Publication: Authorship attribution analysis of thai online messages
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2014-01-01
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2-s2.0-84904490792
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Mahidol University
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SCOPUS
Bibliographic Citation
ICISA 2014 - 2014 5th International Conference on Information Science and Applications. (2014)
Suggested Citation
Rangsipan Marukatat, Robroo Somkiadcharoen, Ratthanan Nalintasnai, Tappasarn Aramboonpong Authorship attribution analysis of thai online messages. ICISA 2014 - 2014 5th International Conference on Information Science and Applications. (2014). doi:10.1109/ICISA.2014.6847369 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/33691
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Title
Authorship attribution analysis of thai online messages
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Abstract
This 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.