基于机器学习的多媒体个性预测研究综述

B. Singh, Mansi Katiyar, Shefali Gupta, Nikam Gitanjali Ganpatrao
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引用次数: 1

摘要

无论是在组织的背景下还是在我们的日常生活中,对个人性格的预测在这两个领域都是一个关键问题。性格的预测取决于许多因素,这些因素可能因人而异。人格预测是通过个体在不同情况下的行为,观察个体在不同情况下的行为,来识别个体的性格。人格特征显示了不同的人基于他们的思想、感情和行为的不同特征。人格特质可以是积极的,也可以是消极的。人格特征是基于大五人格模型,也被称为OCEAN模型,即开放性、严谨性、外向性、宜人性和神经质。在之前的研究中,已经做了很多调查。他们使用不同的技术和不同的算法来预测不同人的性格。一些人使用GSC算法用笔迹来预测性格。在一些使用CNN特征的研究中使用了面部表情。很少有研究集中在社交网站上,通过检查一个人对不同帖子的反应、他们的评论、他们的帖子等来预测个性。一项研究使用AU、LF、POS、情绪特征及其组合来预测人格。除此之外,上面讨论的这些单一模型还有一些限制。它们仅对小数据集有效,但随着数据集规模的增加,它们的准确性不断下降。在这种情况下,多模态识别是有效的,为了使任务自动化,智能多模态代理可以基于语言和非语言特征更好地识别人格特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey on: Personality Prediction from Multimedia through Machine Learning
The prediction of the personality of an individual is a critical problem in both areas whether it is considered in the context of organizations or in the case of our daily lives. Prediction of personality depends on many factors and these factors may vary from one individual to another.Personality prediction is identifying the personalities of individuals through their actions in different situations and observing their behaviours in various circumstances. Personality traits show the different characteristics of different people based on their thoughts, feelings, and behaviours. There can be positive as well as negative personality traits. Personality traits are based on the Big Five Model also known as the OCEAN model i.e. Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. In the previous study, many investigations has been done. They have used different techniques and different algorithms to predict the personality of different people. Some have used handwriting to predict personality using the GSC algorithm. Facial expressions have been used in some studies using CNN features. Few studies have focused on the social networking sites for personality prediction by examining an individual’s reaction to different posts, their comments, their posts, etc. One study predicted personality using AU, LF, POS, Emotional features and their combinations. Apart from this, there are few limitations in these single models discussed above. They work efficiently for only a small dataset but on increasing the size of the dataset their accuracy keeps decreasing. Multimodal is effective in this case and to make the task automatic, an intelligent multimodal agent can identify personality traits better based on both verbal and non-verbal features.
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