Topic modelling analysis of depression text message therapy: A preliminary study

Topic modelling analysis of depression text message therapy: A preliminary study


  • Teh Faradilla Abdul Rahman Centre of Foundation Studies, Universiti Teknologi MARA, Cawangan Selangor, Kampus Dengkil, 43800, Dengkil, Selangor
  • Raudzatul Fathiyah Mohd Said Center of Foundation Studies, Universiti Teknologi MARA, Cawangan Selangor, Kampus Dengkil, 438000, Selangor
  • Alya Geogiana Buja Faculty of Computer & Mathematical Sciences, Universiti Teknologi MARA, Cawangan Melaka, Kampus Jasin, Malaysia
  • Norshita Mat Nayan Institute of Visual Informatics, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia



Topic modelling, depression topics, Biterm Topic Model, Word Network Topic Model, Latent Feature Dirichlet Multinomial Mixture


The coronavirus disease 2019 (COVID-19) that has plagued the world since 2019 has initiated several issues and challenges in the mental health services field. World Health Organisation (WHO) recommended implementing remote mental health services such as telehealth to reach out to patients. One of telehealth services is text messaging therapy. Despite the challenges in treating depression via text messaging, the text messages for depression therapy that were built with different content renders this situation as a captivating subject for study. Nonetheless, the topics included in depression mobile therapy are scarce, particularly from the short text perspective. Fortunately, a machine learning technique known as topic modelling (TM) can be used to extracts topics from a set of documents without manually reading individual documents. It is very useful in searching for topics contained in short texts. This study aims to determine the topics in the text messages sent by mental health practitioners for depression therapy. In this study, three topic modelling techniques, i.e., Biterm Topic Model (BTM), Word Network Topic Model (WNTM), and Latent Feature Dirichlet Multinomial Mixture (LFDMM), were evaluated on 258 text messages of depression therapy. The performance of the TM techniques was evaluated using classification accuracy, clustering, and coherence scores. The findings indicate that the set of text messages comprises five topics. BTM performed better than the other techniques in classification accuracy and clustering in some cases based on the performance measures. Consequently, not much significant difference was found in the coherence score between the three topic modelling.


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Aguilera, A., Bruehlman-Senecal, E., Demasi, O., & Avila, P. (2017). Automated Text Messaging as an Adjunct to Cognitive Behavioral Therapy for Depression:A Clinical Trial. Journal of Medical Internet Research, 19(5).

Agyapong, V. I. O., Juhás, M., Mrklas, K., Hrabok, M., Omeje, J., Gladue, I., Kozak, J., Leslie, M., Chue, P., & Greenshaw, A. J. (2018). Randomized controlled pilot trial of supportive text messaging for alcohol use disorder patients. Journal of Substance Abuse Treatment, 94(2018), 74–80.

Agyapong, V. I. O., Mrklas, K., Juhás, M., Omeje, J., Ohinmaa, A., Dursun, S. M., & Greenshaw, A. J. (2016). Cross-sectional survey evaluating Text4Mood: Mobile health program to reduce psychological treatment gap in mental healthcare in Alberta through daily supportive text messages. BMC Psychiatry, 16(1), 1–12.

Agyapong, V. I. O., Shalaby, R., Hrabok, M., Vuong, W., Noble, J. M., Gusnowski, A., Mrklas, K., Li, D., Snaterse, M., Surood, S., Cao, B., Li, X. M., Greiner, R., & Greenshaw, A. J. (2021). Mental health outreach via supportive text messages during the covid-19 pandemic: Improved mental health and reduced suicidal ideation after six weeks in subscribers of text4hope compared to a control population. International Journal of Environmental Research and Public Health, 18(4), 1–13.

Albalawi, R., Yeap, T. H., & Benyoucef, M. (2020). Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis. Frontiers in Artificial Intelligence, 3(July), 1–14.

Almeida, A. M. P., Almeida, H. S., & Figueiredo-Braga, M. (2018). Mobile solutions in depression: Enhancing communication with patients using an SMS-based intervention. Procedia Computer Science, 138(2018), 89–96.

Anstiss, D., & Davies, A. (2015). “Reach Out, Rise Up”: The efficacy of text messaging in an intervention package for anxiety and depression severity in young people. Children and Youth Services Review, 58, 99–103.

Asmussen, C. B., & Moller, C. (2019). Smart literature review: a practical topic modelling approach to exploratory literature review. Journal of Big Data, 6.

Barrera, A. Z., Aguilera, A., Inlow, N., & Servin, J. (2020). A preliminary study on the acceptability of a brief SMS program for perinatal women. Health Informatics Journal, 26(2), 1079–1087.

Cheng, X., Yan, X., Lan, Y., & Guo, J. (2014). BTM: Topic modeling over short texts. IEEE Transactions on Knowledge and Data Engineering, 26(12), 2928–2941.

Franz, P. J., Nook, E. C., Mair, P., & Nock, M. K. (2019). Using Topic Modeling to Detect and Describe Self-Injurious and Related Content on a Large-Scale Digital Platform. Suicide and Life-Threatening Behavior, 50(1), 5–18.

Grün, B., & Hornik, K. (2011). topicmodels: An R Package for Fitting Topic Models. Journal of Statistical Software., 40(13), 1–30.

Hartnett, D., Murphy, E., Kehoe, E., Agyapong, V., Mcloughlin, D. M., & Farren, C. (2017). Supportive text messages for patients with alcohol use disorder and a comorbid depression : a protocol for a single-blind randomised controlled aftercare trial. BMJ Open, 7:e013587.

Howes, C., Purver, M., & McCabe, R. (2013). Investigating Topic Modelling for therapy dialogue analysis. Proceedings of the IWCS 2013 Workshop on Computational Semantics in Clinical Text (CSCT 2013), 7–16.

Keding, A., Böhnke, J. R., Croudace, T. J., Richmond, S. J., & MacPherson, H. (2015). Validity of single item responses to short message service texts to monitor depression: An mHealth sub-study of the UK ACUDep trial. BMC Medical Research Methodology, 15(1), 1–10.

Kerrigan, A., Kaonga, N. N., Tang, A. M., Jordan, M. R., Steven, Y., & Diseases, I. (2019). Content Guidance for Mobile Phones Short Message Service (SMS)-Based Antiretroviral Therapy Adherence and Appointmnet Reminders : A Review of the Literature. AIDS Care, 31(5), 636–646.

Kraft, S., Wolf, M., Klein, T., Becker, T., Bauer, S., & Puschner, B. (2017). Text message feedback to support mindfulness practice in people with depressive symptoms: A pilot randomized controlled trial. JMIR MHealth and UHealth, 5(5).

Li, C., Wang, H., Zhang, Z., Sun, A., & Ma, Z. (2016). Topic modeling for short texts with auxiliary word embeddings. SIGIR 2016 - Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, 165–174.

Looijmans, A., Jörg, F., Bruggeman, R., Schoevers, R., & Corpeleijn, E. (2017). Design of the Lifestyle Interventions for severe mentally ill Outpatients in the Netherlands (LION) trial; a cluster randomised controlled study of a multidimensional web tool intervention to improve cardiometabolic health in patients with severe mental i. BMC Psychiatry, 17(1), 1–14.

Navarro, C., Yáñez, A. M., Garcia, A., Seguí, A., Gazquez, F., Marino, J. A., Ibarra, O., Serrano-Ripoll, M. J., Gomez-Juanes, R., Bennasar-Veny, M., Salva, J., Oliván, B., Roca, M., Gili, M., & Garcia-Toro, M. (2020). Effectiveness of a healthy lifestyle promotion program as adjunctive teletherapy for treatment-resistant major depression during COVID 19 pandemic: A randomized clinical trial protocol. Medicine, 99(45), e22958.

Nguyen, D. Q., Billingsley, R., Du, L., & Johnson, M. (2015). Improving Topic Models with Latent Feature Word Representations. Transactions of the Association for Computational Linguistics, 3, 598–599.

Onan, Aytuğ. (2022). Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification. Journal of King Saud University - Computer and Information Sciences, 34(5), 2098–2117.

Onan, Aytug, & Tocoglu, M. A. (2021). A Term Weighted Neural Language Model and Stacked Bidirectional LSTM Based Framework for Sarcasm Identification. IEEE Access, 9, 7701–7722.

Pietsch, A. S., & Lessmann, S. (2018). Topic modeling for analyzing open-ended survey responses. Journal of Business Analytics, 1(2), 93–116.

Qiang, J., Qian, Z., Li, Y., Yuan, Y., & Wu, X. (2019). Short text topic modeling techniques, applications, and performance: A survey. Journal of Latex Class Files, 14(8), 1–17.

Quan, X., Kit, C., Ge, Y., & Pan, S. J. (2015). Short and sparse text topic modeling via self-aggregation. Twenty-Fourth International Joint Conference on Artificial Intelligence, 2270–2276.

Ranney, M. L., Freeman, J. R., Connell, G., Boyer, E., Walton, M., Guthrie, K., Cunningham, R. M., Behavior, H., G-, B., Worcester, N., & Arbor, A. (2017). A Depression Prevention Intervention for Adolescents in the Emergency Department. Journal of Adolescent Health, 59(4), 401–410.

Schneider, J., & Vlachos, M. (2018). Topic modeling based on keywords and context. Proceedings of the 2018 SIAM International Conference on Data Mining, May, 369–377.

Shariful Islam, S. M., Chow, C. K., Redfern, J., Kok, C., Rådholm, K., Stepien, S., Rodgers, A., & Hackett, M. L. (2019). Effect of text messaging on depression in patients with coronary heart disease: A substudy analysis from the TEXT ME randomised controlled trial. BMJ Open, 9(2), 1–7.

Shi, L., Cheng, G., Xie, S. R., & Xie, G. (2019). A word embedding topic model for topic detection and summary in social networks. Measurement and Control (United Kingdom), 52(9–10), 1289–1298.

Tadesse, M. M., Lin, H., Xu, B., & Yang, L. (2019). Detection of depression-related posts in reddit social media forum. IEEE Access, 7, 44883–44893.

Tian, T., & Fang, Z. (2019). Attention-based Autoencoder Topic Model for Short Texts. Procedia Computer Science, 151, 1134–1139.

Välimäki, M., Kannisto, K. A., Vahlberg, T., Hätönen, H., & Adams, C. E. (2017). Short text messages to encourage adherence to medication and follow-up for people with psychosis ( Randomized controlled trial in Finland. Journal of Medical Internet Research, 19(7).

Velten, J., Bieda, A., Scholten, S., Wannemüller, A., & Margraf, J. (2018). Lifestyle choices and mental health: A longitudinal survey with German and Chinese students. BMC Public Health, 18(1), 1–15.

Wang, S. H., Ding, Y., Zhao, W., Huang, Y. H., Perkins, R., Zou, W., & Chen, J. J. (2016). Text mining for identifying topics in the literatures about adolescent substance use and depression. BMC Public Health, 16(1), 4–11.

World Health Organization. (2021). Comprehensive Mental Health Action Plan 2013 - 2030.

Wu, X., Li, C., Zhu, Y., & Miao, Y. (2020). Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder. In B. Webber, Y. He, & Y. Liu (Eds.), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (pp. 1772–1782). Association for Computational Linguistics.

Yan, X., Guo, J., Lan, Y., & Cheng, X. (2013). A biterm topic model for short texts. WWW 2013 - Proceedings of the 22nd International Conference on World Wide Web, May, 1445–1455.

Yang, Y., Wang, H., Zhu, J., Wu, Y., Jiang, K., Guo, W., & Shi, W. (2020). Dataless short text classification based on biterm topic model and word embeddings. IJCAI International Joint Conference on Artificial Intelligence, 3969–3975.

Yi, F., Jiang, B., & Wu, J. (2020). Topic Modeling for Short Texts via Word Embedding and Document Correlation. IEEE Access, 8, 30692–30705.

Young, L. M., Moylan, S., John, T., Turner, M., Opie, R., Hockey, M., Saunders, D., Bruscella, C., Jacka, F., Teychenne, M., Rosenbaum, S., Banker, K., Mahoney, S., Tembo, M., Lai, J., Mundell, N., McKeon, G., Yucel, M., Speight, J., … O’Neil, A. (2022). Evaluating telehealth lifestyle therapy versus telehealth psychotherapy for reducing depression in adults with COVID-19 related distress: the curbing anxiety and depression using lifestyle medicine (CALM) randomised non-inferiority trial protocol. BMC Psychiatry, 22(1), 1–12.

Zuo, Y., Wu, J., Xhang, H., Lin, H., Wang, F., Xu, K., & Xiong, H. (2016). Topic Modeling of Short Texts: A Pseudo-Document View. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2105–2114.

Zuo, Y., Zhao, J., & Xu, K. (2016). Word network topic model: a simple but general solution for short and imbalanced texts. Knowledge and Information Systems, 48(2), 379–398.




How to Cite

Abdul Rahman, T. F., Mohd Said, R. F., Buja, A. G., & Mat Nayan, N. (2024). Topic modelling analysis of depression text message therapy: A preliminary study. Journal of Computing Research and Innovation, 9(1), 283–299.



General Computing