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

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 k...

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Bibliographic Details
Published in:PloS one 2022-06, Vol.17 (6), p.e0270041-e0270041
Main Authors: Marra, Alessandro, Baldassari, Cristiano
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Classification
Codes
Computer and Information Sciences
Data mining
Economic activity
Engineering and Technology
Evaluation
Industrial areas
Industrial research
Innovations
Italy
Keywords
North American Industry Classification System
Product reviews
Semantics
Social Sciences
Websites
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0270041