Intelligent Mental Health Screening Through Social Media Text Analysis

Abstract

Mental health conditions having a well- known Uprising enterprises in public in all over the world. Millions of people had not been treated yet, and they remain undiagnosed [1]. Automated screening first evolved from early linguistic studies that were not limited to basic keyword matching. A good example would be Coppersmith et al. [10]; they showed that people who were users diagnosed with PTSD(Post-Traumatic Stress Disorder) or clinical depression had measurable changes in the linguistic expression on Twitter. These digital biomarkers are reactive. For example, Reece et al. [11] showed that machine learning models can predict the occurrence of mental illness, even before the patient receives a clinical diagnosis. This ability to predict the occurrence of mental illness has a direct relationship with the early threat discovery (ERD) framework this paper proposes. There are numerous social media platforms like Facebook, Twitter, Reddit these platforms serve as critical, informal depositories of verbal and emotional patterns, further, as a result, enabling the use of Computational Analysis for early threat discovery( ERD) [1]. This exploration paper draft had an artificial intelligence model train to dissect the Digital vestiges, by using advanced natural language processing ways. Like BSLTM Networks. And the finetuned BERT models [1]. This core working falsehoods in the necessary combination of resolvable AI. Particularly LIME and SHAP, To break the ongoing abstract issue with the high delicacy models presently right now in use [2]. Clear ethical guidelines like discerned sequestration and Adaptive synthetic slice, are a element of the frame In order to reduce data gaps, to make sure the final system should n’t only be robust it should also be fair, suitable, having translucency in order to achieve ethical development as an important tool For professionals in internal health.