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.

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

Marra, Alessandro
Primo
;
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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11564/787951
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