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

The DOI matches its Crossref record; 64 of the 65 references it cites were found in a registry or an index.

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

Open access
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.

Overview

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Crossref · 10.1146/annurev-polisci-052615-025542

Cites

64 of 64 references verified0 citation errors · 0 not checkable

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Citations

Cites

Show all 65 references

Research works printed but not linked: 12

  1. Bird S, 2009, Natural Language Processing with Python—Analyzing Text with the Natural Language Toolkit
  2. Blei D, 2009, Text Mining: Classification, Clustering, and Applications, 71
  3. Blei DM, 2003, J. Mach. Learn. Res., 3, 993
  4. Casas A, 2016, Rev. Espan. Investig. Sociol., 155, 73
  5. 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
  6. Collingwood L, 2011, J. Inf. Technol. Polit., 4, 1
  7. Grimmer J, 2016, The unreliability of measures of intercoder reliability, and what to do about it.
  8. Huang A, 2008, Proc. Sixth New Zealand Computer Science Research Student Conference, 49
  9. Kohavi R, 1995, Proc. Int. Joint Conf. Artificial Intelligence, 1137
  10. Laver M, 2003, Am. Polit. Sci. Rev., 2, 311
  11. 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/
  12. Ward M, 2013, Analysis, 21, 267

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Metadata

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
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.

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Statistics and arithmetic are recomputed, not judged.

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