An Exploratory Approach to Discovering Salary-Related Wording in Job Postings in Korea
| Autor: | Taehyun Ha, Byoung-Youl Coh, Mingook Lee, Bitnari Yun, Hong-Woo Chun |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Journal of Information Science Theory and Practice, Vol 10, Iss special (2022) |
| Druh dokumentu: | article |
| ISSN: | 2287-9099 2287-4577 |
| DOI: | 10.1633/JISTaP.2022.10.S.9 |
| Popis: | Online recruitment websites discuss job demands in various fields, and job postings contain detailed job specifications. Analyzing this text can elucidate the features that determine job salaries. Text embedding models can learn the contextual information in a text, and explainable artificial intelligence frameworks can be used to examine in detail how text features contribute to the models’ outputs. We collected 733,625 job postings using the WORKNET API and classified them into low, mid, and high-range salary groups. A text embedding model that predicts job salaries based on the text in job postings was trained with the collected data. Then, we applied the SHapley Additive exPlanations (SHAP) framework to the trained model and discovered the significant words that determine each salary class. Several limitations and remaining words are also discussed. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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