Wordscores
Wordscores is a text-based scaling method developed by Laver, Benoit, and Garry (2003) that estimates the policy positions of political actors based on word frequencies in their texts. By comparing word usage in reference texts of known positions with test texts, the method infers the latent political dimension of any document without requiring manual coding or training data.
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When to use it
Apply Wordscores when you have reference texts with known positions and want to scale new documents on the same latent dimension. Ideal for analyzing party manifestos, legislative speeches, or news archives where reference materials exist. The method works best when reference texts are truly representative of their positions and when vocabulary is stable across documents.
Strengths & limitations
- Unsupervised scaling: no manual coding required after reference texts are selected
- Fast and scalable: easily processes thousands of documents
- Domain-general: works across different text types (manifestos, speeches, newspaper articles)
- Theory-driven: leverages known reference positions to interpret the latent dimension
- Straightforward interpretation: positions are directly comparable to reference scales
- Depends on reference texts: quality and representativeness of reference texts directly affect accuracy
- Assumes single dimension: typically estimates one latent dimension (e.g., left-right) rather than multiple dimensions
- Vocabulary sensitivity: rare words and new vocabulary in test texts are not scored, potentially introducing bias
- Static references: assumes reference positions are constant and comparable to test documents across time
Frequently asked
How do I choose appropriate reference texts?
Choose reference texts that are clearly and unambiguously representative of the extreme positions on your latent dimension. For political analysis, use manifestos of clearly left and right parties. Ensure they are substantial in length (hundreds or thousands of words) and typical of the document genre.
What if a word appears in my test text but not in the reference texts?
Words absent from reference texts receive no score (treated as missing). Many implementations handle this by excluding rare words entirely or using smoothing techniques. Some methods interpolate or use related words, but this adds assumptions.
Can Wordscores estimate multiple dimensions at once?
Standard Wordscores is designed for single-dimension scaling. To estimate multiple dimensions (e.g., left-right and social-liberal), you need multiple pairs of reference texts or move to methods like Wordfish or topic modeling.
How stable are Wordscores across time?
Wordscores assumes the meaning of words is stable, which breaks down if vocabulary shifts dramatically. Over long time periods or with evolving policy domains, reference-based methods may need updating or be supplemented with relative scaling methods like Wordfish.
What metrics should I use to validate Wordscores results?
Compare estimated positions of reference texts themselves (should recover known positions), correlate with external measures (expert surveys, vote records), or perform holdout validation (remove some reference texts and see if Wordscores still predicts them accurately).
Sources
- Laver, M., Benoit, K., & Garry, J. (2003). Extracting policy positions from political texts using words as data. American Political Science Review, 97(2), 311-331. DOI: 10.1017/s0003055403000698 ↗
- Benoit, K., & Laver, M. (2012). The basic arithmetic of legislative decisions. Journal of Political Institutions and Political Economy, 1(1), 1-29. link ↗
- Klemmensen, R., Hobolt, S. B., & Hansen, M. E. (2007). Estimating policy positions using political texts: A scaling approach. Electoral Studies, 26(4), 746-755. link ↗
How to cite this page
ScholarGate. (2026, June 3). Wordscores. ScholarGate. https://scholargate.app/en/psychometrics/wordscores
Which method?
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