Connecting firm's web scraped textual content to body of science: Utilizing microsoft academic graph hierarchical topic modeling
2022
Hajikhani, Arash | Pukelis, Lukas | Suominen, Arho | Ashouri, Sajad | Schubert, Torben | Notten, Ad | Cunningham, Scott W.
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. The method contains three main steps: data source identification, raw data retrieval, and data preparation and transformation. These steps are applied to two distinct data sources.
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