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Vol. 10, No. 3, August 2025

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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

https://doi.org/10.22219/kinetik.v10i3.2205
Binti Kholifah
Uoliteknik Elektronika 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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