隨著多媒體和網絡技術的普及，現在電子郵件往往附加高容量的的多媒體資料。不過，提供大量夾帶多媒體內容的電子郵件通過企業電子郵件系統可以很容易搞垮整個網絡的服務品質。此外，由於沒有某種形式的限制，許多企業發現，網絡資源被使用在個人利益上。在電子郵件通信業務因此受到不良的延誤和造成企業的損害。員工競相使用電子郵件服務處理個人私務，因此成為一個問題，許多企業不得不處理。顯然，企業應該管理電子郵件服務，使屬於企業的電子郵件具有優先於個人用途。管理外寄電子郵件將企業電子郵件作官方和私人電子郵件的分類，需要一個有效的方法，發展這個方法便成為本研究的目的。為了達到分類方法所需的準確度，本研究盡可能的多方研究評估相關的方法以及資訊。另一方面，監控電子郵件的內容細節，不僅會降低郵件寄送的效能，也可能侵犯在法律規範體系保護下的隱私權。因此追求在準確的分類和保護隱私的權利的平衡變成為一個挑戰。隨著討論與挑戰，本研究建立了一個電子郵件的分類方法，此方法是根據社群特徵，而不是根據電子郵件內容。在本研究所知中，本文是第一個解決上述問題的研究。本研究從電子郵件得到的社群特徵，並將其轉換為向量輸入向量的支持向量機（SVM）分類器。初步結果證明，本研究的方法具有高度的準確性。相對於其他基於郵件內容的電子郵件分類器，本研究的研究證明，在解決類似的問題上，探索社群特徵是一種很有前途的優先方向。 With the popularity of multimedia and network technologies, it is now often to attach large size of multimedia dataset to emails. However, delivering large volume of multimedia data over an enterprise email system can easily bring down the quality of overall network service. Moreover, without some sort of restrictions, many enterprises found that the network resource was occupied for personal interests. The business communication over emails thus suffers undesirable delays and cause damages to businesses. The competition to use email service therefore become an issue that many enterprises have to deal with. Obviously, enterprises should manage the email service so that business emails have the priority over personal usages. This management requires an effective methodology to classify enterprise emails into official and private emails, and the development of the method is the goal of this work. To achieve the accuracy of a desired classification methodology, we normally anticipated the developed method to survey as much information as possible. On the other hand, monitoring details of the email contents not only can decrease the performance of the method, but it also may violate the privacy rights that many legal regulation systems now protected. The balance of pursuing accurate classification and protecting privacy rights becomes a challenge for this problem. With the discussed challenges in mind, we develop an email classification method based on social features, rather than surveying the email contents. To the best of our knowledge, this paper is the first study to address the aforementioned problems. We obtain social features from emails to represent the input vector of support vector machine (SVM) classifier. Preliminary results show that our methodology can classify emails with a high accuracy. Compared with the other content-based feature of email, our work shows that exploring social features is a promising direction to solve similar prioritizing problems.
