Topic modelling analysis of depression text message therapy: A preliminary study
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 t...
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| Format: | Article |
| Language: | English |
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Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis
2024-03-01
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| Series: | Journal of Computing Research and Innovation |
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| Online Access: | https://jcrinn.com/index.php/jcrinn/article/view/401 |
| _version_ | 1797234589375135744 |
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| author | Teh Faradilla Abdul Rahman Raudzatul Fathiyah Mohd Said Alya Geogiana Buja Norshita Mat Nayan |
| author_facet | Teh Faradilla Abdul Rahman Raudzatul Fathiyah Mohd Said Alya Geogiana Buja Norshita Mat Nayan |
| author_sort | Teh Faradilla Abdul Rahman |
| collection | DOAJ |
| description |
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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| first_indexed | 2024-04-24T16:34:28Z |
| format | Article |
| id | doaj.art-02fb15c519ce47519eec504866d5d4ff |
| institution | Directory Open Access Journal |
| issn | 2600-8793 |
| language | English |
| last_indexed | 2024-04-24T16:34:28Z |
| publishDate | 2024-03-01 |
| publisher | Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis |
| record_format | Article |
| series | Journal of Computing Research and Innovation |
| spelling | doaj.art-02fb15c519ce47519eec504866d5d4ff2024-03-29T18:57:14ZengFaculty of Computer and Mathematical Sciences, Universiti Teknologi MARA PerlisJournal of Computing Research and Innovation2600-87932024-03-019110.24191/jcrinn.v9i1.401Topic modelling analysis of depression text message therapy: A preliminary studyTeh Faradilla Abdul Rahman0Raudzatul Fathiyah Mohd Said1Alya Geogiana Buja2Norshita Mat Nayan3Centre of Foundation Studies, Universiti Teknologi MARA, Cawangan Selangor, Kampus Dengkil, 43800, Dengkil, SelangorCenter of Foundation Studies, Universiti Teknologi MARA, Cawangan Selangor, Kampus Dengkil, 438000, SelangorFaculty of Computer & Mathematical Sciences, Universiti Teknologi MARA, Cawangan Melaka, Kampus Jasin, MalaysiaInstitute of Visual Informatics, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia 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. https://jcrinn.com/index.php/jcrinn/article/view/401Topic modellingdepression topicsBiterm Topic ModelWord Network Topic ModelLatent Feature Dirichlet Multinomial Mixture |
| spellingShingle | Teh Faradilla Abdul Rahman Raudzatul Fathiyah Mohd Said Alya Geogiana Buja Norshita Mat Nayan Topic modelling analysis of depression text message therapy: A preliminary study Journal of Computing Research and Innovation Topic modelling depression topics Biterm Topic Model Word Network Topic Model Latent Feature Dirichlet Multinomial Mixture |
| title | Topic modelling analysis of depression text message therapy: A preliminary study |
| title_full | Topic modelling analysis of depression text message therapy: A preliminary study |
| title_fullStr | Topic modelling analysis of depression text message therapy: A preliminary study |
| title_full_unstemmed | Topic modelling analysis of depression text message therapy: A preliminary study |
| title_short | Topic modelling analysis of depression text message therapy: A preliminary study |
| title_sort | topic modelling analysis of depression text message therapy a preliminary study |
| topic | Topic modelling depression topics Biterm Topic Model Word Network Topic Model Latent Feature Dirichlet Multinomial Mixture |
| url | https://jcrinn.com/index.php/jcrinn/article/view/401 |
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