Social feature-based enterprise email classification without examining email contents

  • Authors:

  • Min-Feng Wang;Meng-Feng Tsai;Sie-Long Jheng;Cheng-Hsien Tang

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Central University, No. 300, Jhongda Road, Jhongli 32001, Taiwan, ROC;Department of Computer Science and Information Engineering, National Central University, No. 300, Jhongda Road, Jhongli 32001, Taiwan, ROC;Department of Computer Science and Information Engineering, National Central University, No. 300, Jhongda Road, Jhongli 32001, Taiwan, ROC;Department of Computer Science and Information Engineering, National Central University, No. 300, Jhongda Road, Jhongli 32001, Taiwan, ROC

  • Venue:
  • Journal of Network and Computer Applications
  • Year:
  • 2012

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Abstract

Without imposing restrictions, many enterprises find nonwork-related contents consuming network resources. Business communication over emails thus incurs undesired delays and inflicts damages to businesses, explaining why many enterprises are concerned with the competition to use email services. Obviously, enterprises should prioritize business emails over personal ones in their email service. Therefore, previous works present content-based classification methods to categorize enterprise emails into business or personal correspondence. Accuracy of these methods is largely determined by their ability to survey as much information as possible. However, in addition to decreasing the performance of these methods, monitoring the details of email contents may violate privacy rights that are under legal protection, requiring a careful balance of accurately classifying enterprise emails and protecting privacy rights. The proposed email classification method is thus based on social features rather than a survey of emails contents. Social-based metrics are also designed to characterize emails as social features; the obtained features are treated as an input of machine learning-based classifiers for email classification. Experimental results demonstrate the high accuracy of the proposed method in classifying emails. In contrast with other content-based methods that examine email contents, the emphasis on social features in the proposed method is a promising alternative for solving similar email classification problems.