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Text mining methodologies with R: An application to central bank texts

Author

Listed:
  • Jonathan Benchimol

    (BoI - Bank of Israel)

  • Sophia Kazinnik

    (Federal Reserve Bank of Richmond)

  • Yossi Saadon

    (BoI - Bank of Israel)

Abstract

We review several existing text analysis methodologies and explain their formal application processes using the open-source software R and relevant packages. Several text mining applications to analyze central bank texts are presented.

Suggested Citation

  • Jonathan Benchimol & Sophia Kazinnik & Yossi Saadon, 2022. "Text mining methodologies with R: An application to central bank texts," Post-Print emse-03953759, HAL.
    • Handle: RePEc:hal:journl:emse-03953759
    DOI: 10.1016/j.mlwa.2022.100286
    Note: View the original document on HAL open archive server: https://hal-emse.ccsd.cnrs.fr/emse-03953759v1
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    References listed on IDEAS

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    1. Jonathan Benchimol & Sophia Kazinnik & Yossi Saadon, 2021. "Federal Reserve Communication and the COVID-19 Pandemic," Bank of Israel Working Papers 2021.15, Bank of Israel.
    2. Jonathan Benchimol & Sophia Kazinnik & Yossi Saadon, 2021. "Federal Reserve Communication and the COVID-19 Pandemic," Bank of Israel Working Papers 2021.15, Bank of Israel.
    3. Stephen Hansen & Michael McMahon & Matthew Tong, 2019. "The long-run information effect of central bank communication," Bank of England working papers 777, Bank of England.
    4. Hansen, Stephen & McMahon, Michael, 2016. "Shocking language: Understanding the macroeconomic effects of central bank communication," Journal of International Economics, Elsevier, vol. 99(S1), pages 114-133.
    5. David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
    6. Hansen, Stephen & McMahon, Michael & Tong, Matthew, 2019. "The long-run information effect of central bank communication," Journal of Monetary Economics, Elsevier, vol. 108(C), pages 185-202.
    7. Saskia Ter Ellen & Vegard H. Larsen & Leif Anders Thorsrud, 2022. "Narrative Monetary Policy Surprises and the Media," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 54(5), pages 1525-1549, August.
    8. Feinerer, Ingo & Hornik, Kurt & Meyer, David, 2008. "Text Mining Infrastructure in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i05).
    9. Michael Ehrmann & Marcel Fratzscher, 2007. "Communication by Central Bank Committee Members: Different Strategies, Same Effectiveness?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(2‐3), pages 509-541, March.
    10. Laver, Michael & Benoit, Kenneth & Garry, John, 2003. "Extracting Policy Positions from Political Texts Using Words as Data," American Political Science Review, Cambridge University Press, vol. 97(2), pages 311-331, May.
    11. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    12. Bholat, David & Broughton, Nida & Ter Meer, Janna & Walczak, Eryk, 2019. "Enhancing central bank communications using simple and relatable information," Journal of Monetary Economics, Elsevier, vol. 108(C), pages 1-15.
    13. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

    1. Benchimol Jonathan & Caspi Itamar & Kazinnik Sophia, 2023. "Measuring Communication Quality of Interest Rate Announcements," The Economists' Voice, De Gruyter, vol. 20(1), pages 43-53, June.
    2. Binbin Yang & Sang-Do Park, 2023. "Who Drives Carbon Neutrality in China? Text Mining and Network Analysis," Sustainability, MDPI, vol. 15(6), pages 1-24, March.
    3. Curti, Filippo & Kazinnik, Sophia, 2023. "Central bank communication and website characteristics," Journal of Economic Behavior & Organization, Elsevier, vol. 212(C), pages 1216-1241.
    4. Erkan Işığıçok & Sadullah Çelik & Dilek Özdemir Yılmaz, 2023. "Analysis of Skills and Qualifications Required in Data Scientist Job Postings Based on the Pareto Analysis Perspective Using Text Mining," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(39), pages 10-25, December.
    5. Dimitrios Kanelis & Pierre L. Siklos, 2022. "Emotion in Euro Area Monetary Policy Communication and Bond Yields: The Draghi Era," CQE Working Papers 10322, Center for Quantitative Economics (CQE), University of Muenster.
    6. Kurowski, Łukasz & Smaga, Paweł, 2023. "Analysing financial stability reports as crisis predictors with the use of text-mining," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    7. Martina Dattilo & Fabio Padovano, 2023. "Evaluating the quality of UNESCO World Heritage List: a comparison with the Baedeker's guidebooks," Post-Print hal-04388046, HAL.
    8. Vyshnevskyi, Iegor & Jombo, Wytone & Sohn, Wook, 2024. "The clarity of monetary policy communication and financial market volatility in developing economies," Emerging Markets Review, Elsevier, vol. 59(C).
    9. Bogner Alexandra & Jerger Jürgen, 2023. "Big data in monetary policy analysis—a critical assessment," Economics and Business Review, Sciendo, vol. 9(2), pages 27-40, April.
    10. Martina Dattilo & Fabio Padovano, 2025. "Evaluating the quality of UNESCO World Heritage List: a comparison with the Baedeker’s guidebooks," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 49(1), pages 23-47, March.
    11. Yuqian Zhang, 2023. "Using Google Trends to track the global interest in International Financial Reporting Standards: Evidence from big data," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(2), pages 87-100, April.

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