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Lexicon and Naive Bayes Algorithms to Detect Mental Health Situations from Twitter Data

Journal of Information Systems Engineering and Business Intelligence · Vol. 8 No. 2 (2022) · pp. 142–148

DOI
10.20473/jisebi.8.2.142-148
ISSN
2443-2555, 2598-6333
Published
2022-10-29
Publisher
Universitas Airlangga
Language
eng
License
creativecommons.org/licenses/by/4.0

Abstract

Background: Twitter is a popular social media where users express emotions, thoughts, and opinions that cannot be channelled in the real world. They do this by tweeting short, concise, and clear messages. Since users often express themselves, Twitter data can detect mental health trends. Objective: This study aims to detect suicidal messages through tweets written by users with mental health issues. Methods: These tweets are analysed and classified using the lexicon-based and Naive Bayes algorithms to determine whether it contains suicidal messages. Results: The classification results show that the ‘normal' classification is predominant at 52.3% of the total 3,034,826 tweets, which indicates an increase from September to December 2021. Conclusion: Most tweets are categorised as ‘normal', therefore the mental health status appears secure. However, this finding needs to be re-examined in the future, especially in DKI Jakarta Province, which has the most cases of mental disorders. This study found that the Naive Bayes algorithm is more accurate (85.5%) than the lexicon-based algorithm. This can be improved in future studies by increasing performance at the pre-processing stage. Keywords: Lexicon Based, Mental Disorder, Mental Health, Naí¯ve Bayes, Twitter

Collaboration network of the authors

8 authors · 11 co-authorship links

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  • Udayana University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Wayan Oger VihikanUdayana University2
Ni Made Tara Okta AdrianaUdayana University1
Dwi Putra GithaUdayana University1
Komang Sri Yanisa PutriAffiliation unknown1
Nyoman Tri Rahaditya KusumaAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://e-journal.unair.ac.id/JISEBI/oai, first harvested 29 Okt 2026, last seen 29 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Lexicon and Naive Bayes Algorithms to Detect Mental Health Situations from Twitter Data</dc:title>
	<dc:creator>Shevira, Sheila</dc:creator>
	<dc:creator>Suarjaya, I Made Agus Dwi </dc:creator>
	<dc:creator>Buana, Putu Wira </dc:creator>
	<dc:description xml:lang="en-US">Background: Twitter is a popular social media where users express emotions, thoughts, and opinions that cannot be channelled in the real world. They do this by tweeting short, concise, and clear messages. Since users often express themselves, Twitter data can detect mental health trends.
Objective: This study aims to detect suicidal messages through tweets written by users with mental health issues.
Methods: These tweets are analysed and classified using the lexicon-based and Naive Bayes algorithms to determine whether it contains suicidal messages.
Results: The classification results show that the ‘normal' classification is predominant at 52.3% of the total 3,034,826 tweets, which indicates an increase from September to December 2021.
Conclusion: Most tweets are categorised as ‘normal', therefore the mental health status appears secure. However, this finding needs to be re-examined in the future, especially in DKI Jakarta Province, which has the most cases of mental disorders. This study found that the Naive Bayes algorithm is more accurate (85.5%) than the lexicon-based algorithm. This can be improved in future studies by increasing performance at the pre-processing stage.
 
Keywords: Lexicon Based, Mental Disorder, Mental Health, Naí¯ve Bayes, Twitter</dc:description>
	<dc:publisher xml:lang="en-US">Universitas Airlangga</dc:publisher>
	<dc:date>2022-10-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://e-journal.unair.ac.id/JISEBI/article/view/37942</dc:identifier>
	<dc:identifier>10.20473/jisebi.8.2.142-148</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Information Systems Engineering and Business Intelligence; Vol. 8 No. 2 (2022): October; 142-148</dc:source>
	<dc:source>2443-2555</dc:source>
	<dc:source>2598-6333</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://e-journal.unair.ac.id/JISEBI/article/view/37942/22813</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 The Authors. Published by Universitas Airlangga.</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by/4.0</dc:rights>
</oai_dc:dc>