Measuring Polarization with Text Analysis: Evidence from the UK House of Commons, 1811â€“2015
Niels D. Goet
Political Analysis, 2019, vol. 27, issue 4, 518-539
Political scientists can rely on a long tradition of applying unsupervised measurement models to estimate ideology and preferences from texts. However, in practice the hope that the dominant source of variation in their data is the quantity of interest is often not realized. In this paper, I argue that in the messy world of speeches we have to rely on supervised approaches that include information on party affiliation in order to produce meaningful estimates of polarization. To substantiate this argument, I introduce a validation framework that may be used to comparatively assess supervised and unsupervised methods, and estimate polarization on the basis of 6.2Â million records of parliamentary speeches from the UK House of Commons over the period 1811â€“2015. Beyond introducing several important adjustments to existing estimation approaches, the paperâ€™s methodological contribution therefore consists of outlining the challenges of applying unsupervised estimation techniques to speech data, and arguing in detail why we should instead rely on supervised methods to measure polarization.
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
https://www.cambridge.org/core/product/identifier/ ... type/journal_article link to article abstract page (text/html)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:cup:polals:v:27:y:2019:i:04:p:518-539_00
Access Statistics for this article
More articles in Political Analysis from Cambridge University Press Cambridge University Press, UPH, Shaftesbury Road, Cambridge CB2 8BS UK.
Bibliographic data for series maintained by Keith Waters ().