Depression Detection from Social Media Text Using NLP and Deep Learning Techniques

Depression is a prevalent psychological disorder with potentially serious implications, spreading worldwide. Moreover, that is why research is focused on this subject. Individuals experiencing depression often suffer from anxiety and insecurity in their daily lives, which can impact their relationships and work. In severe cases, this may also lead to various health issues and, tragically, even suicide. We are in the digital age. With the surge in the growth of social media usage, people utilize it as a platform to voice out their emotions, feelings and thoughts. Having machine learning improve and sample data being available regarding depression, there is potential for producing an early depression diagnosis system that would be crucial to reduce the victims number. A machine learning-based model to predict depression is showcased by this paper, based on extracting text data from one of the most common social media outlets: Twitter. We'll use NLP technique through user posts and classify them as indicative or not of depression. We use a bidirectional LSTM model trained on datasets from Twitter to detect depressive tendencies. Our model demonstrates excellent precision in distinguishing between a depressive text and a non depressive text achieving a precision rate of 98% and a recall rate of 99%. and really helps in opening horizons for artificial intelligence to more precisely identify mental health conditions based on an individual's online behavior. This research contributes to the expanding field of depression detection using AI and provides a framework for future mental health analytics research and development.