用招聘广告数据自动识别行业技能需求，助力求职与培训决策。
Data to Decisions: A Computational Framework to Identify skill requirements from Advertorial Data
- 通过分析在线招聘平台文本，提取技能需求特征
- 在印度计算机与信息技术领域验证，揭示当前技能热点
- 适合求职者、培训机构及高校参考，指导职业发展与课程设计
人力资本是随技术演进而持续变化的关键生产要素。随着新技术的兴起，新一代需掌握新技能以保持就业竞争力，从业者也需不断更新技能以适应产业需求。然而，目前尚无直接方法准确识别特定时间点的行业技能需求。本文提出一种“数据到决策”框架，通过分析来自主流在线招聘平台的广告数据，结合统计分析、数据挖掘与自然语言处理技术，识别特定领域的目标技能集合。该框架在印度计算机与信息技术（CS&IT）岗位广告数据上进行了验证，结果不仅揭示了当前该行业的技能需求现状，还为潜在求职者、培训机构以及高等教育与专业训练机构提供了实际应用启示。
原文摘要 · Abstract (English)
Among the factors of production, human capital or skilled manpower is the one that keeps evolving and adapts to changing conditions and resources. This adaptability makes human capital the most crucial factor in ensuring a sustainable growth of industry/sector. As new technologies are developed and adopted, the new generations are required to acquire skills in newer technologies in order to be employable. At the same time professionals are required to upskill and reskill themselves to remain relevant in the industry. There is however no straightforward method to identify the skill needs of the industry at a given point of time. Therefore, this paper proposes a data to decision framework that can successfully identify the desired skill set in a given area by analysing the advertorial data collected from popular online job portals and supplied as input to the framework. The proposed framework uses techniques of statistical analysis, data mining and natural language processing for the purpose. The applicability of the framework is demonstrated on CS&IT job advertisement data from India. The analytical results not only provide useful insights about current state of skill needs in CS&IT industry but also provide practical implications to prospective job applicants, training agencies, and institutions of higher education & professional training.
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