Topic modelling applied on innovation studies of Flemish companies

Abstract

Mapping innovation in companies for the purpose of official statistics is usually done through business surveys. However, this traditional approach faces several drawbacks like a lack of responses, response bias, low frequency, and high costs. Alternatively, text-based models trained on web-scraped text from company websites have been developed to complement or substitute traditional business surveys. This paper utilises web scraping and text-based models to map the business innovation in Flanders with a focus on identifying different types of innovation through topic modelling. More specifically, the scraped web texts are used to identify innovative economic sectors or topics, and to classify firms into these topics using Top2Vec and Lbl2Vec. We conclude that both models can be successfully combined to discover topics (or sectors) and classify companies into these topics which results in an additional parameter for mapping innovation in different regions.

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ORCID for Bart Baesens: orcid.org/0000-0002-5831-5668

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