![]() ![]() 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. These steps are applied to two distinct data sources.ĪB - This paper demonstrates a method to transform and link textual information scraped from companies' websites to the scientific body of knowledge. The method contains three main steps: data source identification, raw data retrieval, and data preparation and transformation. Central to the operationalization of our method are a web scraping process, NLP and a data transformation/linkage procedure. We established a connection with Microsoft Academic Graph hierarchical topic modeling based on companies' website content. ![]() Therefore, we experimented on the European classification of economic activities (known as NACE) on sectoral and company levels. N2 - This paper demonstrates a method to transform and link textual information scraped from companies' websites to the scientific body of knowledge. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 870822. T2 - Utilizing microsoft academic graph hierarchical topic modeling T1 - Connecting firm's web scraped textual content to body of science
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