利用IndoBERT深度学习模型识别外向性和神经质人格维度

N. Dudija, Lezia Natalia, A. Alamsyah, A. Romadhony
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引用次数: 3

摘要

人力资源是企业组织适应变化的必要条件。确定新人才的个性维度可以帮助招聘人员进行匹配技能人才与组织需求的选择过程。本研究的目的是确定与工作需求相对应的人格维度,这些维度与外向性和神经质相关。确定人格维度的传统方法是通过访谈或问卷调查,但这个过程成本高,需要更长的时间来完成。本文提出了一项基于社交媒体文本作为补充方法论的人格识别工作。我们利用文本数据来支持识别新的人才人格维度。在这项研究中,我们使用IndoBERT模型来捕捉基于Twitter社交媒体上的人的个性维度。结果表明,该模型识别外向性和神经质人格维度的准确率达到96%。我们还将我们的结果与先前基于本体模型的工作进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Extraversion and Neuroticism Personality Dimensions Using IndoBERT’s Deep Learning Model
Human resources are essential for the business organization to adapt to change. Identifying the personality dimensions of new talent could help recruiters conduct the selection process of matching skilled talent to the organization’s needs. The objective of this study is to identify the personality dimensions corresponding to the job need, which correlates with extraversion and neuroticism. The legacy methodology to determine personality dimensions is through interviews or questionnaire surveys, but this process is costly and takes longer time to complete. This paper proposes a work on a person personality identification based on social media text as a complementary methodology. We utilize the textual data to support identifying new talent personality dimensions. In this study, we use IndoBERT model to capture person personality dimension based on their post on Twitter social media. As a result, our model achieves 96% accuracy in identifying extraversion and neuroticism personality dimensions. We also compare our result with the previous work based on the ontology model.
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