Optimizing Mental Health Classification on Reddit: A Comparative Study of Adam, RMSProp, and SGD with L2 Regularization
-
Vander Mulya Putra
*
Universitas Dian Nuswantoro, Semarang,
Indonesia
- Junta Zeniarja Universitas Dian Nuswantoro, Semarang, Indonesia (*) Corresponding Author
Abstract
The rising prevalence of mental health discussions on social media platforms has created new opportunities for understanding and supporting individuals facing psychological challenges. This study examines the automated classification of mental health content on Reddit, focusing on five clinically significant conditions (ADHD, anxiety, bipolar disorder, depression, and PTSD) and non-clinical discussions. Reddit was selected as the primary data source due to its unique subreddit structure and rich user-generated content in mental health communities, where individuals actively seek support and share experiences. Using a Multi-layer Perceptron (MLP) architecture, the study conducted a comprehensive evaluation of three optimization algorithms (Adam, RMSProp, and SGD) in conjunction with L2 regularization (λ=0.01) for mental health text classification. The study incorporated Easy Data Augmentation (EDA) techniques to enhance model robustness, implementing paraphrase-based augmentation methods that improved classification performance by 3%. Through systematic evaluation across multiple metrics, the study found that the RMSProp optimizer without L2 regularization achieved optimal performance, demonstrating 83% precision and 82% recall across all diagnostic categories. Notably, the application of L2 regularization consistently resulted in decreased model performance across all optimizers, with performance degradation ranging from 3% to 52%. These findings contribute to the development of more accurate automated mental health monitoring systems while highlighting the critical role of optimizer selection in mental health-related Natural Language Processing (NLP) tasks.
Downloads
References
X. Man, J. Liu, and Z. Xue, “Effects of Bullying Forms on Adolescent Mental Health and Protective Factors: A Global Cross-Regional Research Based on 65 Countries,” Int. J. Environ. Res. Public Health, vol. 19, no. 4, 2022, doi: 10.3390/ijerph19042374.
N. C. Momen et al., “Association between Mental Disorders and Subsequent Medical Conditions,” N. Engl. J. Med., vol. 382, no. 18, pp. 1721–1731, 2020, doi: 10.1056/nejmoa1915784.
C. Arango et al., “Risk and protective factors for mental disorders beyond genetics: an evidence-based atlas,” World Psychiatry, vol. 20, no. 3, pp. 417–436, 2021, doi: 10.1002/wps.20894.
Aschbrenner, J. A. Naslund, A. Bondre, J. Torous, and K. A., “Social Media and Mental Health: Benefits, Risks, and Opportunities for Research and Practice,” J. Technol. Behav. Sci., vol. 5, no. 3, pp. 245–257, 2020, [Online]. Available: https://doi.org/10.1007/s41347-020-00134-x.
V. J. Clemente-Suárez et al., “The impact of the covid-19 pandemic on mental disorders. A critical review,” Int. J. Environ. Res. Public Health, vol. 18, no. 19, 2021, doi: 10.3390/ijerph181910041.
J. A. D. B. Campos, B. G. Martins, L. A. Campos, F. de Fátima Valadão-Dias, and J. Marôco, “Symptoms related to mental disorder in healthcare workers during the COVID-19 pandemic in Brazil,” Int. Arch. Occup. Environ. Health, vol. 94, no. 5, pp. 1023–1032, 2021, doi: 10.1007/s00420-021-01656-4.
F. B. Schuch and D. Vancampfort, “Physical activity, exercise, and mental disorders: it is time to move on,” Trends Psychiatry Psychother., vol. 43, no. 3, pp. 177–184, 2021, doi: 10.47626/2237-6089-2021-0237.
S. H. Aarestad et al., “Clinical Characteristics of Patients Seeking Treatment for Common Mental Disorders Presenting With Workplace Bullying Experiences,” Front. Psychol., vol. 11, no. November, pp. 1–12, 2020, doi: 10.3389/fpsyg.2020.583324.
A. S. Uban, B. Chulvi, and P. Rosso, “An emotion and cognitive based analysis of mental health disorders from social media data,” Futur. Gener. Comput. Syst., vol. 124, pp. 480–494, 2021, doi: 10.1016/j.future.2021.05.032.
N. Proferes, N. Jones, S. Gilbert, C. Fiesler, and M. Zimmer, “Studying Reddit: A Systematic Overview of Disciplines, Approaches, Methods, and Ethics,” Soc. Media Soc., vol. 7, no. 2, 2021, doi: 10.1177/20563051211019004.
Z. Jiang, S. I. Levitan, J. Zomick, and J. Hirschberg, “Detection of mental health conditions from Reddit via deep contextualized representations,” EMNLP 2020 - 11th Int. Work. Heal. Text Min. Inf. Anal. LOUHI 2020, Proc. Work., pp. 147–156, 2020, doi: 10.18653/v1/2020.louhi-1.16.
T. S. Adekunle, O. O. Alabi, M. O. Lawrence, G. N. Ebong, G. O. Ajiboye, and T. A. Bamisaye, “The Use of AI to Analyze Social Media Attacks for Predictive Analytics,” J. Comput. Theor. Appl., vol. 1, no. 4, pp. 386–395, 2024, doi: 10.62411/jcta.10120.
A. Iorliam and J. A. Ingio, “A Comparative Analysis of Generative Artificial Intelligence Tools for Natural Language Processing,” J. Comput. Theor. Appl., vol. 1, no. 3, pp. 311–325, 2024, doi: 10.62411/jcta.9447.
B. Turkoglu and E. Kaya, “Training multi-layer perceptron with artificial algae algorithm,” Eng. Sci. Technol. an Int. J., vol. 23, no. 6, pp. 1342–1350, 2020, doi: 10.1016/j.jestch.2020.07.001.
A. C. Cinar, “Training Feed-Forward Multi-Layer Perceptron Artificial Neural Networks with a Tree-Seed Algorithm,” Arab. J. Sci. Eng., vol. 45, no. 12, pp. 10915–10938, 2020, doi: 10.1007/s13369-020-04872-1.
D. M. Low, L. Rumker, T. Talker, J. Torous, G. Cecchi, and S. S. Ghosh, “Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit During COVID-19: Observational Study,” J. Med. Internet Res., vol. 22, no. 10, p. e22635, 2020, doi: 10.17605/OSF.IO/7PEYQ.
J. Li et al., “KRA: K-Nearest Neighbor Retrieval Augmented Model for Text Classification,” Electron., vol. 13, no. 16, pp. 1–16, 2024, doi: 10.3390/electronics13163237.
J. I. T. Krisna, A. Luthfiarta, L. D. Cahya, S. Winarno, and A. Nugraha, “Comparing Optimizer Strategies For Enhancing Emotion Classification In IndoBERT Models,” Adv. Sustain. Sci. Eng. Technol., vol. 6, no. 2, pp. 1–8, 2024, doi: 10.26877/asset.v6i2.18228.
X. Ni, L. Fang, and H. Huttunen, “Adaptive L2 regularization in person Re-identification,” Proc. - Int. Conf. Pattern Recognit., pp. 9601–9607, 2020, doi: 10.1109/ICPR48806.2021.9412481.
Y. Zhang et al., “Reporting of Ethical Considerations in Qualitative Research Utilizing Social Media Data on Public Health Care: Scoping Review,” J. Med. Internet Res., vol. 26, no. 1, 2024, doi: 10.2196/51496.
R. Patil, S. Boit, V. Gudivada, and J. Nandigam, “A Survey of Text Representation and Embedding Techniques in NLP,” IEEE Access, vol. 11, no. March, pp. 36120–36146, 2023, doi: 10.1109/ACCESS.2023.3266377.
A. Al Bataineh, D. Kaur, and S. M. J. Jalali, “Multi-Layer Perceptron Training Optimization Using Nature Inspired Computing,” IEEE Access, vol. 10, pp. 36963–36977, 2022, doi: 10.1109/ACCESS.2022.3164669.
I. Lorencin, N. Anđelić, J. Španjol, and Z. Car, “Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis,” Artif. Intell. Med., vol. 102, 2020, doi: 10.1016/j.artmed.2019.101746.
J. Wu, K. Hu, Y. Cheng, H. Zhu, X. Shao, and Y. Wang, “Data-driven remaining useful life prediction via multiple sensor signals and deep long short-term memory neural network,” ISA Trans., vol. 97, no. xxxx, pp. 241–250, 2020, doi: 10.1016/j.isatra.2019.07.004.
E. Hassan, M. Y. Shams, N. A. Hikal, and S. Elmougy, “The effect of choosing optimizer algorithms to improve computer vision tasks: a comparative study,” Multimed. Tools Appl., vol. 82, no. 11, pp. 16591–16633, 2023, doi: 10.1007/s11042-022-13820-0.
M. Ahmad, N. Wahid, R. A. Hamid, S. Sadiq, A. Mehmood, and G. S. Choi, “Decision Level Fusion Using Hybrid Classifier for Mental Disease Classification,” Comput. Mater. Contin., vol. 72, no. 3, pp. 5041–5058, 2022, doi: 10.32604/cmc.2022.026077.
D. R. I. M. Setiadi, K. Nugroho, A. R. Muslikh, S. W. Iriananda, and A. A. Ojugo, “Integrating SMOTE-Tomek and Fusion Learning with XGBoost Meta-Learner for Robust Diabetes Recognition,” J. Futur. Artif. Intell. Technol., vol. 1, no. 1, pp. 23–38, 2024, doi: 10.62411/faith.2024-11.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Optimizing Mental Health Classification on Reddit: A Comparative Study of Adam, RMSProp, and SGD with L2 Regularization
Pages: 2228-2239
Copyright (c) 2025 Vander Mulya Putra, Junta Zeniarja
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
