Abstract:
Disorders such as depression, anxiety, and stress are rapidly becoming one of the world’s most serious problems, affecting society and the economy on a substantial level. Shifts to automatic processes for intervention propose new promising avenues, such as using Neural Language Processing (NLP) to bypass the biases and discrimination inherent in traditional methods. This study investigates the detection of mental health disorders by analyzing user- generated content on platforms such as Twitter, Facebook, and Reddit utilizing sentiment analysis within the framework of natural language processing (NLP). The primary focus is on how effective NLP techniques align text data analytics with mental distress, particularly concerning predicting anxiety and depression. Moreover, the study examines the sentiment-mental health relationship to evaluate the developed early detection models while facing challenges of responsibly integrating these technologies. There is promise in sentiment analysis, but problems such
as cultural context, indirect sentiment expression, and privacy issues are still key obstacles. The results demonstrate the transformative potential of NLP technologies for predicting mental health issues in offering proactive support, but further research is necessary to refine the models so they can be implemented accurately and ethically.
