将文本特征与生存分析相结合,用于预测员工周转率
Behavioral sciences (Basel, Switzerland)
|February 27, 2026
概括
通过将专业网络文本分析与人口统计数据相结合,可以更好地预测员工流动. 这种新的方法提高了人力资源 (HR) 决策和劳动力规划的准确性.
科学领域:
- 人力资源管理 人力资源管理
- 数据科学数据科学数据科学
- 组织行为 组织行为
背景情况:
- 员工流动给组织带来了巨大的成本.
- 传统的营业额预测模型往往缺乏对员工情绪和时间动态的细微见解.
- 专业网络平台提供丰富的文本数据,可用于预测建模.
研究的目的:
- 开发和验证一种用于预测员工流动的新方法.
- 将基于变压器的文本分析与使用生存分析的人口变量集成.
- 提高人力资源决策的营业额预测的准确性和可解释性.
主要方法:
- 利用了2020-2022年Maimai (中国专业网络平台) 的4087个工作事件的数据集.
- 采用混合特征提取策略,结合情绪分析,TF-IDF和基于Transformer的深度学习语义表示.
- 应用生存分析来建模时间依赖的营业额风险,并比较各种预测模型.
主要成果:
- 整合文本和人口特征显著改善了预测性能,C指数增加了3.38%,累计/动态AUC增加了3.43%.
- 与传统方法相比,基于变压器的文本分析在捕捉微妙的员工情绪方面表现出卓越的表现.
- 生存分析通过结合时间动态和确定可解释的营业额风险因素,提高了模型的适应性.
结论:
- 这种新的方法有效地将先进的文本分析与生存建模相结合,用于优越的营业额预测.
- 这种方法为中小企业提供了一个实用的,基于数据的工具,用于计划员工和留住人才.
- 调查结果有助于对劳动力市场的深入了解,为人才管理的组织策略和政策制定提供信息.
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