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  3. Vol. 9, No. 1, February 2024
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Vol. 9, No. 1, February 2024

Issue Published : Feb 28, 2024
Creative Commons License

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

Mental Health Prediction Model on Social Media Data Using CNN-BiLSTM

https://doi.org/10.22219/kinetik.v9i1.1849
Abdurrahim
Universitas Islam Indonesia
Dhomas Hatta Fudholi
Universitas Islam Indonesia

Corresponding Author(s) : Dhomas Hatta Fudholi

hatta.fudholi@uii.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 9, No. 1, February 2024
Article Published : Feb 28, 2024

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Abstract

Social media has transformed into a global platform for expression and interaction where users can share photos, images, and videos. The rapid development and widespread use of social media afford the opportunity to analyze the construction of social life in societies and communities. As a result of alterations in lifestyle during the COVID-19 pandemic, mental health disorders increased. Mental health is a complex disease involving numerous individual, socioeconomic, and clinical variables. Natural language processing and analysis methods are required to address this complexity. The classification of mental health-related texts, which can serve as early warnings and early diagnoses, is facilitated by analytical and natural language processing techniques. In this investigation, a CNN-BiLSTM model was utilized, which was aided by a FastText-based word weighting method. The utilized data set consists of texts on mental health with labels such as borderline personality disorder (BPD), anxiety, depression, bipolar, mentalillness, schizophrenia, and poison. There are 35000 training records and 6108 test records. The data will undergo a data cleansing procedure, which will include lower text stages, number removal, reading mark removal, and stopword removal. Modeling with CNN-BiLSTM and FastText weighting yielded an F1-Score and accuracy of 85% and 85%, respectively. In comparison to the Bi-LSTM model, the F1-Score and accuracy were both 83%.

Keywords

Mental Health Natural Language Processing Text Analytic Classification CNN-BiLSTM
Abdurrahim, & Dhomas Hatta Fudholi. (2024). Mental Health Prediction Model on Social Media Data Using CNN-BiLSTM . Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 9(1), 29-44. https://doi.org/10.22219/kinetik.v9i1.1849
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References
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References


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Rokom, “Ministry of Health Reveals Mental Health Issues in Indonesia,” 2021.

J. Singh, M. Wazid, D. P. Singh, and S. Pundir, “An embedded LSTM based scheme for depression detection and analysis,” Procedia Comput. Sci., vol. 215, pp. 166–175, 2022. https://doi.org/10.1016/j.procs.2022.12.019

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J. Ive et al., “Generation and evaluation of artificial mental health records for Natural Language Processing,” npj Digit. Med., vol. 3, no. 1, pp. 1–9, 2020. https://doi.org/10.1038/s41746-020-0267-x

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F. T. Giuntini, M. T. Cazzolato, M. de J. D. dos Reis, A. T. Campbell, A. J. M. Traina, and J. Ueyama, “A review on recognizing depression in social networks: challenges and opportunities,” J. Ambient Intell. Humaniz. Comput., vol. 11, no. 11, pp. 4713–4729, 2020. https://doi.org/10.1007/s12652-020-01726-4

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E. A. Ríssola, D. E. Losada, and F. Crestani, “A survey of computational methods for online mental state assessment on social media,” ACM Trans. Comput. Healthc., vol. 2, no. 2, 2021. https://doi.org/10.1145/3437259

G. Coppersmith, M. Dredze, and C. Harman, “Quantifying Mental Health Signals in Twitter,” in Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality, Jun. 2014, pp. 51–60. https://doi.org/10.3115/v1/W14-3207

R. A. CALVO, D. N. MILNE, M. S. HUSSAIN, and H. CHRISTENSEN, “Natural language processing in mental health applications using non-clinical texts,” Nat. Lang. Eng., vol. 23, no. 5, pp. 649–685, 2017. https://doi.org/10.1017/S1351324916000383

A. Murarka and I. B. M. Raleigh, “Classification of mental illnesses on social media using RoBERTa,” Proc. ofthe 12th Int. Work. Heal. Text Min. Inf. Anal., pp. 59–68, 2021.

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