A Survey of Psychological Personality Classification Approaches

Mervat Ragab Bakry, Mona M. Nasr, Fahad Kamal Al-sheref
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Abstract

Online social networks (OSNs) have become essential ways for users to socially share information and feelings, communicate, and thoughts with others through online social networks. Online social networks such as Twitter and Facebook are some of the most common OSNs among users. Users’ behaviors on social networks aid researchers for detecting and understanding their online behaviors and personality traits. Personality detection is one of the new difficulties in social networks. Machine learning techniques are used to build models for understanding personality, detecting personality traits, and classifying users into different kinds through user generated content based on different features and measures of psychological models such as PEN (Psychoticism, Extraversion, and Neuroticism) model, DISC (Dominance, Influence, Steadiness, and Compliance) model, and the Big-five model (Openness, Extraversion, Consciousness, Agreeableness, and Neurotic) which is the most accepted model of personality. This survey discusses the existing works on psychological personality classification.
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心理人格分类方法综述
在线社交网络(Online social network,简称osn)已经成为用户通过在线社交网络与他人分享信息和感受、交流思想的重要方式。Twitter和Facebook等在线社交网络是用户最常用的osn。用户在社交网络上的行为有助于研究人员发现和理解他们的在线行为和人格特征。人格检测是社交网络的新难点之一。基于PEN (Psychoticism, Extraversion, Neuroticism)模型、DISC (Dominance, Influence, Steadiness, and Compliance)模型、Big-five (Openness, Extraversion, Consciousness, Agreeableness)模型等心理模型的不同特征和度量,利用机器学习技术构建理解人格、检测人格特征的模型,并通过用户生成的内容将用户分类为不同的类型。和神经质),这是最被接受的人格模型。本文对现有的心理人格分类研究进行了综述。
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