DocumentCode
169648
Title
Authorship Attribution Analysis of Thai Online Messages
Author
Marukatat, Rangsipan ; Somkiadcharoen, Robroo ; Nalintasnai, Ratthanan ; Aramboonpong, Tappasarn
Author_Institution
Dept. of Comput. Eng., Mahidol Univ., Nakhon Pathom, Thailand
fYear
2014
fDate
6-9 May 2014
Firstpage
1
Lastpage
4
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.
Keywords
decision trees; natural language processing; support vector machines; C4.5 decision tree; SVM; Thai online messages; author identification; authorship attribution analysis; support vector machine; writing attributes; Accuracy; Decision trees; Kernel; Support vector machines; Training; Training data; Writing;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Science and Applications (ICISA), 2014 International Conference on
Conference_Location
Seoul
Print_ISBN
978-1-4799-4443-9
Type
conf
DOI
10.1109/ICISA.2014.6847369
Filename
6847369
Link To Document