Detecting Depression from Tweets with Neural Language Processing

Abstract As social media becomes a major part of everyday life, analyzing language on is becoming potentially fruitful approach to discover depression patients. At time when starting be taken seriously, sentiment analysis through should studied further so that people can treated in the early stages depression. With intention detect behavior studying media, we built classification model for detection by training Tweets dataset from Shen et al 2017. After optimizing hyper-parameters including learning rate and embedding dimension, achieves test accuracy 98.94% an F1 score 99.04% which higher than best performance 85% F1-measure achieved Shen’s work. These results show method effective used wider range unlabeled data locate potential depressed users.