Depression Detection from Text Classification Using Neural Network

Depression has become a common mental health problem in our society. In fact, according to WHO, depression is a mental health that occurs to 10% of the global population every year. The risk of failure to handle depression is the risk of suicide. People often use social media to express their feelings. Artificial Intelligence, one technological improvement, was created to solve cognitive problems. One of the capabilities to interpret and predict results from text is called Natural Language Processing (NLP). In this research, the researchers propose the idea of forming a model that can classify whether the text entered is classified as depression or non-depression. This research focuses on using deep learning as a model, ratio comparisons in the dataset, and using a bidirectional layer for the models. After conducting the study, the best model is the Bi-GRU model with the dataset ratio of 80:10:10, where this model achieves an accuracy of 0.9955 and a loss accuracy of 0.0154. Hopefully, this model will continue this research until it impacts the surrounding environment.