aaronstone1699/Depression-detection

Depression is one of the most common mental disorders with millions of people suffering from it.It has been found to have an impact on the texts written by the affected masses.In this study our main aim was to utilise tweets to predict the possibility of a user at-risk of depression through the use of Natural Language Processing(NLP) tools and deep learning algorithms.LSTM has been used as a baseline model that resulted in an accuracy of 95.12% and an F1 score of 0.9436. We implemented a hybrid Bi-LSTM + CNN model which we trained on learned embeddings from the tweet dataset was able to improve upon previous works and produce precision and recall of 0.9943 and 0.9988 respectively,giving an F1 score of 0.9971.

PythonAGPL-3.0