Harnessing Natural Language Processing for Mental Health Detection in Malay Text: A Review

This paper comprehensively examines mental health detection in the Malay language using natural language processing (NLP) techniques. With global implications, mental health holds significant importance in Malay-speaking regions. NLP, a specialised branch of artificial intelligence, shows promise in...

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Published in:2023 4th International Conference on Artificial Intelligence and Data Sciences: Discovering Technological Advancement in Artificial Intelligence and Data Science, AiDAS 2023 - Proceedings
Main Author: Ahmad Z.; Maskat R.; Mohamed A.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176607750&doi=10.1109%2fAiDAS60501.2023.10284653&partnerID=40&md5=cef790f8f41322ea5a792e3cf90c4c20
id 2-s2.0-85176607750
spelling 2-s2.0-85176607750
Ahmad Z.; Maskat R.; Mohamed A.
Harnessing Natural Language Processing for Mental Health Detection in Malay Text: A Review
2023
2023 4th International Conference on Artificial Intelligence and Data Sciences: Discovering Technological Advancement in Artificial Intelligence and Data Science, AiDAS 2023 - Proceedings


10.1109/AiDAS60501.2023.10284653
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176607750&doi=10.1109%2fAiDAS60501.2023.10284653&partnerID=40&md5=cef790f8f41322ea5a792e3cf90c4c20
This paper comprehensively examines mental health detection in the Malay language using natural language processing (NLP) techniques. With global implications, mental health holds significant importance in Malay-speaking regions. NLP, a specialised branch of artificial intelligence, shows promise in deciphering mental health issues from text data. The review begins by exploring traditional NLP approaches like word frequencies, illustrating their role in mental health research. It then focuses on advanced techniques such as embeddings, neural networks, and transformer-based language models. The paper discusses prevalent mental health disorders in Malay-speaking communities and the challenges in their detection. It also highlights distinctive features of Malay mental health datasets crucial for NLP model development. The review delves into studies utilising NLP to analyse mental health content across social media and online forums in Malay contexts. These studies' methods, findings, and limitations are detailed, demonstrating NLP's potential in identifying mental health problems on a larger scale. The review emphasises NLP's role in Malay mental health detection and underscores the need for ongoing research. Leveraging NLP techniques can provide deep insights into the Malay-speaking mental health scenario, facilitating effective interventions and support for individuals with such challenges. © 2023 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper

author Ahmad Z.; Maskat R.; Mohamed A.
spellingShingle Ahmad Z.; Maskat R.; Mohamed A.
Harnessing Natural Language Processing for Mental Health Detection in Malay Text: A Review
author_facet Ahmad Z.; Maskat R.; Mohamed A.
author_sort Ahmad Z.; Maskat R.; Mohamed A.
title Harnessing Natural Language Processing for Mental Health Detection in Malay Text: A Review
title_short Harnessing Natural Language Processing for Mental Health Detection in Malay Text: A Review
title_full Harnessing Natural Language Processing for Mental Health Detection in Malay Text: A Review
title_fullStr Harnessing Natural Language Processing for Mental Health Detection in Malay Text: A Review
title_full_unstemmed Harnessing Natural Language Processing for Mental Health Detection in Malay Text: A Review
title_sort Harnessing Natural Language Processing for Mental Health Detection in Malay Text: A Review
publishDate 2023
container_title 2023 4th International Conference on Artificial Intelligence and Data Sciences: Discovering Technological Advancement in Artificial Intelligence and Data Science, AiDAS 2023 - Proceedings
container_volume
container_issue
doi_str_mv 10.1109/AiDAS60501.2023.10284653
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176607750&doi=10.1109%2fAiDAS60501.2023.10284653&partnerID=40&md5=cef790f8f41322ea5a792e3cf90c4c20
description This paper comprehensively examines mental health detection in the Malay language using natural language processing (NLP) techniques. With global implications, mental health holds significant importance in Malay-speaking regions. NLP, a specialised branch of artificial intelligence, shows promise in deciphering mental health issues from text data. The review begins by exploring traditional NLP approaches like word frequencies, illustrating their role in mental health research. It then focuses on advanced techniques such as embeddings, neural networks, and transformer-based language models. The paper discusses prevalent mental health disorders in Malay-speaking communities and the challenges in their detection. It also highlights distinctive features of Malay mental health datasets crucial for NLP model development. The review delves into studies utilising NLP to analyse mental health content across social media and online forums in Malay contexts. These studies' methods, findings, and limitations are detailed, demonstrating NLP's potential in identifying mental health problems on a larger scale. The review emphasises NLP's role in Malay mental health detection and underscores the need for ongoing research. Leveraging NLP techniques can provide deep insights into the Malay-speaking mental health scenario, facilitating effective interventions and support for individuals with such challenges. © 2023 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
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language English
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