@article{2992,
  author = {Fatima Boumahdi, Amina Madani, Ibrahim Cheurfa, Hamza Hentabli},
  title = {Identifying Depression in Tweets Using CNN-deep and BILSTM with Attention Model},
  journal = {International Journal of Web Applications},
  year = {2020},
  volume = {12},
  number = {2},
  doi = {https://doi.org/10.6025/ijwa/2020/12/2/47-61},
  url = {http://www.dline.info/ijwa/fulltext/v12n2/ijwav12n2_2.pdf	},
  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 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 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.},
}