Cited by 300Computational and Text Analysis MethodsPolitical Science Research and EducationDigital Humanities and Scholarship

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Large-Scale Computerized Text Analysis in Political Science: Opportunities and Challenges

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Wilkerson, John, Casas, Andreu·2017·Annual Review of Political Science

DOI 10.1146/annurev-polisci-052615-025542

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Abstract

Text has always been an important data source in political science. What has changed in recent years is the feasibility of investigating large amounts of text quantitatively. The internet provides political scientists with more data than their mentors could have imagined, and the research community is providing accessible text analysis software packages, along with training and support. As a result, text-as-data research is becoming mainstream in political science. Scholars are tapping new data sources, they are employing more diverse methods, and they are becoming critical consumers of findings based on those methods. In this article, we first describe the four stages of a typical text-as-data project. We then review recent political science applications and explore one important methodological challenge—topic model instability—in greater detail.

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Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Latent dirichlet allocation
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
The Coding Manual for Qualitative Researchers
Saldaña, J. (2025) · SAGE Publications Ltd eBooks · doi:10.4135/9781036235611 · Cited by 19,193
Measures of the Amount of Ecologic Association Between Species
Dice, L. R. (1945) · Ecology · doi:10.2307/1932409 · Cited by 12,187
A general method applicable to the search for similarities in the amino acid sequence of two proteins
Needleman, S. B., & Wunsch, C. D. (1970) · Journal of Molecular Biology · doi:10.1016/0022-2836(70)90057-4 · Cited by 11,288
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909
Identification of common molecular subsequences
Smith, T. F., & Waterman, M. S. (1981) · Journal of Molecular Biology · doi:10.1016/0022-2836(81)90087-5 · Cited by 9,962
Thumbs up?: sentiment classification using machine learning techniques
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118704 · Cited by 7,024
Opinion Mining and Sentiment Analysis
Pang, B., & Lee, L. (2008) · now publishers, Inc. eBooks · doi:10.1561/9781601981516 · Cited by 6,957
Congress, The Electoral Connection
Ross, M. J., & Mayhew, D. R. (1975) · The Western Political Quarterly · doi:10.2307/447379 · Cited by 4,094

Show all 65 references

Research works printed but not linked: 12

Bird S, 2009, Natural Language Processing with Python—Analyzing Text with the Natural Language Toolkit
Blei D, 2009, Text Mining: Classification, Clustering, and Applications, 71
Blei DM, 2003, J. Mach. Learn. Res., 3, 993
Casas A, 2016, Rev. Espan. Investig. Sociol., 155, 73
Chuang J, Wilkerson JD, Weiss R, Tingley D, Stewart BM, et al. 2014. Computer-assisted content analysis: topic models for exploring multiple subjective interpretations. Presented at Advances in Neural Information Processing Systems Workshop on Human-Propelled Machine Learning, Montreal, Dec. 8–13
Collingwood L, 2011, J. Inf. Technol. Polit., 4, 1
Grimmer J, 2016, The unreliability of measures of intercoder reliability, and what to do about it.
Huang A, 2008, Proc. Sixth New Zealand Computer Science Research Student Conference, 49
Kohavi R, 1995, Proc. Int. Joint Conf. Artificial Intelligence, 1137
Laver M, 2003, Am. Polit. Sci. Rev., 2, 311
Wallach H, Dicker L, Jensen S. 2010. An alternative prior for nonparametric Bayesian clustering. In Proc. Thirteenth International Conference on Artificial Intelligence and Statistics, May 13–15, 2010, Chia Laguna Resort, Sardinia, Italy , ed. YW Teh, M Titterington, 9:892–99. http://www.jmlr.org/proceedings/papers/v9/
Ward M, 2013, Analysis, 21, 267

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Authors: Wilkerson, J., & Casas, A.
Affiliations: University of Washington (US)
ORCID: Casas, Andreu
Venue: Annual Review of Political Science
Publisher: Annual Reviews
Published: 2017-05-11
Volume: 20
Issue: 1
Pages: 529-544
ISSN: 1094-2939 (ISSN-L)
1545-1577
DOI: 10.1146/annurev-polisci-052615-025542
OpenAlex: W2515855575
Semantic Scholar: 0d345c2fb459e6ecc28328917ab37a4707e4a502
Cited by: 300
References: 65
Citations by year: 2026: 29 · 2025: 48 · 2024: 37 · 2023: 38 · 2022: 34 · 2021: 50 · 2020: 25 · 2019: 25 · 2018: 12 · 2017: 2
FWCI: 7.83
Type: journal article
Language: en
Open access: green
research.vu.nl
Licence: other-oa
Locations: Annual Review of Political Science (publishedVersion)
VU Research Portal (publishedVersion, other-oa)
Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Topics: Social Sciences › General Social Sciences › Computational and Text Analysis Methods
Social Sciences › Political Science and International Relations › Political Science Research and Education
Social Sciences › Arts and Humanities › Literature and Literary Theory › Digital Humanities and Scholarship
Keywords: text-as-data, computational text analysis, topic modeling, online information sources, quantitative text analysis

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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Large-Scale Computerized Text Analysis in Political Science: Opportunities and Challenges

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