The increasing prevalence of mental health discussions on social media platforms such as Reddit and Twitter has created a significant opportunity for computational systems to support early detection of psychological distress through sentiment mining. This study proposes a hybrid intelligent model that integrates classical machine learning techniques with pretrained deep learning architectures to analyze mental health-oriented sentiments across heterogeneous social media sources. The motivation stems from the rising burden of anxiety-related and depressive disorders globally, including conditions such as agoraphobia, panic disorder, and generalized anxiety disorder, which require early identification for effective intervention (Balaram & Marwaha, 2023; Generalized Anxiety Disorder, n.d.).

The proposed framework leverages feature engineering approaches alongside transformer-based contextual embeddings to enhance classification accuracy and domain adaptability. A comparative synthesis of prior research highlights the strengths and limitations of machine learning models, psycholinguistic approaches, and transformer-based architectures in mental health detection tasks. Experimental design considerations include cross-platform dataset alignment, sentiment normalization, and hybrid fusion strategies combining FastText, BERT-like embeddings, and supervised classifiers.

Findings from the literature-driven methodological synthesis suggest that hybrid architectures outperform standalone models in capturing both lexical and contextual emotional cues. However, challenges remain in domain shift, annotation bias, and ethical considerations in mental health prediction systems. The study concludes that hybrid intelligent systems represent a scalable and adaptable direction for mental health sentiment mining across social platforms.