Extracting Policy Positions from Political Texts Using Words as Data

We present a new way of extracting policy positions from political texts that treats texts not as discourses to be understood and interpreted but rather, as data in the form of words. We compare this approach to previous methods of text analysis and use it to replicate published estimates of the pol...

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Bibliographic Details
Published in:The American political science review 2003-05, Vol.97 (2), p.311-331
Main Authors: LAVER, MICHAEL, BENOIT, KENNETH, GARRY, JOHN
Format: Article
Language:English
Subjects:
A priori knowledge
Algorithms
Analytical estimating
Coding
Computers
Confidence intervals
Content analysis
Data analysis
Dictionaries
Economic Policy
Elections
English language
Error of Measurement
Estimates
Estimation methods
Federal Republic of Germany
German language
Government policy
Great Britain
Ireland
Labor
Measurement errors
Methods
Platform
Policy Analysis
Political activism
Political analysis
Political Parties
Political science
Research methods
Social Policy
Speeches
Standard error
Text analysis
Texts
Words
Citations: Items that cite this one
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Summary:We present a new way of extracting policy positions from political texts that treats texts not as discourses to be understood and interpreted but rather, as data in the form of words. We compare this approach to previous methods of text analysis and use it to replicate published estimates of the policy positions of political parties in Britain and Ireland, on both economic and social policy dimensions. We “export” the method to a non-English-language environment, analyzing the policy positions of German parties, including the PDS as it entered the former West German party system. Finally, we extend its application beyond the analysis of party manifestos, to the estimation of political positions from legislative speeches. Our “language-blind” word scoring technique successfully replicates published policy estimates without the substantial costs of time and labor that these require. Furthermore, unlike in any previous method for extracting policy positions from political texts, we provide uncertainty measures for our estimates, allowing analysts to make informed judgments of the extent to which differences between two estimated policy positions can be viewed as significant or merely as products of measurement error.We thank Raj Chari, Gary King, Michael McDonald, Gail McElroy, and three anonymous reviewers for comments on drafts of this paper.
ISSN:0003-0554
1537-5943
DOI:10.1017/S0003055403000698