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Cited by 671Computational and Text Analysis MethodsTopic ModelingSentiment Analysis and Opinion Mining

How to analyze political attention with minimal assumptions and costs

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Quinn, Kevin M., Monroe, Burt L., Colaresi, Michael, Crespin, Michael H., Radev, Dragomir R.·2010·American Journal of Political Science
DOI 10.1111/j.1540-5907.2009.00427.x
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Abstract

Previous methods of analyzing the substance of political attention have had to make several restrictive assumptions or been prohibitively costly when applied to large‐scale political texts. Here, we describe a topic model for legislative speech, a statistical learning model that uses word choices to infer topical categories covered in a set of speeches and to identify the topic of specific speeches. Our method estimates, rather than assumes, the substance of topics, the keywords that identify topics, and the hierarchical nesting of topics. We use the topic model to examine the agenda in the U.S. Senate from 1997 to 2004. Using a new database of over 118,000 speeches (70,000,000 words) from the Congressional Record, our model reveals speech topic categories that are both distinctive and meaningfully interrelated and a richer view of democratic agenda dynamics than had previously been possible.

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Crossref · 10.1111/j.1540-5907.2009.00427.x

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  • Political scientists have extended topic models so that the parameters correspond to politically relevant quantities of interest.

    “Political scientists have extended topic models so that the parameters correspond to politically relevant quantities of interest.”

    From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Quinn et al. 2010

  • If topics are valid, then external events should explain sudden increases in attention to a topic.

    “Quinn et al. (2010) argue that if topics are valid, then external events should explain sudden increases in attention to a topic”

    From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Quinn et al. 2010

  • Unsupervised methods shift the user burden from determining categories before analysis to validating model output afterward.

    “As Quinn et al. (2010) observe, unsupervised methods shift the user burden from determining categories before analysis to validating model output afterward.”

    From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Quinn et al. 2010

  • The dynamic multitopic model (Quinn et al. 2010) models daily attention to topics in Senate floor speeches.

    “The first topic model in political science is the dynamic multitopic model (Quinn et al. 2010), used to model the daily attention to topics in Senate floor speeches”

    From Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts (2013) · cites this paper as Quinn et al. 2010

  • Quantitative social scientists typically test their assumptions by assessing construct reliability and construct validity.

    “In contrast to computer scientists, who focus primarily on out-ofsample prediction, quantitative social scientists typically test their assumptions by assessing construct reliability and construct validity.”

    From Measurement and Fairness (2021) · cites this paper as [49]

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Cites

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Research works printed but not linked: 7

  1. [6]6. Baumgartner, Frank R., and Bryan D. Jones, eds. 2002. Policy Dynamics. Chicago: University of Chicago Press.
  2. [9]9. Budge, Ian, Hans-Dieter Klingemann, Andrea Volkens, Judith Bara, and Eric Tannenbaum. 2001. Mapping Policy Prefer- ences: Parties, Electors and Governments, 1945-1998. Oxford: Oxford University Press.
  3. [24]24. Holsti, Ole R., Richard A. Brody, and Robert C. North. 1964. "Measuring Affect and Action in International Reaction Models: Empirical Materials from the 1962 Cuban Crisis." Journal of Peace Research 1(3/4): 170-90.
  4. [44]44. Miller, Warren E., and Donald Stokes. 1963. "Constituency In- fluence in Congress." American Political Science Review 57: 45-56.
  5. [58]58. Rothenberg, Lawrence S., and Mitchell S. Sanders. 2000. "Sev- ering the Electoral Connection: Shirking in the Contem- porary Congress." American Journal of Political Science 44: 316-25.
  6. [64]64. Stone, Philip J., Dexter C. Dunphy, Marshall S. Smith, and Daniel M. Ogilvie. 1966. The General Enquirer: A Computer Approach to Content Analysis. Cambridge, MA: MIT Press.
  7. [68]68. West, Mike, and Jeff Harrison. 1997. Bayesian Forecasting and Dynamic Models. New York: Springer.

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Related

Authors
Quinn, K. M., Monroe, B. L., Colaresi, M., Crespin, M. H., & Radev, D. R.
Affiliations
  • University of California, Berkeley (US)
  • Pennsylvania State University (US)
  • Michigan State University (US)
  • University of Georgia (US)
  • University of Michigan (US)
ORCID
Venue
American Journal of Political Science
Publisher
Wiley
Published
2009-12-28
Volume
54
Issue
1
Pages
209-228
ISSN
  • 0092-5853 (ISSN-L)
  • 1540-5907
DOI
10.1111/j.1540-5907.2009.00427.x
OpenAlex
W2144874182
Cited by
671
References
70
Citations by year
2026: 21 · 2025: 48 · 2024: 40 · 2023: 57 · 2022: 55 · 2021: 57 · 2020: 64 · 2019: 60 · 2018: 52 · 2017: 42 · 2016: 36 · 2015: 41 · 2014: 35 · 2013: 20 · 2012: 15
FWCI
72.81
Type
journal article
Language
en
Open access
Licence
Locations
American Journal of Political Science (publishedVersion, public-domain)
Funders
  • National Science Foundation (0527513, 0714688)
  • Center for Advanced Study in the Behavioral Sciences, Stanford University
  • Center for Advanced Study, University of Illinois at Urbana-Champaign
Topics
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Keywords
political attention, parliamentary speeches, topic modeling, topic hierarchy, United States Senate, congressional records, statistical learning, word choice

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