Publication:
Authorship attribution analysis of thai online messages

dc.contributor.authorRangsipan Marukataten_US
dc.contributor.authorRobroo Somkiadcharoenen_US
dc.contributor.authorRatthanan Nalintasnaien_US
dc.contributor.authorTappasarn Aramboonpongen_US
dc.contributor.otherMahidol Universityen_US
dc.date.accessioned2018-11-09T02:09:37Z
dc.date.available2018-11-09T02:09:37Z
dc.date.issued2014-01-01en_US
dc.description.abstractThis paper presents a framework to identify the authors of Thai online messages. The identification is based on 53 writing attributes and the selected algorithms are support vector machine (SVM) and C4.5 decision tree. Experimental results indicate that the overall accuracies achieved by the SVM and the C4.5 were 79% and 75%, respectively. This difference was not statistically significant (at 95% confidence interval). As for the performance of identifying individual authors, in some cases the SVM was clearly better than the C4.5. But there were also other cases where both of them could not distinguish one author from another. © 2014 IEEE.en_US
dc.identifier.citationICISA 2014 - 2014 5th International Conference on Information Science and Applications. (2014)en_US
dc.identifier.doi10.1109/ICISA.2014.6847369en_US
dc.identifier.other2-s2.0-84904490792en_US
dc.identifier.urihttps://repository.li.mahidol.ac.th/handle/123456789/33691
dc.rightsMahidol Universityen_US
dc.rights.holderSCOPUSen_US
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84904490792&origin=inwarden_US
dc.subjectComputer Scienceen_US
dc.titleAuthorship attribution analysis of thai online messagesen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
mu.datasource.scopushttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84904490792&origin=inwarden_US

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