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Bangla Depressive Social Media Text Detection Using Hybrid Deep Learning Approach

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Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 348))

Abstract

Depression is considered as one of the major reasons of suicide which also affects personal and professional life of an individual. As people nowadays prefer to use social media, analysis of the contents generated by a person can lead us to get an insight of his mental health. For this, automated systems are essential due to huge explosion of social-media-generated data and privacy issue. In this paper, a deep learning approach based on Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Word2Vec embedding has been proposed to recognize depressive Bangla social media texts, which obtained 92.25% accuracy, 94.46% sensitivity, and 91.15% specificity. A dataset of 4000 Bangla social media posts was also created. The proposed approach was found better performing than LSTM, GRU, and classical machine learning models that proved its effectiveness in Bangla depressive text detection.

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Acknowledgements

This research received funding from the ICT division of the Government of the People’s Republic of Bangladesh for 2020-21 financial year (tracking no: 20FS13595).

Author information

Authors and Affiliations

  1. Department of ICT, Bangladesh University of Professionals, Dhaka, Bangladesh

    Tapotosh Ghosh

  2. Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh

    M. Shamim Kaiser

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  1. Tapotosh Ghosh
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  2. M. Shamim Kaiser
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Editor information

Editors and Affiliations

  1. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  2. Amity University Rajasthan, Jaipur, India

    Kanad Ray

  3. Surface Characterization Group, National Institute for Materials Science, Tsukuba, Japan

    Anirban Bandyopadhyay

  4. Faculty of Applied Sciences and Technology, Tun Hussein Onn University of Malaysia, Parit Raja, Johor, Malaysia

    Kavikumar Jacob

  5. Faculty of Applied Sciences and Technology (FAST), Universiti Tun Hussein Onn Malaysia, Johor, Malaysia

    Kek Sie Long

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Ghosh, T., Kaiser, M.S. (2022). Bangla Depressive Social Media Text Detection Using Hybrid Deep Learning Approach. In: Kaiser, M.S., Ray, K., Bandyopadhyay, A., Jacob, K., Long, K.S. (eds) Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 348. Springer, Singapore. https://doi.org/10.1007/978-981-16-7597-3_9

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