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Mental Health Detection from Social Media Posts Using NLP
Authors
1 3 4 5Department of Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering Tadepalligudem, Andhra Pradesh, India. 2 Associate Professor, Department of Artificial Intelligence and Machine Learning, Sasi Institute of Technology and Engineering Tadepalligudem, Andhra Pradesh, India.
Article Information
- Organised By
- Madanapalle Institute of Technology & Science (MITS)
- Conference Date
- 9 & 10 March 2026
- Pages
- 152-158
- Article Type
- Research Article
- Check for updates
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References
1. A. Kumar and S. Patel, “Deep learning approaches for mental health prediction,” in Proc. IEEE Int. Conf. AI, 2021, pp. 12–18.
2. R. Sharma et al., “Social media analytics for depression detection,” in Proc. ACM Conf. Data Mining, 2020, pp. 44–50.
3. J. Lee and K. Park, “NLP-based emotion recognition using LSTM,” in Proc. Int. Conf. NLP, 2019, pp. 66–72.
4. S. Mehta and P. Singh, “Transformer models for sentiment analysis,” in Proc. IEEE Big Data Conf., 2022, pp. 101–108.
5. T. Brown et al., “Context-aware embeddings for healthcare text mining,” in Proc. ICMLA, 2021, pp. 210–216.
6. M. Trotzek, S. Koitka, and C. M. Friedrich, “Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences,” IEEE Transactions on Knowledge and Data Engineer- ing, vol. 32, no. 3, pp. 588–601, 2020.
7. A. Cohan, B. Desmet, A. Yates, A. Soldaini, and N. Goharian, “SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions,” in Proc. 27th Int. Conf. on Com- putational Linguistics (COLING), 2018, pp. 1485–1497.
8. G. Gkotsis et al., “Characterisation of mental health conditions in social media using informed deep learning,” Scientific Reports, vol. 7, no. 1, pp. 1–11, 2017.
9. H. Orabi, P. Buddhitha, M. H. Orabi, and D. Inkpen, “Deep learning for depression detection of Twitter users,” in Proc. 5th Workshop on Computational Linguistics and Clinical Psychology, 2018, pp. 88–97.
10. Y. Liu, M. Ott, N. Goyal, et al., “RoBERTa: A Robustly Optimized BERT Pretraining Approach,” arXiv preprint arXiv:1907.11692, 2019.
11. J. Pennebaker, R. Boyd, K. Jordan, and K. Blackburn, “The development and psychometric properties of LIWC2015,” University of Texas at Austin, 2015.
12. E. Mohammadi, A. R. Nezhad, and H. Shakiba, “Emotion detection from text using attention-based LSTM networks,” in Proc. IEEE Int. Conf. on Data Science and Advanced Analytics, 2019, pp. 652–657.
13. S. Chancellor and M. De Choudhury, “Methods in predictive techniques for mental health status on social media: A critical review,” NPJ Digital Medicine, vol. 3, no. 1, pp. 1–11, 2020.
14. T. Nasukawa and J. Yi, “Sentiment analysis: Capturing favorability using natural language processing,” in Proc. 2nd Int. Conf. on Knowledge Capture, 2003, pp. 70–77.
15. K. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. NAACL-HLT, 2019, pp. 4171–4186.
2. R. Sharma et al., “Social media analytics for depression detection,” in Proc. ACM Conf. Data Mining, 2020, pp. 44–50.
3. J. Lee and K. Park, “NLP-based emotion recognition using LSTM,” in Proc. Int. Conf. NLP, 2019, pp. 66–72.
4. S. Mehta and P. Singh, “Transformer models for sentiment analysis,” in Proc. IEEE Big Data Conf., 2022, pp. 101–108.
5. T. Brown et al., “Context-aware embeddings for healthcare text mining,” in Proc. ICMLA, 2021, pp. 210–216.
6. M. Trotzek, S. Koitka, and C. M. Friedrich, “Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences,” IEEE Transactions on Knowledge and Data Engineer- ing, vol. 32, no. 3, pp. 588–601, 2020.
7. A. Cohan, B. Desmet, A. Yates, A. Soldaini, and N. Goharian, “SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions,” in Proc. 27th Int. Conf. on Com- putational Linguistics (COLING), 2018, pp. 1485–1497.
8. G. Gkotsis et al., “Characterisation of mental health conditions in social media using informed deep learning,” Scientific Reports, vol. 7, no. 1, pp. 1–11, 2017.
9. H. Orabi, P. Buddhitha, M. H. Orabi, and D. Inkpen, “Deep learning for depression detection of Twitter users,” in Proc. 5th Workshop on Computational Linguistics and Clinical Psychology, 2018, pp. 88–97.
10. Y. Liu, M. Ott, N. Goyal, et al., “RoBERTa: A Robustly Optimized BERT Pretraining Approach,” arXiv preprint arXiv:1907.11692, 2019.
11. J. Pennebaker, R. Boyd, K. Jordan, and K. Blackburn, “The development and psychometric properties of LIWC2015,” University of Texas at Austin, 2015.
12. E. Mohammadi, A. R. Nezhad, and H. Shakiba, “Emotion detection from text using attention-based LSTM networks,” in Proc. IEEE Int. Conf. on Data Science and Advanced Analytics, 2019, pp. 652–657.
13. S. Chancellor and M. De Choudhury, “Methods in predictive techniques for mental health status on social media: A critical review,” NPJ Digital Medicine, vol. 3, no. 1, pp. 1–11, 2020.
14. T. Nasukawa and J. Yi, “Sentiment analysis: Capturing favorability using natural language processing,” in Proc. 2nd Int. Conf. on Knowledge Capture, 2003, pp. 70–77.
15. K. Devlin, M. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proc. NAACL-HLT, 2019, pp. 4171–4186.
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