基于属性的人格检测计算方法

R. Rastogi, D. Chaturvedi, M. Gupta
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引用次数: 1

摘要

心理学家试图通过多种方法来衡量人格以分析人类的行为,这些方法有自我增强(用幽默来增强自我)、附属(用幽默来增强与他人的关系)、攻击性(用幽默来增强他人的利益)、自我挫败(用幽默来增强与他人的关系而牺牲自己)。本章的目的是启发人格检测测试在学术、就业、群体互动和自我反思中的应用。本章提供了使用多媒体和物联网来检测个性和分析不同的人类行为。它还包括大数据的概念,用于存储和处理通过物联网分析个性时产生的数据。结果表明,线性回归和多元线性回归的预测效果最好,可以实现对个体性格的预测。与其他模型相比,决策树回归模型的准确率最低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computational Approach for Personality Detection on Attributes
Psychologists seek to measure personality to analyze the human behavior through a number of methods, which are self-enhancing (humor use to enhance self), affiliative (humor use to enhance the relationship with other), aggressive (humor use to enhance the self at the expense of others), self-defeating (the humor use to enhance relationships at the expense of self). The purpose of this chapter is to enlighten the use of personality detection test in academics, job placement, group-interaction, and self-reflection. This chapter provides the use of multimedia and IoT to detect the personality and to analyze the different human behaviors. It also includes the concept of big data for the storage and processing the data that will be generated while analyzing the personality through IoT. Linear regression and multiple linear regression are proved to be the best, so they can be used to implement the prediction of personality of individuals. Decision tree regression model has achieved minimum accuracy in comparison to others.
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