A Large-scale Industrial and Professional Occupation Dataset
This dataset addresses a bottleneck for researchers and companies in occupational data mining, though it is incremental as it primarily provides a new resource rather than a methodological breakthrough.
The authors tackled the lack of publicly available occupational datasets by creating the Industrial and Professional Occupation Dataset (IPOD), which includes 192k job titles from 56k LinkedIn users with manual annotations for seniority, domain, and location.
There has been growing interest in utilizing occupational data mining and analysis. In today's job market, occupational data mining and analysis is growing in importance as it enables companies to predict employee turnover, model career trajectories, screen through resumes and perform other human resource tasks. A key requirement to facilitate these tasks is the need for an occupation-related dataset. However, most research use proprietary datasets or do not make their dataset publicly available, thus impeding development in this area. To solve this issue, we present the Industrial and Professional Occupation Dataset (IPOD), which comprises 192k job titles belonging to 56k LinkedIn users. In addition to making IPOD publicly available, we also: (i) manually annotate each job title with its associated level of seniority, domain of work and location; and (ii) provide embedding for job titles and discuss various use cases. This dataset is publicly available at https://github.com/junhua/ipod.