Philip Thekkekara, JoelYongchareon, SiraLiesaputra, Veronica2024-04-232024-04-232024-03-22Expert Systems with Applications, ISSN: 0957-4174 (Print), Elsevier BV, 249, 123834-123834. doi: 10.1016/j.eswa.2024.1238340957-4174http://hdl.handle.net/10292/17457Depression has long been described as a common mental health disorder and a disease with a set of diagnostic criteria that influences the affected individuals' feelings and behavior. The prevalence of Internet use has augmented people’s openness to share their experiences and struggles, including mental health disorders on social media thus researchers have tried developing classification models for depression detection using various machine learning and deep learning techniques. In this research, we propose a deep learning architecture with an attention mechanism on CNN-BiLSTM (CBA) and provide a comparative analysis to benchmark well-known deep learning models using the public dataset namely CLEF2017. We found that along with F1 score, precision and recall it is also vital to consider the Area under the curve - Receiver operating characteristic curve (AUC-ROC) and Mathews Correlation Coefficient (MCC) metrics for evaluating depression classification models since the MCC considers all the four values of a confusion matrix. Based on our experiments, the CBA model outperforms the existing state of the art model with an overall accuracy of 96.71% and scores of 0.85 and 0.77 for AUC-ROC and MCC, respectively.© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by/nc/4.0/).http://creativecommons.org/licenses/by-nc/4.0/46 Information and Computing Sciences4608 Human-Centred Computing4611 Machine LearningBehavioral and Social ScienceMental HealthDepressionMental health3 Good Health and Well Being01 Mathematical Sciences08 Information and Computing Sciences09 EngineeringArtificial Intelligence & Image ProcessingJournal ArticleOpenAccess10.1016/j.eswa.2024.123834