企業的目的就是創造顧客，順利創造顧客的關鍵往往取決於是否能掌握掌握溝通對象之人格特質以採用有效的溝通策略。對於企業來說，目標顧客或者潛在顧客是其需掌握人格資訊的對象，然而傳統的人格評測方式在時間與人力的成本過於高昂，且無法作到不著痕跡地掌握顧客人格資訊，因此如何能有效地對大量對象進行自動化人格預測便是值得研究的議題。近年蓬勃發展的各式社交媒體由於已成為使用者公開發表言論並與他人互動之數位平台，或有助於實現自動化人格預測之需求。
本研究以當前世上會員數最多的社交媒體網站─Facebook之使用者資料作為基礎，發展一能夠由使用者「互動行為紀錄」與「動態文章」進行人格類型預測的方法。研究中使用Marston所提出的DISC作為人格模型，並藉由設計使用者於Facebook的互動行為特徵、以文字探勘技術如TF-IDF與VSM計算使用者動態文章類型、應用正規化編輯距離以挖掘使用者互動行為相似序列等方式實現人格預測的目的。本研究亦為社交媒體互動行為建立通用模型、提出能夠有效率設計社交媒體特徵的方法以及設計並實作以群眾外包為基礎的Facebook資料蒐集機制，讓真實的Facebook使用者有效率地提供資料並協助完成訓練資料的標註。

For an enterprise, it is fundamental to win as many customers as possible. The key to successfully winning customers is often determined by understanding the personality characteristics of communication objects in order to employ an effective communications strategy. An enterprise needs to obtain the personality information of target or potential customers. However, the traditional method for personality evaluation is extremely costly because of time and labor consumption, and it is incapable of acquiring customer personality information without their awareness. Therefore, the manner in which to effectively conduct automated personality predicts for a large number of objects is an important issue. The diverse social media that have emerged in recent years have become a digital platform where users deliver their speeches publicly and interact with others. Perhaps social media can serve the needs of automated personality predicts. Based on Facebook user data, the main social media platform in the world, this research developed three methods for predicting personality types based on interactions logs and users’ statuses. In this research, Dominance, Inducement, Submission, Compliance (DISC) proposed by Marston is used as the personality model. To predict personality types, the interaction features of users were designed accordingly and calculated, and some text mining technique such as TF-IDF, VSM, and normalized edit distance were used in this research. For interactions, this research also serves to build a universal model for social media interaction, and it is used to propose an efficient method for designing interaction features; for users’s statuses, this research developed a complete mechanism based on crowdsourcing, and it could make real Facebook users provide their data and label training data efficiently.

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