Depression detection based on twitter using NLP and sentiment analysis

Abstract

Depression is the most common illness, serious disease, and underestimated by human beings. The serious depression will affect the emotion, physical condition, or cause suicide. Depression can be detected by reading their social media post. This research aims to develop a system that used to analyze the user depression status based on their social media post. This research will implement Recurrent Neural Network (RNN) model and Convolutional Neural Network (CNN) model in order to get the most accurate parameter for building the model and compare the accuracy of the prediction. The RNN (LSTM) 7-layer model are the most accuracy, precision, recall, F1 score of and less loss compare with other three model. The accuracy is 80.99%, F1 80.16%, and loss 45.0%. The RNN (LSTM) had selected 7-layer as the model in development the google chrome extension to perform the tweet sentiment analysis. The system will notify the user about their depression status; suggested to ask treatment with phycologist.