research-article

An attention-based hybrid architecture with explainability for depressive social media text detection in Bangla

Authors:

Tapotosh Ghosh,

Md. Hasan Al Banna,

Md. Jaber Al Nahian,

Mohammed Nasir Uddin,

M. Shamim Kaiser,

Mufti MahmudAuthors Info & Claims

Volume 213, Issue PC

https://doi.org/10.1016/j.eswa.2022.119007

Published: 01 March 2023 Publication History

Abstract

Mental health has become a major concern in recent years. Social media have been increasingly used as platforms to gain insight into a person’s mental health condition by analysing the posts and comments, which are textual in nature. By analysing these texts, depressive posts can be detected. To facilitate this process, this work presents an attention-based bidirectional Long Short-Term Memory (LSTM)- Convolutional Neural Network (CNN) based model to detect depressive Bangla social media texts, which is lighter and more robust than the conventional models and provides better performance. A dataset containing such Bangla texts was also developed in this work to mitigate the scarcity. Different preprocessing stages were followed, and three embeddings were used in this task. Thanks to the attention mechanism, the proposed model achieved an accuracy of 94.3% with 92.63% of sensitivity and 95.12% of specificity. When tested on other languages, such as English, the proposed model performed remarkably. The robustness and explainability of the proposed model were also discussed in this paper. Additionally, when compared with classical machine learning models, ensemble approaches, transformers, other similar models, and existing architectures, the proposed model outperformed them.

Highlights

•

The proposed method detects depression from Bangla social media texts.

•

The method is based on the bidirectional LSTM and CNN.

•

Attention mechanism was used to improve the performance of the bidirectional LSTM.

•

The proposed method is explainable and outperforms existing methods.

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Cited By

Geethanjali RValarmathi A(2024)A Deep Learning based Hybrid Model for Maternal Health Risk Detection and Multifaceted Emotion Analysis in Social NetworksInternational Journal of Applied Mathematics and Computer Science10.61822/amcs-2024-003834:4(565-577)Online publication date: 25-Dec-2024
https://dl.acm.org/doi/10.61822/amcs-2024-0038
Gan LGuo YYang T(2024)Machine Learning for Depression Detection on Web and Social MediaInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.34212620:1(1-28)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.4018/IJSWIS.342126
Garg M(2024)Towards Mental Health Analysis in Social Media for Low-resourced LanguagesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/363876123:3(1-22)Online publication date: 9-Mar-2024
https://dl.acm.org/doi/10.1145/3638761
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An attention-based hybrid architecture with explainability for depressive social media text detection in Bangla

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 213, Issue PC

Mar 2023

1402 pages

ISSN:0957-4174

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Pergamon Press, Inc.

United States

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Published: 01 March 2023

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Cited By

Geethanjali RValarmathi A(2024)A Deep Learning based Hybrid Model for Maternal Health Risk Detection and Multifaceted Emotion Analysis in Social NetworksInternational Journal of Applied Mathematics and Computer Science10.61822/amcs-2024-003834:4(565-577)Online publication date: 25-Dec-2024
https://dl.acm.org/doi/10.61822/amcs-2024-0038
Gan LGuo YYang T(2024)Machine Learning for Depression Detection on Web and Social MediaInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.34212620:1(1-28)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.4018/IJSWIS.342126
Garg M(2024)Towards Mental Health Analysis in Social Media for Low-resourced LanguagesACM Transactions on Asian and Low-Resource Language Information Processing10.1145/363876123:3(1-22)Online publication date: 9-Mar-2024
https://dl.acm.org/doi/10.1145/3638761
Yang KYi JChen AJin Z(2024)Buffer-textEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107774130:COnline publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.107774
Gorai JShaw D(2024)A BERT-encoded ensembled CNN model for suicide risk identification in social media postsNeural Computing and Applications10.1007/s00521-024-09642-w36:18(10955-10970)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s00521-024-09642-w
Le-Hinh NVo-Hoang HTran TCao X(2023)Improving Depression Classification in Social Media Text with Transformer EnsemblesProceedings of the 12th International Symposium on Information and Communication Technology10.1145/3628797.3628977(554-561)Online publication date: 7-Dec-2023
https://dl.acm.org/doi/10.1145/3628797.3628977

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