Connecting firm's web scraped textual content to body of science: Utilizing microsoft academic graph hierarchical topic modeling

Arash Hajikhani*, Arho Suominen, Sajad Ashouri, Lukas Pukelis, Torben Schubert, Ad Notten, Scott Cunningham

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

This paper demonstrates a method to transform and link textual information scraped from companies' websites to the scientific body of knowledge. The method illustrates the benefit of Natural Language Processing (NLP) in creating links between established economic classification systems with novel and agile constructs that new data sources enable. Therefore, we experimented on the European classification of economic activities (known as NACE) on sectoral and company levels. We established a connection with Microsoft Academic Graph hierarchical topic modeling based on companies' website content. Central to the operationalization of our method are a web scraping process, NLP and a data transformation/linkage procedure.
Original languageEnglish
Article number101650
JournalMethodsX
Volume9
DOIs
Publication statusPublished - 27 Feb 2022

JEL classifications

  • o32 - Management of Technological Innovation and R&D
  • o31 - Innovation and Invention: Processes and Incentives
  • o34 - Intellectual Property Rights

Keywords

  • Natural language processing
  • Economic classification scheme
  • Knowledge transformation
  • Web scraping

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