Predicting Stress, Anxiety, and Depression from Social Media Comments: A Holistic Multi-Modal Deep Learning and NLP Framework

In the current digital era, millions of people are affected by increased levels of stress, anxiety, and desperation making mental health a critical worldwide concern. Proactive approaches to early detection of these illnesses are necessary to address this dilemma. This research aims to create a mental health condition prediction model by utilizing deep learning, machine learning, and natural language processing (NLP) cutting edge methods. We use social media data to identify potential red flags for mental health issues early on. To build a strong framework, our suggested approach combines classic machine learning techniques like Random Forest and SVMs with neural networks, such as CNNs, LSTMs, and GRUs. Through an examination of language patterns and behaviors seen in social media content, our models provide insights into current trends in mental health. Experimental results demonstrate high predictive accuracy, highlighting the potential for early intervention. This research addresses the growing need for innovative, data-driven approaches to mental health care, facilitating timely support, personalized treatment, and improved outcomes for individuals and communities.