Decoding Mental Health Through Social Media with Dual-Stream AI

Mental health disorders have now become a huge concern in the world health industry, offering a unique opportunity for early detection and intervention through social media. This paper introduces an NLP-enhanced dual-stream CNN-LSTM model to detect multiple mental health disorders from social media content. Such advanced natural language processing techniques combined with a hybrid deep learning architecture improve accuracy as well as robustness in mental health classification. There are two streams moving in parallel: the CNN to capture local features from text and the LSTM to model long-term dependencies, both of these augmented with NLP-based feature extraction. The dual-stream architecture captures the short-term linguistic patterns as well as the long-term contextual information. Further NLP extensions are in the area of topic modeling and sentiment analysis, which add to the model's mental health indicator understanding. This work introduces a viable paradigm for large-scale monitoring of social media for mental health through timely pertinent implications toward early intervention and public health initiatives.