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Pasha Khatami Hasibuan
Universitas Negeri Yogyakarta
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Early Detection of Mental Health on Social Media Using Bidirectional Architecture LSTM and Word Embedding Pasha Khatami Hasibuan
Journal of Science Technology and Education Vol 2 No 1 (2026): February
Publisher : Yayasan Arfah Bin Haji Muhammad Saleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63980/jste.v2i1.175

Abstract

Linguistic digital footprints on social media platforms have transformed into psychological biomarkers that offer real-time mental health screening opportunities. However, the main challenge in automated clinical text processing is semantic ambiguity and class imbalances (imbalanced datasets) that lead to predictive bias in conventional models. This study aims to develop a multiclass classification system for seven mental health categories (including Suicidal, Biopolar, and Anxiety) using a Deep Learning approach. The proposed method integrates Bidirectional Long Short-Term Memory (Bi-LSTM) architecture with Word Embedding to capture the context of a two-way narrative, as well as apply Class Weighting techniques to address data distribution disparities. The experiment was conducted on a public dataset containing 52,680 text entries. The test results showed that the model was able to overcome the majority bias very effectively, as evidenced by the high Recall values in critical classes such as Biopolar (0.84) and Suicidal (0.76), as well as an F1-Score of 0.91 in the Normal class. This study concludes that the Bi-LSTM architecture with adaptive class weighting is able to be an early warning system that is sensitive to high-risk disturbance signals.
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