2025/10/01 by Stephen Meisenbacher, Meisenbacher, Stephen, Svetlozar Nestorov +3 · 1 citation
Computer Science · #Advanced Computational Techniques and Applications #Computation and Language (cs.CL) #Computers and Society (cs.CY) #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining
paper · doi:10.48550/arxiv.2510.01470
openalex publication_date 2025/10/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Data from online job postings are difficult to access and are not built in a standard or transparent manner. Data included in the standard taxonomy and occupational information database (O*NET) are updated infrequently and based on small survey samples. We adopt O*NET as a framework for building natural language processing tools that extract structured information from job postings. We publish the Job Ad Analysis Toolkit (JAAT), a collection of open-source tools built for this purpose, and demonstrate its reliability and accuracy in out-of-sample and LLM-as-a-Judge testing. We extract more than 10 billion data points from more than 155 million online job ads provided by the National Labor Exchange (NLx) Research Hub, including O*NET tasks, occupation codes, tools, and technologies, as well as wages, skills, industry, and more features. We describe the construction of a dataset of occupation, state, and industry level features aggregated by monthly active jobs from 2015 - 2025. We illustrate the potential for research and future uses in education and workforce development.