Sentimental Analysis of Social Media Data for Mental Health Monitoring

The mental health problem known as depression is a multifaceted and widespread condition that presents a substantial obstacle to global health. Early detection and management are critical to lessen its negative impact on people's well-being. With the rise of social media platforms like Twitter, researchers have become interested in mining their textual content for indicators of depression using deep learning techniques. This paper presents a complete methodology for detecting depression using deep learning models trained on an open-source Kaggle dataset. The proposed method employs the Transformers, i.e., BERT, to retrieve embeddings from the text data (tweets). The embeddings derived from BERT are subsequently utilized as input for the customized Deep Neural Network, which consists of five layers and an LSTM model. The experimental results show that the Deep Neural Network outpaces the LSTM model by achieving 100% and 99.13% training and validation accuracies, respectively. The utilization of deep learning methodologies to detect depression through analysis of tweets demonstrates a potentially advantageous and groundbreaking strategy for tackling the difficulties connected with diagnosing and providing assistance to those who may be undergoing depressive episodes.