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A Generalized Email Classification System for Workflow Analysis

Author

Listed:
  • Piyanuch Chaipornkaew

    (College of Innovative Technology and Engineering Dhurakij Pundit UniversityBangkok, Thailand.)

  • Takorn Prexawanprasut

    ( College of Innovative Technology and Engineering Dhurakij Pundit University Bangkok, Thailand.)

  • Chia-Lin Chang

    ( Department of Applied Economics Department of FinanceNational Chung Hsing University Taichung, Taiwan.)

  • Michael McAleer

    ( Department of Quantitative Finance National Tsing Hua University, Taiwan and Econometric Institute Erasmus School of Economics Erasmus University Rotterdam, The Netherlands and Department of Quantitative Economics Complutense University of Madrid, Spain And Institute of Advanced Sciences Yokohama National University, Japan.)

Abstract
One of the most powerful internet communication channels is email. As employees and their clients communicate primarily via email, much crucial business data is conveyed via email content. Where businesses are understandably concerned, they need a sophisticated workflow management system to manage their transactions. A workflow management system should also be able to classify any incoming emails into suitable categories. Previous research has implemented a system to categorize emails based on the words found in email messages. Two parameters affected the accuracy of the program, namely the number of words in a database compared with sample emails, and an acceptable percentage for classifying emails. As the volume of email has become larger and more sophisticated, this research classifies email messages into a larger number of categories and changes a parameter that affects the accuracy of the program. The first parameter, namely the number of words in a database compared with sample emails, remains unchanged, while the second parameter is changed from an acceptable percentage to the number of matching words. The empirical results suggest that the number of words in a database compared with sample emails is 11, and the number of matching words to categorize emails is 7. When these settings are applied to categorize 12,465 emails, the accuracy of this experiment is approximately 65.3%. The optimal number of words that yields high accuracy levels lies between 11 and 13, while the number of matching words lies between 6 and 8.

Suggested Citation

  • Piyanuch Chaipornkaew & Takorn Prexawanprasut & Chia-Lin Chang & Michael McAleer, 2017. "A Generalized Email Classification System for Workflow Analysis," Documentos de Trabajo del ICAE 2017-21, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    • Handle: RePEc:ucm:doicae:1721
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    File URL: https://eprints.ucm.es/id/eprint/44630/1/1721.pdf
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    References listed on IDEAS

    as
    1. Piyanuch Chaipornkaew & Takorn Prexawanprasut & Michael McAleer, 2017. "You’ve Got Email: A Workflow Management Extraction System," Journal of Reviews on Global Economics, Lifescience Global, vol. 6, pages 342-349.
    2. Chaipornkaew, P. & Prexawanprasut, T. & McAleer, M.J., 2017. "You’ve Got Email: a Workflow Management Extraction System," Econometric Institute Research Papers EI2017-11, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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      More about this item

      Keywords

      Email; business data; workflow management system; business transactions.;
      All these keywords.

      JEL classification:

      • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
      • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
      • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
      • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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