A Mixed Deep Learning Based Model to Early Detection of Depression

Authors

  • Boumahdi Fatima Université Blida 1, Laboratoire LRDSI, Faculté des Sciences, B.P 270, Route de Soumaa, Blida, Algerie https://orcid.org/0000-0001-6255-9713
  • Madani Amina Université Blida 1, Laboratoire LRDSI, Faculté des Sciences, B.P 270, Route de Soumaa, Blida, Algerie
  • Rezoug Nachida Université Blida 1, Laboratoire LRDSI, Faculté des Sciences, B.P 270, Route de Soumaa, Blida, Algerie
  • Hentabli Hamza Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.13052/jwe1540-9589.19344

Keywords:

Sentiment analysis, early risk detection, deep learning, mental health, depression identification, text classification

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 posts. 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. A total of four classifier models were deployed with the same dataset. 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.

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Author Biographies

Boumahdi Fatima, Université Blida 1, Laboratoire LRDSI, Faculté des Sciences, B.P 270, Route de Soumaa, Blida, Algerie

Boumahdi Fatima obtained the BS and master degree in computer science from Saad Dahlab University, Blida 1, Algeria, in 2006. She got the PhD degree in computer science from the National School of Computer Science (ESI), Algier, Algeria, in 2015. Since 2015, she is assistant professor in Sciences Faculty at Saad Dahleb University, Blida, Algeria. She published numerous publications in the areas of Decision Support Systems, Web information systems, and Service Oriented Architecture. Her current research interests and endeavours mainly go out to natural language processing, Sentiment Analysis, Deep learning and Artificiel Intelligence.

Madani Amina, Université Blida 1, Laboratoire LRDSI, Faculté des Sciences, B.P 270, Route de Soumaa, Blida, Algerie

Madani Amina is a Lecturer at Department of Informatics, Saad Dahleb University – Blida 1, Algeria. She has received her Ph.D at the National School of Computer Science (ESI), Algiers in May 2017. Her interests in research include: Data Mining, Deep Learning, Natural Language Processing, Sentiment Analysis, Trending Topics and Social Networks.

Rezoug Nachida, Université Blida 1, Laboratoire LRDSI, Faculté des Sciences, B.P 270, Route de Soumaa, Blida, Algerie

Rezoug Nachida is a Lecturer at Department of Informatics, Saad Dahleb University – Blida 1, Algeria. She has received her Ph.D at the National School of Computer Science (ESI), Algiers in May 2016. Her main research interests are in data warehousing solutions, data mining and OLAP system, decision support system, and context-aware recommender system. She has co-authored in numerous papers in proceedings of international conferences.

Hentabli Hamza, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

Hentabli Hamza obtained the BS degree in computer science from Saad Dahlab University, Blida 1, Algeria, in 2003. He got the master and PhD degree in computer science from Universiti Teknologi Malaysia, johor (Malaysia), in 2019. His current research interests lie in the fields of Information Retrieval, Cheminformatics, Image Processing, Deep learning and Data Mining.

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Published

2020-07-17

How to Cite

Fatima, B., Amina, M., Nachida, R., & Hamza, H. (2020). A Mixed Deep Learning Based Model to Early Detection of Depression. Journal of Web Engineering, 19(3-4), 429–456. https://doi.org/10.13052/jwe1540-9589.19344

Issue

Section

Semantic Machine Learning, Web Data Integration and Applications