Semantic Coherence and NLP: Redesigning post-COVID Mental Health Diagnostics with CNNs and LSTMs

The COVID-19 pandemic has intensified the need for innovative, scalable diagnostic tools for mental health, given the surge in related disorders globally. This study presents a novel neural symbolic approach leveraging natural language processing (NLP) to analyze semantic coherence in text data, aimed at predicting mental health outcomes. Integrating convolutional neural networks (CNNs) with long short-term memory networks (LSTM) and an attention mechanism, this model excels in extracting and emphasizing critical linguistic features from vast datasets of online textual communications. Our evaluations show that the model achieves an accuracy of 92.4%, with precision, recall, and an F1-score significantly superior to traditional LSTM models. The ROC-AUC score of 0.92 highlights its effectiveness at distinguishing various mental health states, while the attention mechanism enhances the model’s interpretability, shedding light on key text features indicative of mental distress. This research underscores the potential of AI in enhancing mental health diagnostics in the context of current events, proposing a powerful tool for early detection and intervention.