• 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