patterns. The information will be useful for creating smart tools that can help individuals with their mental problems while also tracking trends that could lead to early interventions.