Citation

Almars AM. Computers, Materials and Continua 2022; 71(2): 3091-3106.

Copyright

(Copyright © 2022)

DOI

10.32604/cmc.2022.022609

PMID

unavailable

Abstract

Depression is a common mental health issue that affects a large percentage of people all around the world. Usually, people who suffer from this mood disorder have issues such as low concentration, dementia, mood swings, and even suicide. A social media platform like Twitter allows people to communicate as well as share photos and videos that reflect their moods. Therefore, the analysis of socialmedia content provides insight into individual moods, including depression. Several studies have been conducted on depression detection in English and less in Arabic. The detection of depression from Arabic social media lags behind due the complexity of Arabic language and the lack of resources and techniques available. In this study, we performed a depression analysis on Arabic social media content to understand the feelings of the users. A bidirectional long short-term memory (Bi-LSTM) with an attention mechanism is presented to learn important hidden features for depression detection successfully. The proposed deep learning model combines an attention mechanism with a Bi-LSTM to simultaneously focus on discriminative features and learn significant word weights that contribute highly to depression detection. In order to evaluate our model, we collected a Twitter dataset of approximately 6000 tweets. The data labelling was done by manually classifying tweets as depressed or not depressed. Experimental results showed that the proposed model outperformed state-of-the-art machine learning models in detecting depression. The attention-based Bi- LSTM model achieved 0.83% accuracy on the depression detection task. © 2022 Tech Science Press. All rights reserved.

Language: en

Keywords

Mental health; Mood disorders; Learn+; Social networking (online); Social media; Deep learning; Depression detection; Feature extraction; Bidirectional long short-term memory; Long short-term memory; Health issues; Attention mechanisms; Attention model; Bi-LSTM