Research Area:  Machine Learning
Mental health is considered as one of todays worlds most prominent plagues. Therefore, our work aims to use the potential of social media platforms to solve one of mental healths biggest issues, which is depression identification. We propose a new deep learning model that we train on a depression-dedicated dateset in order to detect such mental illness from an individuals posts. Our main contributions lie in the three following points: (1) We trained our own word embeddings using a depression-dedicated data set. (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 models 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. A total of four classifier models were deployed with the same data set. Those implementing CNN-BiLSTM with Attention model attained greater overall Accuracy, Recall, Precision and F1 macro scores of 0.97, 0.95, 0.84 and 0.92 on the final assessment test set, respectively.
Author(s) Name:  Boumahdi Fatima , Madani Amina , Rezoug Nachida , Hentabli Hamza
Journal name:  Semantic Machine Learning, Web Data Integration and Applications
Publisher name:  Riverpublishers
Volume Information:  Vol 19 Iss 3-4
Paper Link:   https://journals.riverpublishers.com/index.php/JWE/article/view/703