ARCHIVES
Original Article
Multi-Class Mental Health Detection With LSTM and BilLSTM Models
Dondapati Sasi Prasanna1
Suneel Kumar Duvvuri2
1 Student, M.Sc Computer Science, Government College (Autonomous), Rajahmundry, Andhra Pradesh, India. 2 Assistant Professor, Department of Computer Science, Government College (Autonomous), Rajahmundry, Andhra Pradesh, India.
Published Online: March-April 2026
Pages: 152-164
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260702020
References
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2022, doi: 10.1002/wps.21018.
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2026, doi: 10.1177/00207640261424406.
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e18472, Mar. 2020, doi: 10.2196/18472.
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vol. 8, p. 196, Mar. 2025, doi: 10.1038/s41746-025-01602-5.
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2018, pp. 13–24. doi: 10.18653/v1/W18-0602.
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10.48550/arXiv.2106.00610.
12. Y. Hussain, M. A. Zaheer, A. M. Khan, and A. S. Malik, “Depression detection using deep learning and large language models from
multimodalities,” Front. Digit. Health, vol. Volume 8-2026, 2026, doi: 10.3389/fdgth.2026.1759857.
13. A. Mccallum and K. Nigam, “A Comparison of Event Models for Naive Bayes Text Classification,” Work Learn Text Categ, vol. 752,
Mar. 2001.
14. C. Cortes and V. Vapnik, “Support-vector networks,” Chem. Biol. Drug Des., vol. 297, pp. 273–297, Mar. 2009, doi:
10.1007/%2FBF00994018.
15. L. Breiman, “Random Forests,” Mach. Learn., vol. 45, pp. 5–32, Mar. 2001, doi: 10.1023/A:1010950718922.
16. T. J. Devi and A. Gopi, “The Evaluation of Deep Learning Models for Detecting Mental Disorders Based on Text Summarization in
Societal Analysis,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 3, pp. 1620–1628, 2024.
17. Q. Saeed and Y. Cha, “Multi-modal deep-attention-BiLSTM based early detection of mental health issues using social media posts,”
Sci. Rep., vol. 15, Mar. 2025, doi: 10.1038/s41598-025-19141-0.
18. Y. Hussain, M. A. Zaheer, A. M. Khan, and A. S. Malik, “Depression detection using deep learning and large language models from
multimodalities,” Front. Digit. Health, vol. Volume 8-2026, 2026, doi: 10.3389/fdgth.2026.1759857.
19. J. Thekkekara and S. Yongchareon, “An Attention-Based BERT–CNN–BiLSTM Model for Depression Detection from Emojis in
Social Media Text,” Big Data and Cognitive Computing, vol. 9, p. 310, Mar. 2025, doi: 10.3390/bdcc9120310.
20. M. Nadeem, M. A. Abbasi, F. Ali, K. Mughal, S. Azhar, and A. Saeed, “A Deep Learning Approach to Early Detection of Depression
Through Social Media Text Analysis,” International Journal of Social Sciences Bulletin, vol. 1, no. 1, pp. 653–675, 2025, doi:
10.5281/zenodo.17435175.
21. S. C H, “A Deep Learning Framework for Depression and Major Depressive Disorder Detection from Social Media Text UsingMentalBERT and Multilayer Perceptron,” Mar. 2025, doi: 10.22266/ijies2025.0930.16.
22. A. Amanat et al., “Deep Learning for Depression Detection from Textual Data,” Electronics (Basel)., vol. 11, Mar. 2022, doi:
10.3390/electronics11050676.
23. Md. Z. Uddin, K. Dysthe, A. Følstad, and P. Brandtzaeg, “Deep learning for prediction of depressive symptoms in a large textual
dataset,” Neural Comput. Appl., vol. 34, pp. 1–24, Jan. 2022, doi: 10.1007/s00521-021-06426-4.
24. Y. Shen, H. Yang, and L. Lin, “Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based
Model,” Mar. 2022. doi: 10.48550/arXiv.2202.08210.
25. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, pp. 1735–1780, Mar. 1997, doi:
10.1162/neco.1997.9.8.1735.
26. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, pp. 1735–1780, Mar. 1997, doi:
10.1162/neco.1997.9.8.1735.
27. S. Jain, D. Rastogi, P. Nagrath, G. Stoian, D. Dănciulescu, and J. D, “Depression Detection Model Based on Facial, Verbal Features,
and Motion Activity Using Deep Learning,” Traitement du Signal, vol. 42, pp. 2499–2512, Mar. 2025, doi: 10.18280/ts.420505.
28. G. Lam, H. Dongyan, and W. Lin, “CONTEXT-AWARE DEEP LEARNING FOR MULTI-MODAL DEPRESSION DETECTION.”
29. B. Diep, M. Stanojevic, and J. Novikova, “Multi-modal deep learning system for depression and anxiety detection,” Mar. 2022. doi:
10.48550/arXiv.2212.14490.
30. P. Zhou et al., “Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification,” Mar. 2016, pp. 207–
212. doi: 10.18653/v1/P16-2034.
31. T. Mikolov, K. Chen, G. s Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space,” Proceedings of
Workshop at ICLR, vol. 2013, Mar. 2013.
32. Y. Hussain, M. A. Zaheer, A. M. Khan, and A. S. Malik, “Depression detection using deep learning and large language models from
multimodalities,” Front. Digit. Health, vol. Volume 8-2026, 2026, doi: 10.3389/fdgth.2026.1759857.
33. L. he et al., “Deep Learning for Depression Recognition with Audiovisual Cues: A Review,” Mar. 2021. doi:
10.48550/arXiv.2106.00610.
34. F. Y. Prity, T. C. Munira, S. Ahmed Shayeed, and M. J. U. Chowdhury, “MentalDistress: A multi-class social media text dataset for
mental health–related emotion detection,” 2026, Mendeley Data. doi: 10.17632/b42wr437hg.1.
35. S. Bird, “NLTK: The natural language toolkit,” Mar. 2006. doi: 10.3115/1225403.1225421.
36. T. Mikolov, K. Chen, G. s Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space,” Proceedings of
Workshop at ICLR, vol. 2013, Mar. 2013.
37. D. P. Patinavalasa and D. Suneel Kumar, “Scalable Email Spam Detection Using BiLSTM with Large-Scale Hybrid Datasets,”
International Journal of Recent Trends in Multidisciplinary Research, p. 96, Mar. 2026, doi: 10.59256/ijrtmr.20260602016.
38. M. Abadi et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” Mar. 2016, doi:
10.48550/arXiv.1603.04467.
39. A. Zhang, Z. Lipton, M. Li, and A. Smola, “Dive into Deep Learning,” Mar. 2021. doi: 10.48550/arXiv.2106.11342.
40. F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, Mar. 2012.
41. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language
Understanding,” Mar. 2018. doi: 10.48550/arXiv.1810.04805.
42. A. Vaswani et al., “Attention Is All You Need,” Mar. 2017, doi: 10.48550/arXiv.1706.03762.
Switzerland. [Online]. Available: https://apps.who.int/iris/handle/10665/345301
2. “Mental health summit Voices of people with lived experience in the WHO South-East Asia Region,” 2024. [Online]. Available:
http://apps.who.int/bookorders.
3. M. Freeman, “The World Mental Health Report: transforming mental health for all,” World Psychiatry, vol. 21, pp. 391–392, Mar.
2022, doi: 10.1002/wps.21018.
4. M. Farahzadi, “Depression; Let’s talk,” J. Community Health Res., Mar. 2017.
5. C. Lomas, “Global Mental Health Workforce Composition and National Depression Prevalence: Cross-National Evidence From the
WHO Mental Health Atlas 2020 and the Global Burden of Disease Study 2019,” Int. J. Soc. Psychiatry, p. 207640261424406, Mar.
2026, doi: 10.1177/00207640261424406.
6. J. C. Eichstaedt et al., “Facebook language predicts depression in medical records,” Proceedings of the National Academy of Sciences,
vol. 115, no. 44, pp. 11203–11208, 2018, doi: 10.1073/pnas.1802331115.
7. B. Rudd and R. Beidas, “Digital Mental Health: The Answer to the Global Mental Health Crisis?,” JMIR Ment. Health, vol. 7, p.
e18472, Mar. 2020, doi: 10.2196/18472.
8. Z. Yingbo et al., “The comprehensive clinical benefits of digital phenotyping: from broad adoption to full impact,” NPJ Digit. Med.,
vol. 8, p. 196, Mar. 2025, doi: 10.1038/s41746-025-01602-5.
9. M. Morales, S. Scherer, and R. Levitan, “A Linguistically-Informed Fusion Approach for Multimodal Depression Detection,” Mar.
2018, pp. 13–24. doi: 10.18653/v1/W18-0602.
10. D. Jurafsky and J. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational
Linguistics, and Speech Recognition, vol. 2. 2008.
11. L. he et al., “Deep Learning for Depression Recognition with Audiovisual Cues: A Review,” Mar. 2021. doi:
10.48550/arXiv.2106.00610.
12. Y. Hussain, M. A. Zaheer, A. M. Khan, and A. S. Malik, “Depression detection using deep learning and large language models from
multimodalities,” Front. Digit. Health, vol. Volume 8-2026, 2026, doi: 10.3389/fdgth.2026.1759857.
13. A. Mccallum and K. Nigam, “A Comparison of Event Models for Naive Bayes Text Classification,” Work Learn Text Categ, vol. 752,
Mar. 2001.
14. C. Cortes and V. Vapnik, “Support-vector networks,” Chem. Biol. Drug Des., vol. 297, pp. 273–297, Mar. 2009, doi:
10.1007/%2FBF00994018.
15. L. Breiman, “Random Forests,” Mach. Learn., vol. 45, pp. 5–32, Mar. 2001, doi: 10.1023/A:1010950718922.
16. T. J. Devi and A. Gopi, “The Evaluation of Deep Learning Models for Detecting Mental Disorders Based on Text Summarization in
Societal Analysis,” International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 3, pp. 1620–1628, 2024.
17. Q. Saeed and Y. Cha, “Multi-modal deep-attention-BiLSTM based early detection of mental health issues using social media posts,”
Sci. Rep., vol. 15, Mar. 2025, doi: 10.1038/s41598-025-19141-0.
18. Y. Hussain, M. A. Zaheer, A. M. Khan, and A. S. Malik, “Depression detection using deep learning and large language models from
multimodalities,” Front. Digit. Health, vol. Volume 8-2026, 2026, doi: 10.3389/fdgth.2026.1759857.
19. J. Thekkekara and S. Yongchareon, “An Attention-Based BERT–CNN–BiLSTM Model for Depression Detection from Emojis in
Social Media Text,” Big Data and Cognitive Computing, vol. 9, p. 310, Mar. 2025, doi: 10.3390/bdcc9120310.
20. M. Nadeem, M. A. Abbasi, F. Ali, K. Mughal, S. Azhar, and A. Saeed, “A Deep Learning Approach to Early Detection of Depression
Through Social Media Text Analysis,” International Journal of Social Sciences Bulletin, vol. 1, no. 1, pp. 653–675, 2025, doi:
10.5281/zenodo.17435175.
21. S. C H, “A Deep Learning Framework for Depression and Major Depressive Disorder Detection from Social Media Text UsingMentalBERT and Multilayer Perceptron,” Mar. 2025, doi: 10.22266/ijies2025.0930.16.
22. A. Amanat et al., “Deep Learning for Depression Detection from Textual Data,” Electronics (Basel)., vol. 11, Mar. 2022, doi:
10.3390/electronics11050676.
23. Md. Z. Uddin, K. Dysthe, A. Følstad, and P. Brandtzaeg, “Deep learning for prediction of depressive symptoms in a large textual
dataset,” Neural Comput. Appl., vol. 34, pp. 1–24, Jan. 2022, doi: 10.1007/s00521-021-06426-4.
24. Y. Shen, H. Yang, and L. Lin, “Automatic Depression Detection: An Emotional Audio-Textual Corpus and a GRU/BiLSTM-based
Model,” Mar. 2022. doi: 10.48550/arXiv.2202.08210.
25. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, pp. 1735–1780, Mar. 1997, doi:
10.1162/neco.1997.9.8.1735.
26. S. Hochreiter and J. Schmidhuber, “Long Short-Term Memory,” Neural Comput., vol. 9, pp. 1735–1780, Mar. 1997, doi:
10.1162/neco.1997.9.8.1735.
27. S. Jain, D. Rastogi, P. Nagrath, G. Stoian, D. Dănciulescu, and J. D, “Depression Detection Model Based on Facial, Verbal Features,
and Motion Activity Using Deep Learning,” Traitement du Signal, vol. 42, pp. 2499–2512, Mar. 2025, doi: 10.18280/ts.420505.
28. G. Lam, H. Dongyan, and W. Lin, “CONTEXT-AWARE DEEP LEARNING FOR MULTI-MODAL DEPRESSION DETECTION.”
29. B. Diep, M. Stanojevic, and J. Novikova, “Multi-modal deep learning system for depression and anxiety detection,” Mar. 2022. doi:
10.48550/arXiv.2212.14490.
30. P. Zhou et al., “Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification,” Mar. 2016, pp. 207–
212. doi: 10.18653/v1/P16-2034.
31. T. Mikolov, K. Chen, G. s Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space,” Proceedings of
Workshop at ICLR, vol. 2013, Mar. 2013.
32. Y. Hussain, M. A. Zaheer, A. M. Khan, and A. S. Malik, “Depression detection using deep learning and large language models from
multimodalities,” Front. Digit. Health, vol. Volume 8-2026, 2026, doi: 10.3389/fdgth.2026.1759857.
33. L. he et al., “Deep Learning for Depression Recognition with Audiovisual Cues: A Review,” Mar. 2021. doi:
10.48550/arXiv.2106.00610.
34. F. Y. Prity, T. C. Munira, S. Ahmed Shayeed, and M. J. U. Chowdhury, “MentalDistress: A multi-class social media text dataset for
mental health–related emotion detection,” 2026, Mendeley Data. doi: 10.17632/b42wr437hg.1.
35. S. Bird, “NLTK: The natural language toolkit,” Mar. 2006. doi: 10.3115/1225403.1225421.
36. T. Mikolov, K. Chen, G. s Corrado, and J. Dean, “Efficient Estimation of Word Representations in Vector Space,” Proceedings of
Workshop at ICLR, vol. 2013, Mar. 2013.
37. D. P. Patinavalasa and D. Suneel Kumar, “Scalable Email Spam Detection Using BiLSTM with Large-Scale Hybrid Datasets,”
International Journal of Recent Trends in Multidisciplinary Research, p. 96, Mar. 2026, doi: 10.59256/ijrtmr.20260602016.
38. M. Abadi et al., “TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems,” Mar. 2016, doi:
10.48550/arXiv.1603.04467.
39. A. Zhang, Z. Lipton, M. Li, and A. Smola, “Dive into Deep Learning,” Mar. 2021. doi: 10.48550/arXiv.2106.11342.
40. F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” Journal of Machine Learning Research, vol. 12, Mar. 2012.
41. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language
Understanding,” Mar. 2018. doi: 10.48550/arXiv.1810.04805.
42. A. Vaswani et al., “Attention Is All You Need,” Mar. 2017, doi: 10.48550/arXiv.1706.03762.
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