{"title":"纳斯达克 100 强公司的招聘启示:基于主题的劳动力市场分类方法","authors":"Seyed Mohammad Ali Jafari, Ehsan Chitsaz","doi":"arxiv-2409.00658","DOIUrl":null,"url":null,"abstract":"The emergence of new and disruptive technologies makes the economy and labor\nmarket more unstable. To overcome this kind of uncertainty and to make the\nlabor market more comprehensible, we must employ labor market intelligence\ntechniques, which are predominantly based on data analysis. Companies use job\nposting sites to advertise their job vacancies, known as online job vacancies\n(OJVs). LinkedIn is one of the most utilized websites for matching the supply\nand demand sides of the labor market; companies post their job vacancies on\ntheir job pages, and LinkedIn recommends these jobs to job seekers who are\nlikely to be interested. However, with the vast number of online job vacancies,\nit becomes challenging to discern overarching trends in the labor market. In\nthis paper, we propose a data mining-based approach for job classification in\nthe modern online labor market. We employed structural topic modeling as our\nmethodology and used the NASDAQ-100 indexed companies' online job vacancies on\nLinkedIn as the input data. We discover that among all 13 job categories,\nMarketing, Branding, and Sales; Software Engineering; Hardware Engineering;\nIndustrial Engineering; and Project Management are the most frequently posted\njob classifications. This study aims to provide a clearer understanding of job\nmarket trends, enabling stakeholders to make informed decisions in a rapidly\nevolving employment landscape.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nasdaq-100 Companies' Hiring Insights: A Topic-based Classification Approach to the Labor Market\",\"authors\":\"Seyed Mohammad Ali Jafari, Ehsan Chitsaz\",\"doi\":\"arxiv-2409.00658\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The emergence of new and disruptive technologies makes the economy and labor\\nmarket more unstable. To overcome this kind of uncertainty and to make the\\nlabor market more comprehensible, we must employ labor market intelligence\\ntechniques, which are predominantly based on data analysis. Companies use job\\nposting sites to advertise their job vacancies, known as online job vacancies\\n(OJVs). LinkedIn is one of the most utilized websites for matching the supply\\nand demand sides of the labor market; companies post their job vacancies on\\ntheir job pages, and LinkedIn recommends these jobs to job seekers who are\\nlikely to be interested. However, with the vast number of online job vacancies,\\nit becomes challenging to discern overarching trends in the labor market. In\\nthis paper, we propose a data mining-based approach for job classification in\\nthe modern online labor market. We employed structural topic modeling as our\\nmethodology and used the NASDAQ-100 indexed companies' online job vacancies on\\nLinkedIn as the input data. We discover that among all 13 job categories,\\nMarketing, Branding, and Sales; Software Engineering; Hardware Engineering;\\nIndustrial Engineering; and Project Management are the most frequently posted\\njob classifications. This study aims to provide a clearer understanding of job\\nmarket trends, enabling stakeholders to make informed decisions in a rapidly\\nevolving employment landscape.\",\"PeriodicalId\":501273,\"journal\":{\"name\":\"arXiv - ECON - General Economics\",\"volume\":\"8 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - ECON - General Economics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.00658\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - ECON - General Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.00658","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Nasdaq-100 Companies' Hiring Insights: A Topic-based Classification Approach to the Labor Market
The emergence of new and disruptive technologies makes the economy and labor
market more unstable. To overcome this kind of uncertainty and to make the
labor market more comprehensible, we must employ labor market intelligence
techniques, which are predominantly based on data analysis. Companies use job
posting sites to advertise their job vacancies, known as online job vacancies
(OJVs). LinkedIn is one of the most utilized websites for matching the supply
and demand sides of the labor market; companies post their job vacancies on
their job pages, and LinkedIn recommends these jobs to job seekers who are
likely to be interested. However, with the vast number of online job vacancies,
it becomes challenging to discern overarching trends in the labor market. In
this paper, we propose a data mining-based approach for job classification in
the modern online labor market. We employed structural topic modeling as our
methodology and used the NASDAQ-100 indexed companies' online job vacancies on
LinkedIn as the input data. We discover that among all 13 job categories,
Marketing, Branding, and Sales; Software Engineering; Hardware Engineering;
Industrial Engineering; and Project Management are the most frequently posted
job classifications. This study aims to provide a clearer understanding of job
market trends, enabling stakeholders to make informed decisions in a rapidly
evolving employment landscape.