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An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning

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dc.contributor.authorSharif, Waqas
dc.contributor.authorMumtaz, Shahzad
dc.contributor.authorShafiq, Zubair
dc.contributor.authorRiaz, Omer
dc.contributor.authorAli, Tenvir
dc.contributor.authorHusnain, Mujtaba
dc.contributor.authorChoi, Guy San
dc.date.accessioned2024-03-11T13:57:00Z
dc.date.available2024-03-11T13:57:00Z
dc.date.issued2019-09-06
dc.descriptionThis research was supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program. No.10063130, Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1A2C1006159), and MSIT(Ministry of Science and ICT), Korea, under the ITRC(Information Technology Research Center) support program (IITP-2019-2016-0-00313) supervised by the IITP (Institute for Information & communications Technology Promotion), and the 2018 Yeungnam University Research Grant.en
dc.description.statusPeer revieweden
dc.format.extent20
dc.format.extent2421573
dc.identifier281445639
dc.identifier75e56381-7424-4a87-9fdc-a5aa8124e594
dc.identifier85072407040
dc.identifier.citationSharif, W, Mumtaz, S, Shafiq, Z, Riaz, O, Ali, T, Husnain, M & Choi, G S 2019, 'An Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learning', Applied Sciences, vol. 9, no. 18, 3723. https://doi.org/10.3390/app9183723en
dc.identifier.doi10.3390/app9183723
dc.identifier.iss18en
dc.identifier.issn2076-3417
dc.identifier.othercrossref: 10.3390/app9183723
dc.identifier.otherORCID: /0000-0003-2606-2405/work/143115729
dc.identifier.urihttps://hdl.handle.net/2164/22959
dc.identifier.urlhttps://www.mdpi.com/2076-3417/9/18/3723en
dc.identifier.vol9en
dc.language.isoeng
dc.relation.ispartofApplied Sciencesen
dc.subjectSDG 16 - Peace, Justice and Strong Institutionsen
dc.subjectcyber-extremeen
dc.subjectTwitteren
dc.subjectexploratory data analysisen
dc.subjectprincipal component analysisen
dc.subjectterm frequency—inverse document frequencyen
dc.subjectsupport vector machineen
dc.subjectensemble classificationen
dc.subjectQA75 Electronic computers. Computer scienceen
dc.subject.lccQA75en
dc.titleAn Empirical Approach for Extreme Behavior Identification through Tweets Using Machine Learningen
dc.typeJournal articleen

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