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Análisis de Sentimiento Basado en el Informe de Percepciones de Negocios del Banco Central de Chile

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  • María del Pilar Cruz
  • Hugo Peralta
  • Bruno Ávila

Abstract

Using the texts of the Business Perceptions Report published quarterly by the Central Bank of Chile, we construct a numerical index that reflects the emotional feeling or tone contained in the documents. For the construction of the index, we use the Sentiment Analysis (SA) or opinion mining methodology to extract the sentiment orientation of the documents, taking into account the positive or negative contextual polarity of their language. The results show that the IPN index has a high and significant correlation with various indices referring to business confidence and economic expectations in the medium term. The correlation with quantitative indicators of activity such as GDP growth, consumption or investment, is lower but still significant. The main contribution of this work is the formulation of a dictionary in Spanish language for SA and the generation of a numerical index through the application of the Sentiment Analysis methodology.

Suggested Citation

  • María del Pilar Cruz & Hugo Peralta & Bruno Ávila, 2020. "Análisis de Sentimiento Basado en el Informe de Percepciones de Negocios del Banco Central de Chile," Working Papers Central Bank of Chile 862, Central Bank of Chile.
    • Handle: RePEc:chb:bcchwp:862
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    File URL: https://www.bcentral.cl/documents/33528/133326/DTBC_862.pdf
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    References listed on IDEAS

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    1. Nicolás Chanut & Mario Marcel C. & Carlos A. Medel V., 2019. "Can economic perception surveys improve macroeconomic forecasting in Chile?," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 22(3), pages 034-097, December.
    2. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    3. Malcolm Baker & Jeffrey Wurgler, 2007. "Investor Sentiment in the Stock Market," Journal of Economic Perspectives, American Economic Association, vol. 21(2), pages 129-152, Spring.
    4. Paul Ormerod & Rickard Nyman & David Tuckett, 2015. "Measuring Financial Sentiment to Predict Financial Instability: A New Approach based on Text Analysis," Papers 1508.05357, arXiv.org.
    5. Ricardo Correa & Keshav Garud & Juan M Londono & Nathan Mislang, 2021. "Sentiment in Central Banks’ Financial Stability Reports," Review of Finance, European Finance Association, vol. 25(1), pages 85-120.
    6. Joaquin Iglesias & Alvaro Ortiz & Tomasa Rodrigo, 2017. "How do the EM Central Bank talk? A Big Data approach to the Central Bank of Turkey," Working Papers 17/24, BBVA Bank, Economic Research Department.
    7. 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.
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    Cited by:

    1. María del Pilar Cruz N. & Hugo Peralta V. & Juan Pablo Cova M., 2022. "Utilización de noticias de prensa como indicador de confianza económica en tiempo real," Working Papers Central Bank of Chile 938, Central Bank of Chile.

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