IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2209.05383.html
   My bibliography  Save this paper

Weak Supervision in Analysis of News: Application to Economic Policy Uncertainty

Author

Listed:
  • Paul Trust
  • Ahmed Zahran
  • Rosane Minghim

Abstract

The need for timely data analysis for economic decisions has prompted most economists and policy makers to search for non-traditional supplementary sources of data. In that context, text data is being explored to enrich traditional data sources because it is easy to collect and highly abundant. Our work focuses on studying the potential of textual data, in particular news pieces, for measuring economic policy uncertainty (EPU). Economic policy uncertainty is defined as the public's inability to predict the outcomes of their decisions under new policies and future economic fundamentals. Quantifying EPU is of great importance to policy makers, economists, and investors since it influences their expectations about the future economic fundamentals with an impact on their policy, investment and saving decisions. Most of the previous work using news articles for measuring EPU are either manual or based on a simple keyword search. Our work proposes a machine learning based solution involving weak supervision to classify news articles with regards to economic policy uncertainty. Weak supervision is shown to be an efficient machine learning paradigm for applying machine learning models in low resource settings with no or scarce training sets, leveraging domain knowledge and heuristics. We further generated a weak supervision based EPU index that we used to conduct extensive econometric analysis along with the Irish macroeconomic indicators to validate whether our generated index foreshadows weaker macroeconomic performance

Suggested Citation

  • Paul Trust & Ahmed Zahran & Rosane Minghim, 2022. "Weak Supervision in Analysis of News: Application to Economic Policy Uncertainty," Papers 2209.05383, arXiv.org, revised Sep 2022.
  • Handle: RePEc:arx:papers:2209.05383
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2209.05383
    File Function: Latest version
    Download Restriction: no
    ---><---

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2209.05383. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.