IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0270041.html
   My bibliography  Save this article

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

Author

Listed:
  • Alessandro Marra
  • Cristiano Baldassari

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.

Suggested Citation

  • Alessandro Marra & Cristiano Baldassari, 2022. "Using text data instead of SIC codes to tag innovative firms and classify industrial activities," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-21, June.
    • Handle: RePEc:plo:pone00:0270041
    DOI: 10.1371/journal.pone.0270041
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0270041
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0270041&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0270041?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ashlee Humphreys & Rebecca Jen-Hui Wang & Eileen FischerEditor & Linda PriceAssociate Editor, 2018. "Automated Text Analysis for Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(6), pages 1274-1306.
    2. Nathan, Max & Rosso, Anna, 2015. "Mapping digital businesses with big data: Some early findings from the UK," Research Policy, Elsevier, vol. 44(9), pages 1714-1733.
    3. Lihua Wang & Edward J. Zajac, 2007. "Alliance or acquisition? a dyadic perspective on interfirm resource combinations," Strategic Management Journal, Wiley Blackwell, vol. 28(13), pages 1291-1317, December.
    4. Pasquale Pavone & Margherita Russo, 2017. "Clusters of specializations in the automotive supply chain in Italy. An empirical analysis using text mining," Department of Economics 0116, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
    5. Gerard Hoberg & Gordon Phillips, 2018. "Conglomerate Industry Choice and Product Language," Management Science, INFORMS, vol. 64(8), pages 3735-3755, August.
    6. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    7. Pasquale Pavone & Margherita Russo, 2017. "Clusters of specializations in the automotive supply chain in Italy. An empirical analysis using text mining," Center for the Analysis of Public Policies (CAPP) 0157, Universita di Modena e Reggio Emilia, Dipartimento di Economia "Marco Biagi".
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Alessandro Marra & Marco Cucculelli & Alfredo Cartone, 2024. "So far, yet so close. Using networks of words to measure proximity and spillovers between firms," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 14(4), pages 973-1000, December.
    2. Francesco Saverio Stentella Lopes & Franco Fiordelisi & Ornella Ricci, 2019. "Corporate Culture and Merger Success," Working Papers 19013, Bangor Business School, Prifysgol Bangor University (Cymru / Wales).
    3. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    4. Müller, Karsten, 2020. "German forecasters' narratives: How informative are German business cycle forecast reports?," Working Papers 23, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    5. Gao, Xin & Xu, Weidong & Li, Donghui, 2025. "Media coverage and managerial investment learning from stock markets: International evidence," Research in International Business and Finance, Elsevier, vol. 76(C).
    6. Yan Luo & Linying Zhou, 2020. "Textual tone in corporate financial disclosures: a survey of the literature," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 17(2), pages 101-110, September.
    7. Jiao Ji & Oleksandr Talavera & Shuxing Yin, 2018. "The Hidden Information Content: Evidence from the Tone of Independent Director Reports," Working Papers 2018-28, Swansea University, School of Management.
    8. Leah Warfield Smith & Randall Lee Rose & Alex R. Zablah & Heath McCullough & Mohammad “Mike” Saljoughian, 2023. "Examining post-purchase consumer responses to product automation," Journal of the Academy of Marketing Science, Springer, vol. 51(3), pages 530-550, May.
    9. Lixiang Wang & Wendi Hou & Yupei Liu, 2023. "How do co‐shareholding networks affect negative media coverage? Evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(4), pages 4221-4249, December.
    10. Kamaladdin Fataliyev & Aneesh Chivukula & Mukesh Prasad & Wei Liu, 2021. "Stock Market Analysis with Text Data: A Review," Papers 2106.12985, arXiv.org, revised Jul 2021.
    11. Bennani, Hamza, 2018. "Media coverage and ECB policy-making: Evidence from an augmented Taylor rule," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 26-38.
    12. Chen, Yanyan & Mandler, Timo & Meyer-Waarden, Lars, 2021. "Three decades of research on loyalty programs: A literature review and future research agenda," Journal of Business Research, Elsevier, vol. 124(C), pages 179-197.
    13. Keval Amin & Erica Harris, 2022. "The Effect of Investor Sentiment on Nonprofit Donations," Journal of Business Ethics, Springer, vol. 175(2), pages 427-450, January.
    14. King, Timothy & Srivastav, Abhishek & Williams, Jonathan, 2016. "What's in an education? Implications of CEO education for bank performance," Journal of Corporate Finance, Elsevier, vol. 37(C), pages 287-308.
    15. Kirtac, Kemal & Germano, Guido, 2024. "Sentiment trading with large language models," Finance Research Letters, Elsevier, vol. 62(PB).
    16. Sapkota, Niranjan, 2022. "News-based sentiment and bitcoin volatility," International Review of Financial Analysis, Elsevier, vol. 82(C).
    17. Eryka Probierz & Adam Galuszka & Katarzyna Klimczak & Karol Jedrasiak & Tomasz Wisniewski & Tomasz Dzida, 2021. "Financial Sentiment on Twitter's Community and it's Connection to Polish Stock Market Movements in Context of Behavior Modelling," European Research Studies Journal, European Research Studies Journal, vol. 0(4 - Part ), pages 56-65.
    18. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2022. "Media-expressed tone, option characteristics, and stock return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    19. Caporale, Guglielmo Maria & Spagnolo, Fabio & Spagnolo, Nicola, 2016. "Macro news and stock returns in the Euro area: A VAR-GARCH-in-mean analysis," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 180-188.
    20. Baur, Dirk G. & Smales, Lee A., 2020. "Hedging geopolitical risk with precious metals," Journal of Banking & Finance, Elsevier, vol. 117(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0270041. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.