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Using text data instead of SIC codes to tag innovative firms and classify industrial activities

Articolo
Data di Pubblicazione:
2022
Abstract:
The paper uses text mining and semantic algorithms to tag innovative firms and offer an alternative perspective to classify industrial activities. Instead of referring to firms' standard industrial classification codes, we gather information from companies' websites and corporate purposes, extract keywords and generate tags concerning firms' activities, specializations, and competences. Evidence is interesting because allows us to understand 'what firms do' in a more penetrating and updated way than referring to standard industrial classification codes. Moreover, through matching firms' keywords, we can explore the degree of closeness between the firms under observation, a measure by which researchers can derive industrial proximity. The analysis can provide policymakers with a detailed and comprehensive picture of the innovative trajectories underlying the industrial structure in a geographic area.
Tipologia CRIS:
1.1 Articolo in rivista
Keywords:
Industry; Organizations
Elenco autori:
Marra, Alessandro; Baldassari, Cristiano
Autori di Ateneo:
MARRA Alessandro
Link alla scheda completa:
https://ricerca.unich.it/handle/11564/787951
Link al Full Text:
https://ricerca.unich.it//retrieve/handle/11564/787951/335482/Marra%202022%20Po.pdf
Pubblicato in:
PLOS ONE
Journal
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