Exploring LSTM RNNs for Depression Detection: A Deep Learning Perspective

One common mental illness that affects individuals's daily life and general state of affairs is depression. Early diagnosis of depression is important since it can lead to timely intervention, better treatment outcomes, and less long-term psychological and physical health impacts, therefore improving good health and well-being. This work attempts to classify sadness using a dataset including 7,732 entries collected from web scraping SubReddit and cleaned using many NLP techniques. With ‘1’ denoting a depressed person and ‘0’ denoting a non-depressed individual, the data—exclusively in English—is separated into two categories: ‘clean_text’ and ‘is_depression’. Long short-term memory (LSTM) and recurrent neural network (RNN) deep learning approaches were used to group the data. The method called for 80% of the data for training and 20% for validation. The algorithm underscored its probable contribution in early depression detection and showed outstanding 96% accuracy in recognizing depressive symptoms from text data. This study increases quality education and helps to reduce inequality by way of its innovative approach, utilization of industry, innovation, and infrastructure in its technology implementation, and expansion of mental health support accessibility.