Text mining on job advertisement data: Systematic process for detecting artificialintelligence related jobs

Asta Bäck, Arash Hajikhani, Arho Suominen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Abstract

The use of online job advertisement has made them an important source of quantitative information about the innovation system. This data offers significant opportunities to study trends, transitions in the job markets and skill demands. In this study, we have utilized the job ads data of a major Finnish job market platform to investigate the emergence of AI-related jobs. More than 480 000 job advertisements during 2013-2020 was used to create insight on skills transitions, particularly focusing on artificial intelligence related skills. A glossary of AI-related skills was created and applied to the job data to identify the relatedness spectrum of ads to AI using a three-tier system. By incorporating sectoral firm-level information, we explored the variation in AI-related skills demand over time and sectors. Our study presents a systematic way to utilize job advertisement data for detecting demand trends for specific skills.

Original languageEnglish
Title of host publicationProceedings of the 1st Workshop on AI + Informetrics (AII2021) co-located with the iConference 2021 (AII 2021)
EditorsYi Chang, Chengzhi Zhang, Philipp Mayr, Arho Suominen
PublisherCEUR-WS
Pages111-124
Number of pages14
Publication statusPublished - 2021
Publication typeA4 Article in conference proceedings
EventAI + Informetrics - Virtual, Online
Duration: 17 Mar 2021 → …

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR-WS
Volume2871
ISSN (Print)1613-0073

Conference

ConferenceAI + Informetrics
CityVirtual, Online
Period17/03/21 → …

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • General Computer Science

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