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

Syed Rakeen, Thejas Prasad A H *, Sumanth Skanda Bharadwaj and Sujay Srinivas

Department of MCA, Surana College (Autonomous) Bangalore, Karnataka, India.
 
Research Article
International Journal of Science and Research Archive, 2024, 13(01), 881–889.
Article DOI: 10.30574/ijsra.2024.13.1.1704
Publication history: 
Received on 03 August 2024; revised on 10 September 2024; accepted on 13 September 2024
 
Abstract: 
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.
 
Keywords: 
Sentimental Analysis; Social Media; Mental Health; Machine Learning; Depression
 
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