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Suicide related text classification with prism algorithm

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Raw but valuable user data is continuously being generated on social media platforms. This data is, however, more valuable when they are mined using different approaches such as machine learning techniques. Additionally, this user-generated data can be used to potentially save lives especially of vulnerable social media users, as several studies carried out have shown the correlation between social media and suicide. In this study, we aim at contributing to the research relating to suicide communication on social media. We measured the performance of five machine learning algorithms: Prism, Decision Tree, Naive Bayes, Random Forest and Support Vector Machine, in classifying suicide-related text from Twitter. The results of the study showed that the Prism algorithm has outperformed the other machine learning algorithms with an F-measure of 0.84 for the target classes (Suicide and Flippant). This result, to the best of our knowledge, is the highest performance that has been achieved in classifying social media suicide-related text.
Original languageEnglish
Title of host publicationProceedings of the 2018 International Conference on Machine Learning and Cybernetics (ICMLC)
PublisherIEEE
Pages575-580
Number of pages6
Volume2
ISBN (Electronic)978-1-5386-5214-5
ISBN (Print)978-1-5386-5215-2
DOIs
Publication statusPublished - 12 Nov 2018
Event2018 International Conference on Machine Learning and Cybernetics - http://www.icmlc.com/icmlc/welcome.html, Chengdu, China
Duration: 15 Jul 201818 Jul 2018

Publication series

NameInternational Conference on Machine Learning and Cybernetics (ICMLC)
PublisherIEEE
ISSN (Print)2160-133X
ISSN (Electronic)2160-1348

Conference

Conference2018 International Conference on Machine Learning and Cybernetics
Abbreviated titleICMLC 2018
CountryChina
CityChengdu
Period15/07/1818/07/18

Documents

  • ICMLC22018

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    Accepted author manuscript (Post-print), 179 KB, PDF document

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