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Vol. 10, No. 3, August 2025

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Analysis of Mental Health Disorders via Social Media Mining Using LSTM and Bi-LSTM

https://doi.org/10.22219/kinetik.v10i3.2205
Binti Kholifah
Universitas Negeri Surabaya
Iwan Syarif
Politeknik Elektronika Negeri Surabaya
Tessy Badriyah
Politeknik Elektronika Negeri Surabaya

Corresponding Author(s) : Binti Kholifah

bintikholifah@unesa.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 10, No. 3, August 2025

Abstract

Mental health disorders are a growing global concern, with many individuals lacking early detection and appropriate treatment. Mental illness can impact a person’s quality of life and often goes undetected until symptoms worsen. One contributing factor to this problem is the limited ways to detect mental disorders in their early stages. Social media, especially platform X, offers the potential to analyze users’ emotional expressions that may indicate a mental disorder, such as depression or anxiety. Psychological symptoms can be explored more broadly using Natural Languages ​​Processing. This study optimizes several text pre-processing techniques to extract meaningful information from social media text. Then to convert words into number vectors, several word embedding methods are used such as Word2Vec, FastText, and Glove. Meanwhile, the classification process is carried out using LSTM and Bi-LSTM because they are considered capable of studying data sequence patterns such as sentence structure well. The results show that the addition of expanding contraction, emoticon handling, negation handling, repeated character handling, and spelling correction in the pre-processing text can improve model performance. In addition, Bi-LSTM with pre-trained FastText shows better results than other methods in all experiments with 86% accuracy, 87.5% precision, 84% recall, and 85.71% F1-Score.

Keywords

Mental Health Text Pre-processing Word Embedding LSTM BiLSTM
Kholifah, B., Syarif, I., & Badriyah, T. (2025). Analysis of Mental Health Disorders via Social Media Mining Using LSTM and Bi-LSTM . Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10(3). https://doi.org/10.22219/kinetik.v10i3.2205
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References
  1. WHO, “Suicide,” 2024. https://www.who.int/news-room/fact-sheets/detail/suicide (accessed Dec. 23, 2024).
  2. N. A. Wirawan, “Angka Kasus Bunuh Diri di Indonesia Meningkat 60% dalam 5 Tahun Terakhir,” 2024. https://data.goodstats.id/statistic/angka-kasus-bunuh-diri-di-indonesia-meningkat-60-dalam-5-tahun-terakhir-2FzH6 (accessed Dec. 23, 2024).
  3. K. K. R. Biro Komunikasi dan Pelayanan Publik, “Cegah Bunuh Diri, Kemenkes Ajak Remaja Bicara Soal Kesehatan Mental – Sehat Negeriku,” 2024. https://sehatnegeriku.kemkes.go.id/baca/umum/20240917/2446492/cegah-bunuh-diri-kemenkes-ajak-remaja-bicara-soal-kesehatan-mental/ (accessed Dec. 23, 2024).
  4. E. Velthorst et al., “The impact of loneliness and social relationship dissatisfaction on clinical and functional outcomes in Dutch mental health service users,” Psychiatry Research, vol. 342. 2024. doi: 10.1016/j.psychres.2024.116242.
  5. B. Kholifah, I. Syarif, and T. Badriyah, “Mental Disorder Detection via Social Media Mining using Deep Learning,” Kinet. Game Technol. Inf. Syst. Comput. Network, Comput. Electron. Control, vol. 4, pp. 309–316, 2020, doi: 10.22219/kinetik.v5i4.1120.
  6. M. L. Zou, M. X. Li, and V. Cho, “Depression and disclosure behavior via social media: A study of university students in China,” Heliyon, vol. 6, no. 2, p. e03368, 2020, doi: 10.1016/j.heliyon.2020.e03368.
  7. I. Syarif, N. Ningtias, and T. Badriyah, “Study on Mental Disorder Detection via Social Media Mining,” 2019 4th Int. Conf. Comput. Commun. Secur., pp. 1–6, 2019, doi: 10.1109/CCCS.2019.8888096.
  8. C. Ouni, E. Benmohamed, and H. Ltifi, “Deep learning-based Soft word embedding approach for sentiment Deep learning-based Soft word embedding approach for sentiment analysis analysis,” Procedia Comput. Sci., vol. 246, pp. 1355–1364, 2024, doi: 10.1016/j.procs.2024.09.720.
  9. R. Phukan, P. J. Goutom, and N. Baruah, “Assamese Fake News Detection: A Comprehensive Exploration of LSTM and Bi-LSTM Techniques,” Procedia Comput. Sci., vol. 235, pp. 2167–2177, 2024, doi: 10.1016/j.procs.2024.04.205.
  10. J. Zhao, D. Zeng, Y. Xiao, L. Che, and M. Wang, “User personality prediction based on topic preference and sentiment analysis using LSTM model,” Pattern Recognition Letters, vol. 138. pp. 397–402, 2020. doi: 10.1016/j.patrec.2020.07.035.
  11. V. Kishore and M. Kumar, “Enhanced Multimodal Fake News Detection with Optimal Feature Fusion and Modified Bi-LSTM Architecture,” Cybernetics and Systems. 2023. doi: 10.1080/01969722.2023.2175155.
  12. U. Naseem, I. Razzak, and P. W. Eklund, “A survey of pre-processing techniques to improve short-text quality: a case study on hate speech detection on twitter,” Multimedia Tools and Applications, vol. 80, no. 28–29. pp. 35239–35266, 2021. doi: 10.1007/s11042-020-10082-6.
  13. M. Siino, I. Tinnirello, and M. La Cascia, “Is text preprocessing still worth the time? A comparative survey on the influence of popular preprocessing methods on Transformers and traditional classifiers,” Inf. Syst., vol. 121, no. March 2023, p. 102342, 2024, doi: 10.1016/j.is.2023.102342.
  14. N. H. Yahya and H. Abdul Rahim, “Linguistic markers of depression: Insights from english-language tweets before and during the COVID-19 pandemic,” Lang. Heal., vol. 1, no. 2, pp. 36–50, 2023, doi: 10.1016/j.laheal.2023.10.001.
  15. A. Daud, D. Irwanto, M. Said, M. Mubyl, and Mustamin, “Identification Of Chatbot Usage In Online Store Services Using Natural Language Processing Methods,” Adv. Sustain. Sci. Eng. Technol., vol. 6, no. 2, pp. 1–9, 2024, doi: 10.26877/asset.v6i2.18309.
  16. N. Garg and K. Sharma, “Text pre-processing of multilingual for sentiment analysis based on social network data,” Int. J. Electr. Comput. Eng., vol. 12, no. 1, pp. 776–784, 2022, doi: 10.11591/ijece.v12i1.pp776-784.
  17. A. Jabbar, S. Iqbal, M. I. Tamimy, A. Rehman, S. A. Bahaj, and T. Saba, “An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems,” IEEE Access, vol. 11, no. October, pp. 133681–133702, 2023, doi: 10.1109/ACCESS.2023.3332710.
  18. S. Biradar, G. T. Raju, and K. M. Divakar, “Negation Handling and Domain Generalization in Sentiment Analysis using Machine Learning Models,” 2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024. 2024. doi: 10.1109/ICKECS61492.2024.10616885.
  19. P. K. Singh and S. Paul, “Deep Learning Approach for Negation Handling in Sentiment Analysis,” IEEE Access, vol. 9, pp. 102579–102592, 2021, doi: 10.1109/ACCESS.2021.3095412.
  20. D. J. Ladani and N. P. Desai, “Stopword Identification and Removal Techniques on TC and IR applications: A Survey,” 2020 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020. pp. 466–472, 2020. doi: 10.1109/ICACCS48705.2020.9074166.
  21. Z. H. Kilimci and S. Akyokus, “The Evaluation of Word Embedding Models and Deep Learning Algorithms for Turkish Text Classification,” UBMK 2019 - Proceedings, 4th Int. Conf. Comput. Sci. Eng., pp. 548–553, 2019, doi: 10.1109/UBMK.2019.8907027.
  22. M. Mars, “From Word Embeddings to Pre-Trained Language Models: A State-of-the-Art Walkthrough,” Appl. Sci., vol. 12, no. 17, 2022, doi: 10.3390/app12178805.
  23. S. Sivakumar, L. S. Videla, T. Rajesh Kumar, J. Nagaraj, S. Itnal, and D. Haritha, “Review on Word2Vec Word Embedding Neural Net,” Proc. - Int. Conf. Smart Electron. Commun. ICOSEC 2020, no. Icosec, pp. 282–290, 2020, doi: 10.1109/ICOSEC49089.2020.9215319.
  24. T. Yao, Z. Zhai, and B. Gao, “Text Classification Model Based on fastText,” Proc. 2020 IEEE Int. Conf. Artif. Intell. Inf. Syst. ICAIIS 2020, pp. 154–157, 2020, doi: 10.1109/ICAIIS49377.2020.9194939.
  25. P. J. Worth, “Word Embeddings and Semantic Spaces in Natural Language Processing,” International Journal of Intelligence Science, vol. 13, no. 01. pp. 1–21, 2023. doi: 10.4236/ijis.2023.131001.
  26. C. O. Ewald and Y. Li, “The role of news sentiment in salmon price prediction using deep learning,” J. Commod. Mark., vol. 36, 2024, doi: 10.1016/j.jcomm.2024.100438.
  27. R. Kizito, P. Scruggs, X. Li, M. Devinney, J. Jansen, and R. Kress, “Long Short-Term Memory Networks for Facility Infrastructure Failure and Remaining Useful Life Prediction,” IEEE Access, vol. 9, pp. 67585–67594, 2021, doi: 10.1109/ACCESS.2021.3077192.
  28. S. Shan et al., “A Deep Learning Model with Attention-BiLSTM Networks Combining XGBoost Residual Correction for Short-Term Water Demand Forecast,” 2022, [Online]. Available: https://doi.org/10.21203/rs.3.rs-1992189/v1
  29. B. H. Nayef, S. N. H. S. Abdullah, R. Sulaiman, and A. M. Saeed, “Text Extraction with Optimal Bi-LSTM,” Comput. Mater. Contin., vol. 76, no. 3, pp. 3549–3567, 2023, doi: 10.32604/cmc.2023.039528.
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