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Cited by 684Computational and Text Analysis MethodsElectoral Systems and Political ParticipationSentiment Analysis and Opinion Mining

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

A Scaling Model for Estimating Time‐Series Party Positions from Texts

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Slapin, Jonathan B., Proksch, Sven‐Oliver·2008·American Journal of Political Science
DOI 10.1111/j.1540-5907.2008.00338.x
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Abstract

Recent advances in computational content analysis have provided scholars promising new ways for estimating party positions. However, existing text‐based methods face challenges in producing valid and reliable time‐series data. This article proposes a scaling algorithm called WORDFISH to estimate policy positions based on word frequencies in texts. The technique allows researchers to locate parties in one or multiple elections. We demonstrate the algorithm by estimating the positions of German political parties from 1990 to 2005 using word frequencies in party manifestos. The extracted positions reflect changes in the party system more accurately than existing time‐series estimates. In addition, the method allows researchers to examine which words are important for placing parties on the left and on the right. We find that words with strong political connotations are the best discriminators between parties. Finally, a series of robustness checks demonstrate that the estimated positions are insensitive to distributional assumptions and document selection.

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Research works printed but not linked: 2

  1. De Swaan Abram, 1973, Coalition Theories and Cabinet Formation
  2. Klingemann Hans‐Dieter, 1995, Citizens and the State, 183

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Metadata

Authors
Slapin, J. B., & Proksch, S.
Affiliations
  • Trinity College Dublin (IE)
  • University of California, Los Angeles (US)
ORCID
Venue
American Journal of Political Science
Publisher
Wiley
Published
2008-07
Volume
52
Issue
3
Pages
705-722
ISSN
  • 0092-5853 (ISSN-L)
  • 1540-5907
DOI
10.1111/j.1540-5907.2008.00338.x
OpenAlex
W1998151601
Cited by
684
References
52
Citations by year
2026: 40 · 2025: 30 · 2024: 44 · 2023: 32 · 2022: 35 · 2021: 43 · 2020: 47 · 2019: 52 · 2018: 44 · 2017: 45 · 2016: 38 · 2015: 34 · 2014: 38 · 2013: 50 · 2012: 29
FWCI
143.73
Type
journal article
Language
en
Open access
Licence
onlinelibrary.wiley.com/termsAndConditions#vor (vor, 8 d)
Locations
American Journal of Political Science (publishedVersion)
Topics
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
  • Social Sciences › Political Science and International Relations › Electoral Systems and Political Participation
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Keywords
party positions, policy positions, word frequency, party manifestos, time series estimation, German political parties, party system change, political connotation, left-right positioning

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