Linguistic Signals of Mental Health in Social Media Using Deep Learning

Abstract

Mental health conditions such as depression, anxiety, psychological anguish, and suicide thoughts have grown to be major global public health issues. People are increasingly expressing their feelings, thoughts, and psychological problems through online textual content due to the quick development of social media platforms, which presents important chances for early mental health assessment. However, traditional machine learning techniques frequently fall short in capturing the temporal evolution of users' emotional states as well as the semantic context, which leads to limited robustness and decreased prediction accuracy. By combining Sentence Transformer-based semantic embeddings, Long Short-Term Memory (LSTM) networks, and ensemble learning techniques, this article suggests an intelligent deep learning framework for identifying linguistic indications of mental health from social media text. To create clean textual representations, social media posts first go through text preprocessing, which includes tokenization, normalization, noise removal, and stop-word elimination. Then, rich contextual semantic embeddings are extracted using Sentence Transformer models, and temporal affective patterns across successive user posts are captured by LSTM networks. To increase prediction robustness and reduce misclassification, an ensemble classifier consisting of Random Forest, Gradient Boosting, and Support Vector Machine (SVM) algorithms is employed to fuse and analyze the extracted semantic and temporal features. The suggested approach outperforms traditional deep learning models in terms of accuracy, precision, recall, and F1-score in experimental evaluation on benchmark social media datasets, allowing for the accurate detection of psychological distress and suicidal ideation. The suggested system offers a scalable, comprehensible, and effective solution for real-time mental health risk screening, assisting researchers, medical professionals, and digital well-being platforms in providing prompt interventions while encouraging the ethical and responsible application of artificial intelligence in mental healthcare.