• DocumentCode
    145601
  • Title

    Text Message Authorship Classification Using Kernel Support Vector Machines

  • Author

    Kretchmar, Matt ; Yifu Zhao

  • Author_Institution
    Dept. of Math. & Comput. Sci., Denison Univ., Granville, OH, USA
  • Volume
    2
  • fYear
    2014
  • fDate
    10-13 March 2014
  • Firstpage
    215
  • Lastpage
    218
  • Abstract
    We explore the application of Kernel Support Vector Machines (SVM) to the realm of text messages. Our intent is to classify the author of a text message based on usage patterns present in a training set of text messages. We achieve between 57% and 96% accuracy in determining the author of unknown samples.
  • Keywords
    electronic messaging; pattern classification; support vector machines; SVM; kernel support vector machines; text message authorship classification; text message training set; usage patterns; Accuracy; Educational institutions; Kernel; Support vector machines; Testing; Training; Vectors; Classification; Machine Learning Applications; NLP; SVM; Text Messages;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Science and Computational Intelligence (CSCI), 2014 International Conference on
  • Conference_Location
    Las Vegas, NV
  • Type

    conf

  • DOI
    10.1109/CSCI.2014.121
  • Filename
    6822332
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=145601