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Measuring News Sentiment of Korea Using Transformer

Author

Listed:
  • Beomseok Seo

    (Bank of Korea)

  • Younghwan Lee

    (Bank of Korea)

  • Hyungbae Cho

    (Bank of Korea)

Abstract

We have developed the Korean news sentiment index (NSI) to gauge the economic sentiment of Korea on a daily basis by analyzing news texts gathered from the Internet. Our framework utilizes cutting-edge natural language processing techniques to compute the NSI and examine keywords, offering insights into its fluctuations. We designed a sentiment classifier using transformer neural networks that effectively process extensive news samples to compute the NSI of Korea. We compute the NSI more frequently and immediately than official indices that rely on monthly surveys. Through this, we can identify changes in economic sentiment before official statistics are released. Moreover, the proposed framework offers keyword analysis and sector indices to clarify why economic sentiments fluctuate. Our comprehensive assessments demonstrate that the NSI is a valuable leading index and an essential tool for identifying inflection points in economic sentiment.

Suggested Citation

  • Beomseok Seo & Younghwan Lee & Hyungbae Cho, 2024. "Measuring News Sentiment of Korea Using Transformer," Korean Economic Review, Korean Economic Association, vol. 40, pages 149-176.
  • Handle: RePEc:kea:keappr:ker-20240101-40-1-05
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    References listed on IDEAS

    as
    1. Andrii Babii & Eric Ghysels & Jonas Striaukas, 2022. "Machine Learning Time Series Regressions With an Application to Nowcasting," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1094-1106, June.
    2. Hamza Bennani & Matthias Neuenkirch, 2017. "The (home) bias of European central bankers: new evidence based on speeches," Applied Economics, Taylor & Francis Journals, vol. 49(11), pages 1114-1131, March.
    3. Robert B. Barsky & Eric R. Sims, 2012. "Information, Animal Spirits, and the Meaning of Innovations in Consumer Confidence," American Economic Review, American Economic Association, vol. 102(4), pages 1343-1377, June.
    4. 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.
    5. Kim Nguyen & Gianni La Cava, 2020. "Start Spreading the News: News Sentiment and Economic Activity in Australia," RBA Research Discussion Papers rdp2020-08, Reserve Bank of Australia.
    6. Bas Aarle & Marcus Kappler, 2012. "Economic sentiment shocks and fluctuations in economic activity in the euro area and the USA," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 47(1), pages 44-51, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    News Text Data; Natural Language Processing for Economic Analysis; Sentiment Shocks;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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