Text classification for suicide related tweets

Chiroma, Fatima, Liu, Han ORCID: https://orcid.org/0000-0002-7731-8258 and Cocea, Mihaela 2018. Text classification for suicide related tweets. Presented at: International Conference on Machine Learning and Cybernetics (ICMLC 2018), Chengdu, China, 15-18 July 2018. 2018 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, pp. 587-592. 10.1109/ICMLC.2018.8527039

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Abstract

Online social networks have become a vital medium for communication. With these platforms, users have the freedom to share their opinions as well as receive information from a diverse group of people. Although this could be beneficial, there are some growing concerns regarding its negative impact on the safety of its users such as the spread of suicidal ideation. Therefore, in this study, we aim to determine the performance of machine classifiers in identifying suicide-related text from Twitter (tweets). The experiment for the study was conducted using four popular machine classifiers: Decision Tree, Naive Bayes, Random Forest and Support Vector Machine. The results of the experiment showed an F-measure ranging from 0.346 to 0.778 for suicide-related communication, with the best performance being achieved using the Decision Tree classifier.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	IEEE
ISBN:	978-1-5386-5214-5
Funders:	Department of Health Policy Research Programme, Petroleum Technology Development Fund
Related URLs:	Organisation
Date of First Compliant Deposit:	6 July 2018
Date of Acceptance:	17 May 2018
Last Modified:	25 Oct 2022 13:25
URI:	https://orca.cardiff.ac.uk/id/eprint/119817

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• Text classification for suicide related tweets. (deposited 06 Jul 2018 10:16)
  • Text classification for suicide related tweets. (deposited 15 May 2019 10:30) [Currently Displayed]

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