Identifying Depression in Tweets using Deep Learning.

Abstract

Mental health is considered as one of today's world's most prominent plagues. Therefore, our work aims to use the potential of social media platforms to solve one of mental health's biggest issues, which is depression identification. We propose a new deep learning model that we train on a depression-dedicated dataset in order to detect such mental illness from an individual's tweets. Our main contributions with this work lie in the three following points: (1) We trained our own word embeddings using a depression-dedicated dataset. (2) We combined a CNN model with the MSA model in order to improve the feature extraction process and enhance the model's performance. (3) We analyzed through different experiments the performance of three deep learning models in order to provide more perspectives and insights for depression researches. Our model achieved a 99% accuracy, outperforming any statistical or deep learning models found in literature currently. Keywords: Sentiment Analysis, Deep Learning, Mental Health, Text Classification.