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

Jonathan Benchimol; Sophia Kazinnik; Yossi Saadon

doi:10.1016/j.mlwa.2022.100286

Journal Articles Machine Learning with Applications Year : 2022

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

(1) , (2) , (1)

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Jonathan Benchimol

Function : Author
PersonId : 4420
IdHAL : jonathanbenchimol
ORCID : 0000-0002-3205-3196
IdRef : 139284516

Bank of Israel

Sophia Kazinnik

Function : Author

Federal Reserve Bank of Richmond

Yossi Saadon

Function : Author

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.

Keywords

Text mining R programming Sentiment analysis Topic modeling Natural language processing Central bank communication

Domains

Quantitative Finance [q-fin] Computation and Language [cs.CL] Economics and Finance Library and information sciences

Jonathan Benchimol : Connect in order to contact the contributor

https://hal-emse.ccsd.cnrs.fr/emse-03953759

Submitted on : Tuesday, January 24, 2023-11:04:06 AM

Last modification on : Monday, October 14, 2024-12:38:13 PM

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emse-03953759 , version 1 (24-01-2023)

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HAL Id : emse-03953759 , version 1
DOI : 10.1016/j.mlwa.2022.100286

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Jonathan Benchimol, Sophia Kazinnik, Yossi Saadon. Text mining methodologies with R: An application to central bank texts. Machine Learning with Applications, 2022, 8, pp.100286. ⟨10.1016/j.mlwa.2022.100286⟩. ⟨emse-03953759⟩

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

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