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Understanding Wordscores

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  • Lowe, Will

Abstract

Wordscores is a widely used procedure for inferring policy positions, or scores, for new documents on the basis of scores for words derived from documents with known scores. It is computationally straightforward, requires no distributional assumptions, but has unresolved practical and theoretical problems. In applications, estimated document scores are on the wrong scale and the theoretical development does not specify a statistical model, so it is unclear what assumptions the method makes about political text and how to tell whether they fit particular text analysis applications. The first part of the paper demonstrates that badly scaled document score estimates reflect deeper problems with the method. The second part shows how to understand Wordscores as an approximation to correspondence analysis which itself approximates a statistical ideal point model for words. Problems with the method are identified with the conditions under which these layers of approximation fail to ensure consistent and unbiased estimation of the parameters of the ideal point model.

Suggested Citation

  • Lowe, Will, 2008. "Understanding Wordscores," Political Analysis, Cambridge University Press, vol. 16(4), pages 356-371.
  • Handle: RePEc:cup:polals:v:16:y:2008:i:04:p:356-371_00
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    Cited by:

    1. Pongsak Luangaram & Yuthana Sethapramote, 2016. "Central Bank Communication and Monetary Policy Effectiveness: Evidence from Thailand," PIER Discussion Papers 20, Puey Ungphakorn Institute for Economic Research.
    2. Pierre-Marc Daigneault & Dominic Duval & Louis M. Imbeau, 2018. "Supervised scaling of semi-structured interview transcripts to characterize the ideology of a social policy reform," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(5), pages 2151-2162, September.
    3. Adriana Bunea & Raimondas Ibenskas, 2015. "Quantitative text analysis and the study of EU lobbying and interest groups," European Union Politics, , vol. 16(3), pages 429-455, September.
    4. Alastair Langtry & Niklas Potrafke & Marcel Schlepper & Timo Wochner, 2024. "Gambling for Re-election," CESifo Working Paper Series 11125, CESifo.
    5. Gropp, Reint & Radev, Deyan, 2017. "Social centralization, bank integration and the transmission of lending shocks," SAFE Working Paper Series 174, Leibniz Institute for Financial Research SAFE.
    6. Pongsak Luangaram & Yuthana Sethapramote, 2016. "Central Bank Communication and Monetary Policy Effectiveness: Evidence from Thailand," PIER Discussion Papers 20., Puey Ungphakorn Institute for Economic Research, revised Feb 2016.
    7. Caroline Le Pennec, 2020. "Strategic Campaign Communication: Evidence from 30,000 Candidate Manifestos," SoDa Laboratories Working Paper Series 2020-05, Monash University, SoDa Laboratories.
    8. Barth, Andreas & Radev, Deyan, 2022. "Integration culture of global banks and the transmission of lending shocks," Journal of Banking & Finance, Elsevier, vol. 134(C).
    9. Gessler, Theresa & Hunger, Sophia, 2022. "How the refugee crisis and radical right parties shape party competition on immigration," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 10(3), pages 524-544.
    10. Kevin M. Quinn & Burt L. Monroe & Michael Colaresi & Michael H. Crespin & Dragomir R. Radev, 2010. "How to Analyze Political Attention with Minimal Assumptions and Costs," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 209-228, January.
    11. Gropp, Reint E. & Radev, Deyan, 2017. "Social centralisation, bank integration and the transmission of lending shocks," IWH Discussion Papers 18/2017, Halle Institute for Economic Research (IWH).
    12. Anna Calissano & Simone Vantini & Marika Arena, 2020. "Monitoring rare categories in sentiment and opinion analysis: a Milan mega event on Twitter platform," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(4), pages 787-812, December.

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