Mental Health Prediction from Digital Conversations via Transformer-Based NLP Models

The increasing prominence of mental health conditions, particularly normal (non-depressed state), depression, suicidal, anxiety, stress, bipolar, and personality disorder, has rendered the necessity for prompt detection and intervention strategies vital. As internet usage has expanded exponentially, linguistic data and online social networking outlets have transformed into key tools towards detection of mental health issues. This research investigation describes a structure for textual evaluation-based mental health surveillance built around Natural Language Processing (NLP), comparing conventional machine learning methods with a BERT-based transformer model. The research study evaluated users state of mind across numerous groups through an amalgamated collection of posts in text that dealt with their mental wellness. The BERT approach demonstrated greater outcomes, in line with its effectiveness assessment conducted using [Accuracy: 0.98, Precision: 0.96, Recall: 0.92, and F1-Score: 0.94], appreciably more effective than standard algorithms. The above findings illustrate how transformer-based topologies are capable of being utilized for collecting contextual interpretations of language, thus raising the preciseness and dependability of digital mental wellness evaluation solutions. The proposed framework ought to be used as an initial foundation for digital approaches for continuous mental health surveillance, affording investigators and professionals in mental health with reliable, versatile, and secure observations. Context representation learning has been linked with the BERT model's excellent efficiency; however, characteristics of the dataset along with possible class imbalance have been identified as limitations.