Stress Detection from Text Using Hybrid NLP-ML with SHAP Explainability

Mental illnesses, especially stress and anxiety represent a significant challenge to the health of individuals and social life in general on an international scale. The standard clinical methods of assessing stress such as self-report, professional interview, and laboratory tests are all constrained by the nature of the data to be assessed. The fast whether many social media sites, like Reddit, Twitter, and Facebook created an incredible quantity of user-created text that serves as an excellent source of naturally occurring, emotion-filled language and discourse on stress. In this paper, a new hybrid natural language processing (NLP) and machine learning (ML) system will be proposed in order to identify stress in social media text on a real-time basis. In the proposed system, three complementary sets of features are combined: lexical features with terms frequency-inverse document frequency (TF-IDF) vectorization features and unigram and bigram analysis features; emotional features by using VADER (Valence Aware Dictionary and Sentiment Reasoner) sentiment analysis features and domain-specific stress vocabulary; structural features that capture linguistic patterns at the sentence level. These sets are combined to form a single high-dimension vector and trained with a Logistic Regression (LR) which is chosen by strict empirical analyses with a Support Vector Machine (SVM) on the basis of F1-score. Post hoc interpretability SHAP (SHapley Additive explements) is added to reveal the features that are most significant to each prediction. The experiments with Reddit Stress (Dreaddit) databank show that the accuracy is around 87 percent and F1-score is 0.87. The system is enabled to operate in real time using Flask with a REST API and a React.js frontend. An edge case management in the form of a rule-based mechanism of overriding manipulates emotionally colored