TEXT CLASSIFICATION FOR SUICIDE RELATED TWEETS

被引：0

作者：

Chiroma, Fatima ^{[1
]}

Liu, Han ^{[2
]}

Cocea, Mihaela ^{[1
]}

机构：

[1] Univ Portsmouth, Sch Comp, Portsmouth, Hants, England

[2] Cardiff Univ, Sch Comp Sci & Informat, Cardiff, Wales

来源：

PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 2 | 2018年

关键词：

Text classification; Machine classifier; Social media; Suicide; SENTIMENT; TWITTER; USERS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：587 / 592

页数：6

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