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Topic modelling analysis of depression therapy text: a preliminary study / Teh Faradilla Abdul Rahman ... [et al.]

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 te...

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Main Authors: Abdul Rahman, Teh Faradilla, Mohd Said, Raudzatul Fathiyah, Buja, Alya Geogiana, Mat Nayan, Norshita
Format: Article
Language:English
Published: UiTM Cawangan Perlis 2024
Subjects:
Algorithms
Online Access:https://ir.uitm.edu.my/id/eprint/94363/1/94363.pdf
https://ir.uitm.edu.my/id/eprint/94363/
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spelling my.uitm.ir.943632024-05-03T09:38:55Z https://ir.uitm.edu.my/id/eprint/94363/ Topic modelling analysis of depression therapy text: a preliminary study / Teh Faradilla Abdul Rahman ... [et al.] jcrinn Abdul Rahman, Teh Faradilla Mohd Said, Raudzatul Fathiyah Buja, Alya Geogiana Mat Nayan, Norshita Algorithms 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. UiTM Cawangan Perlis 2024-03 Article PeerReviewed text en https://ir.uitm.edu.my/id/eprint/94363/1/94363.pdf Topic modelling analysis of depression therapy text: a preliminary study / Teh Faradilla Abdul Rahman ... [et al.]. (2024) Journal of Computing Research and Innovation (JCRINN) <https://ir.uitm.edu.my/view/publication/Journal_of_Computing_Research_and_Innovation_=28JCRINN=29/>, 9 (1): 22. pp. 283-299. ISSN 2600-8793
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
topic Algorithms
spellingShingle Algorithms
Abdul Rahman, Teh Faradilla
Mohd Said, Raudzatul Fathiyah
Buja, Alya Geogiana
Mat Nayan, Norshita
Topic modelling analysis of depression therapy text: a preliminary study / Teh Faradilla Abdul Rahman ... [et al.]
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.
format Article
author Abdul Rahman, Teh Faradilla
Mohd Said, Raudzatul Fathiyah
Buja, Alya Geogiana
Mat Nayan, Norshita
author_facet Abdul Rahman, Teh Faradilla
Mohd Said, Raudzatul Fathiyah
Buja, Alya Geogiana
Mat Nayan, Norshita
author_sort Abdul Rahman, Teh Faradilla
title Topic modelling analysis of depression therapy text: a preliminary study / Teh Faradilla Abdul Rahman ... [et al.]
title_short Topic modelling analysis of depression therapy text: a preliminary study / Teh Faradilla Abdul Rahman ... [et al.]
title_full Topic modelling analysis of depression therapy text: a preliminary study / Teh Faradilla Abdul Rahman ... [et al.]
title_fullStr Topic modelling analysis of depression therapy text: a preliminary study / Teh Faradilla Abdul Rahman ... [et al.]
title_full_unstemmed Topic modelling analysis of depression therapy text: a preliminary study / Teh Faradilla Abdul Rahman ... [et al.]
title_sort topic modelling analysis of depression therapy text: a preliminary study / teh faradilla abdul rahman ... [et al.]
publisher UiTM Cawangan Perlis
publishDate 2024
url https://ir.uitm.edu.my/id/eprint/94363/1/94363.pdf
https://ir.uitm.edu.my/id/eprint/94363/
_version_ 1800100599437459456
score 13.211869

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