Understanding Malaysian Public Opinion on Suicide through Sentiment Analysis and Topic Modeling of Reddit Posts

Suicide is a global public health concern, with the World Health Organization (WHO) identifying it as the second leading cause of death among individuals aged from 15 to 29 years. In Malaysia, recent statistics indicate a 10% increase in suicide cases in 2023 compared to the previous year. Online fo...

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Published in:Engineering, Technology and Applied Science Research
Main Author: Kamaruddin S.S.; Abdul-Rahman S.; Wibowo W.
Format: Article
Language:English
Published: Dr D. Pylarinos 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211465078&doi=10.48084%2fetasr.8738&partnerID=40&md5=725120b6d732e38b25b301159856b6e8
id 2-s2.0-85211465078
spelling 2-s2.0-85211465078
Kamaruddin S.S.; Abdul-Rahman S.; Wibowo W.
Understanding Malaysian Public Opinion on Suicide through Sentiment Analysis and Topic Modeling of Reddit Posts
2024
Engineering, Technology and Applied Science Research
14
6
10.48084/etasr.8738
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211465078&doi=10.48084%2fetasr.8738&partnerID=40&md5=725120b6d732e38b25b301159856b6e8
Suicide is a global public health concern, with the World Health Organization (WHO) identifying it as the second leading cause of death among individuals aged from 15 to 29 years. In Malaysia, recent statistics indicate a 10% increase in suicide cases in 2023 compared to the previous year. Online forums, such as Reddit, have become platforms for sharing opinions on this matter. Extracting and automatically analyzing these discussions can provide valuable insights into public opinion concerning this issue. While the existing research primarily focuses on identifying suicidal ideation from posts, the work on discerning public opinion remains limited. This study scraped opinion posts on suicide from the Malaysian Reddit community. Sentiment Analysis (SA) was conducted using a lexicon-based sentiment analyzer and topic modeling was performed by deploying Latent Dirichlet Allocation (LDA). The analysis revealed the following insights: (1) predominantly negative sentiments were detected in the opinions, both overall and within identified topics and (2) topic modeling indicated two distinctive topics reflecting the different perspectives and concerns of the Muslim and non-Muslim communities. Specifically, the overall SA revealed that 58% of the posts were negative, 10% were neutral, and 32% were positive. Within the identified topics, the Muslim community expressed a notably higher percentage of negative sentiments at 64%, compared to the 50% found in the non-Muslim community. These findings offer evidence-based insights into public opinion regarding suicide in Malaysia, contributing to the understanding of societal perspectives on this critical issue. © by the authors.
Dr D. Pylarinos
22414487
English
Article
All Open Access; Gold Open Access
author Kamaruddin S.S.; Abdul-Rahman S.; Wibowo W.
spellingShingle Kamaruddin S.S.; Abdul-Rahman S.; Wibowo W.
Understanding Malaysian Public Opinion on Suicide through Sentiment Analysis and Topic Modeling of Reddit Posts
author_facet Kamaruddin S.S.; Abdul-Rahman S.; Wibowo W.
author_sort Kamaruddin S.S.; Abdul-Rahman S.; Wibowo W.
title Understanding Malaysian Public Opinion on Suicide through Sentiment Analysis and Topic Modeling of Reddit Posts
title_short Understanding Malaysian Public Opinion on Suicide through Sentiment Analysis and Topic Modeling of Reddit Posts
title_full Understanding Malaysian Public Opinion on Suicide through Sentiment Analysis and Topic Modeling of Reddit Posts
title_fullStr Understanding Malaysian Public Opinion on Suicide through Sentiment Analysis and Topic Modeling of Reddit Posts
title_full_unstemmed Understanding Malaysian Public Opinion on Suicide through Sentiment Analysis and Topic Modeling of Reddit Posts
title_sort Understanding Malaysian Public Opinion on Suicide through Sentiment Analysis and Topic Modeling of Reddit Posts
publishDate 2024
container_title Engineering, Technology and Applied Science Research
container_volume 14
container_issue 6
doi_str_mv 10.48084/etasr.8738
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211465078&doi=10.48084%2fetasr.8738&partnerID=40&md5=725120b6d732e38b25b301159856b6e8
description Suicide is a global public health concern, with the World Health Organization (WHO) identifying it as the second leading cause of death among individuals aged from 15 to 29 years. In Malaysia, recent statistics indicate a 10% increase in suicide cases in 2023 compared to the previous year. Online forums, such as Reddit, have become platforms for sharing opinions on this matter. Extracting and automatically analyzing these discussions can provide valuable insights into public opinion concerning this issue. While the existing research primarily focuses on identifying suicidal ideation from posts, the work on discerning public opinion remains limited. This study scraped opinion posts on suicide from the Malaysian Reddit community. Sentiment Analysis (SA) was conducted using a lexicon-based sentiment analyzer and topic modeling was performed by deploying Latent Dirichlet Allocation (LDA). The analysis revealed the following insights: (1) predominantly negative sentiments were detected in the opinions, both overall and within identified topics and (2) topic modeling indicated two distinctive topics reflecting the different perspectives and concerns of the Muslim and non-Muslim communities. Specifically, the overall SA revealed that 58% of the posts were negative, 10% were neutral, and 32% were positive. Within the identified topics, the Muslim community expressed a notably higher percentage of negative sentiments at 64%, compared to the 50% found in the non-Muslim community. These findings offer evidence-based insights into public opinion regarding suicide in Malaysia, contributing to the understanding of societal perspectives on this critical issue. © by the authors.
publisher Dr D. Pylarinos
issn 22414487
language English
format Article
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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