A Web-Based Mental Health Assessment Framework using Natural Language Processing Techniques

Abstract

Mental illnesses like depression, anxiety, stress, and so on have become more widespread, and this has necessitated the availability of assessment tools that are readily available, scalable, and automated. The paper will offer a web-based mental health risk assessment system that utilizes the latest technology of Natural Language Processing (NLP) in real-time to analyze textual information provided by users. The offered system incorporates a hybrid deep learning framework with the relation of DeBERTa, BiLSTM, and XGBoost to promote the contextual comprehension, sequential emotional pattern identification, and effective classification of the performance. First, text input by the users is received with a secure web interface and processed with general NLP preprocessing methods, such as tokenization, lemmatization, and sentiment normalization. To extract deep semantic relationships in the text, DeBERTa is used to extract contextual embeddings. Such embeddings are also trained in the form of a Bidirectional Long Short-Term Memory (BiLSTM) network in order to capture emotional dynamics and linguistic reinforcing relations. Fused feature representation, sentiments, and linguistic indicators are input into an XGBoost classifier to predict mental health in multi-class. There is a weighted risk scoring system used to measure the level of severity and provide tailored feedback. It is experimentally tested on standard mental health text data sets that the proposed hybrid framework is more effective than the traditional machine learning and standalone transformer models in terms of accuracy, precision, recall, and F1-score. The architecture is scalable to the deployment of a web system, which is guaranteed to perform in real-time, secure data, and privacy of users. The suggested framework offers a solid and smart instrument to identify the risk of mental health early and help intervene in time and to promote the development of digital health care.