Identifying Depression in Tweets Using CNN-deep and BILSTM with Attention Model
Fatima Boumahdi, Amina Madani, Ibrahim Cheurfa, Hamza Hentabli
International Journal of Web Applications, Vol. 12, No. 2, 2020.
DOI: https://doi.org/10.6025/ijwa/2020/12/2/47-61
Link: http://www.dline.info/ijwa/fulltext/v12n2/ijwav12n2_2.pdf	
Abstract:
Mental health is considered as one of todayâ€™s worldâ€™s most prominent plagues. Therefore, our work aims to use the
potential of social media platforms to solve one of mental healthâ€™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 individualâ€™s tweets. Our main contributions lie in the three following points: (1) We trained our own word embeddings using
a depression-dedicated dataset. (2) We combined a Convolutional Neural Networks model with the Message-level Sentiment
Analysis model in order to improve the feature extraction process and enhance the modelâ€™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.