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Text Mining-based Economic Activity Estimates

Author

Listed:
  • Ksenia Yakovleva

    (Bank of Russia, Russian Federation)

Abstract

This paper outlines the methodology for calculating a high-frequency indicator of economic activity in Russia. News articles taken from Internet resources are used as data sources. The news articles are analysed using text mining and machine learning methods, which, although developed relatively recently, have quickly found wide application in scientific research, including economic studies. This is because news is not only a key source of information but a way to gauge the sentiment of journalists and survey respondents about the current situation and convert it into quantitative data.

Suggested Citation

  • Ksenia Yakovleva, 2017. "Text Mining-based Economic Activity Estimates," Bank of Russia Working Paper Series wps25, Bank of Russia.
    • Handle: RePEc:bkr:wpaper:wps25
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    File URL: http://cbr.ru/Content/Document/File/87561/wp25_e.pdf
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    Cited by:

    1. Filipp Ulyankin, 2020. "Forecasting Russian Macroeconomic Indicators Based on Information from News and Search Queries," Russian Journal of Money and Finance, Bank of Russia, vol. 79(4), pages 75-97, December.

    More about this item

    Keywords

    economic activity estimates; text mining; machine learning.;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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