AI based Detection of Depression and Anxiety through Social Media Analysis

The cases of depression and anxiety have been rising drastically since COVID-19 pandemic. There are quite a number of individuals who are left to be undiagnosed despite the progressive advancement of diagnostics because of the stigma, lack of knowledge and insufficient care amenities. A psychological insight is achieved with subtlety through the use of social media since the pages in the social media are typically full of personal feelings and problems posted there. The proposed research problem is how to find the symptoms of depression and anxiety at an early stage in social media content with the assistance of artificial intelligence (AI) and machine learning (ML), in particular, sentiment analysis and natural language processing (NLP). It is used to analyze the linguistic, emotional and behavioral patterns and contrast the findings with the deep learning models (e.g., LSTM, CNN, BERT) with the traditional machine learning models (e.g., SVM, Random Forest, Logistic Regression). Its results will have to show that the sentiment analysis using AI could be applied as a scalable, real-time, and non-invasive sentiment analysis tool to complement the conventional diagnostics. The significance of privacy, transparency and ethics are also noticed by the paper and presented as an efficient mental health provider and digital health project.