Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-24T14:03:55.478Z Has data issue: false hasContentIssue false
  • English
  • Français

Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts

Part of: PA editors' choice articles

Published online by Cambridge University Press:  04 January 2017

Justin Grimmer  and
Brandon M. Stewart
Justin Grimmer*
Affiliation:
Department of Political Science, Stanford University, Encina Hall West 616 Serra Street, Stanford, CA 94305
Brandon M. Stewart
Affiliation:
Department of Government and Institute for Quantitative Social Science, Harvard University, 1737 Cambridge Street, Cambridge, MA 02138 e-mail: [email protected]
*
e-mail: [email protected] (corresponding author)
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Politics and political conflict often occur in the written and spoken word. Scholars have long recognized this, but the massive costs of analyzing even moderately sized collections of texts have hindered their use in political science research. Here lies the promise of automated text analysis: it substantially reduces the costs of analyzing large collections of text. We provide a guide to this exciting new area of research and show how, in many instances, the methods have already obtained part of their promise. But there are pitfalls to using automated methods—they are no substitute for careful thought and close reading and require extensive and problem-specific validation. We survey a wide range of new methods, provide guidance on how to validate the output of the models, and clarify misconceptions and errors in the literature. To conclude, we argue that for automated text methods to become a standard tool for political scientists, methodologists must contribute new methods and new methods of validation.

Type
Research Article
Information
Political Analysis , Volume 21 , Issue 3 , Summer 2013 , pp. 267 - 297
Copyright
Copyright © The Author 2013. Published by Oxford University Press on behalf of the Society for Political Methodology 

Footnotes

Authors' note: For helpful comments and discussions, we thank participants in Stanford University's Text as Data class, Mike Alvarez, Dan Hopkins, Gary King, Kevin Quinn, Molly Roberts, Mike Tomz, Hanna Wallach, Yuri Zhurkov, and Frances Zlotnick. Replication data are available on the Political Analysis Dataverse at http://hdl.handle.net/1902.1/18517. Supplementary materials for this article are available on the Political Analysis Web site.

References

Adler, E. Scott, and Wilkerson, John. 2011. The Congressional bills project. http://www.congressionalbills.org.CrossRefGoogle Scholar
Ansolabehere, Stephen, and Iyengar, Shanto. 1995. Going negative: How political advertisements shrink and polarize the electorate. New York, NY: Simon & Schuster.Google Scholar
Armstrong, J. S. 1967. Derivation of theory by means of factor analysis or Tom Swift and his electric factor analysis machine. The American Statistician 21(1): 17–21.Google Scholar
Ashworth, Scott, and Bueno de Mesquita, Scott. 2006. Delivering the goods: Legislative particularism in different electoral and institutional settings. Journal of Politics 68(1): 168–79.CrossRefGoogle Scholar
Beauchamp, Nick. 2011. Using text to scale legislatures with uninformative voting. New York University Mimeo.Google Scholar
Benoit, K., Laver, M., and Mikhaylov, S. 2009. Treating words as data with error: Uncertainty in text statements of policy positions. American Journal of Political Science 53(2): 495–513.CrossRefGoogle Scholar
Berinsky, Adam, Huber, Greg, and Lenz, Gabriel. 2012. Using mechanical turk as a subject recruitment tool for experimental research. Political Analysis 20: 351–68.Google Scholar
Bishop, Christopher. 1995. Neural networks for pattern recognition. Gloucestershire, UK: Clarendon Press.CrossRefGoogle Scholar
Bishop, Christopher. 2006. Pattern recognition and machine learning. New York, NY: Springer.Google Scholar
Blei, David. 2012. Probabilistic topic models. Communications of the ACM 55(4): 77–84.Google Scholar
Blei, David, Ng, Andrew, and Jordan, Michael. 2003. Latent dirichlet allocation. Journal of Machine Learning and Research 3: 993–1022.Google Scholar
Blei, David, and Jordan, Michael. 2006. Variational inference for dirichlet process mixtures. Journal of Bayesian Analysis 1(1): 121–44.Google Scholar
Bonica, Adam. 2011. Estimating ideological positions of candidates and contributions from campaign finance records. Stanford University Mimeo.Google Scholar
Bradley, M. M., and Lang, P. J. 1999. Affective Norms for English Words (ANEW): Stimuli, instruction, manual and affective ratings. University of Florida Mimeo.Google Scholar
Breiman, L. 2001. Random Forests. Machine Learning 45: 5–32.CrossRefGoogle Scholar
Budge, Ian, and Pennings, Paul. 2007. Do they work? Validating computerised word frequency estimates against policy series. Electoral Studies 26: 121–29.CrossRefGoogle Scholar
Burden, Barry, and Sanberg, Joseph. 2003. Budget rhetoric in presidential campaigns from 1952 to 2000. Political Behavior 25(2): 97–118.CrossRefGoogle Scholar
Chang, Jonathan, Boyd-Graber, Jordan, Wang, Chong, Gerrish, Sean, and Blei, David M. 2009. Reading tea leaves: How humans interpret topic models. In Advances in neural information processing systems, eds. Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C. K. I., and Culotta, A., 288–96. Cambridge, MA: The MIT Press.Google Scholar
Cleveland, William S. 1979. Robust locally weighted regression and scatterplots. Journal of the American Statistical Association 74(368): 829–36.Google Scholar
Clinton, Joshua, Jackman, Simon, and Rivers, Douglas. 2004. The statistical analysis of roll call data. American Political Science Review 98(02): 355–70.CrossRefGoogle Scholar
Dempster, Arthur, Laird, Nathan, and Rubin, Donald. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39(1): 1–38.Google Scholar
Diermeier, Daniel, Godbout, Jean-Francois, Yu, Bei, and Kaufmann, Stefan. 2011. Language and ideology in Congress. British Journal of Political Science 42(1): 31–55.Google Scholar
Dietterich, T. 2000. Ensemble methods in machine learning. Multiple Classifier Systems 1–15.CrossRefGoogle Scholar
Efron, Bradley, and Gong, Gail. 1983. A leisurely look at the bootstrap, the jackknife, and cross-validation. American Statistician 37(1): 36–48.Google Scholar
Eggers, Andy, and Hainmueller, Jens. 2009. MPs for sale? Returns to office in postwar British politics. American Political Science Review 103(04): 513–33.CrossRefGoogle Scholar
Eshbaugh-Soha, Matthew. 2010. The tone of local presidential news coverage. Political Communication 27(2): 121–40.CrossRefGoogle Scholar
Fenno, Richard. 1978. Home style: House members in their districts. Boston, MA: Addison Wesley.Google Scholar
Frey, Brendan, and Dueck, Delbert. 2007. Clustering by passing messages between data points. Science 315(5814): 972–6.Google Scholar
Gelpi, C., and Feaver, P. D. 2002. Speak softly and carry a big stick? Veterans in the political elite and the American use of force. American Political Science Review 96(4): 779–94.CrossRefGoogle Scholar
Gerber, Elisabeth, and Lewis, Jeff. 2004. Beyond the median: Voter preferences, district heterogeneity, and political representation. Journal of Political Economy 112(6): 1364–83.CrossRefGoogle Scholar
Greene, William. 2007. Econometric analysis. 6th ed. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Grimmer, Justin. 2010. A Bayesian hierarchical topic model for political texts: Measuring expressed agendas in senate press releases. Political Analysis 18(1): 1–35.CrossRefGoogle Scholar
Grimmer, Justin. Forthcoming 2012. Appropriators not position takers: The distorting effects of electoral incentives on congressional representation. American Journal of Political Science.CrossRefGoogle Scholar
Grimmer, Justin, and King, Gary. 2011. General purpose computer-assisted clustering and conceptualization. Proceedings of the National Academy of Sciences 108(7): 2643–50.CrossRefGoogle ScholarPubMed
Hand, David J. 2006. Classifier technology and the illusion of progress. Statistical Science 21(1): 1–15.Google Scholar
Hart, R. P. 2000. Diction 5.0: The text analysis program. Thousand Oaks, CA: Sage-Scolari.Google Scholar
Hastie, Trevor, Tibshirani, Robert, and Friedman, Jerome. 2001. The elements of statistical learning. New York, NY: Springer.CrossRefGoogle Scholar
Hillard, Dustin, Purpura, Stephen, and Wilkerson, John. 2008. Computer-assisted topic classification for mixed-methods social science research. Journal of Information Technology & Politics 4(4): 31–46.CrossRefGoogle Scholar
Hofmann, Thomas. 1999. Probabilistic latent semantic indexing. Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 50–7.CrossRefGoogle Scholar
Hopkins, Daniel, and King, Gary. 2010. Extracting systematic social science meaning from text. American Journal of Political Science 54(1): 229–47.Google Scholar
Hopkins, Daniel, King, Gary, Knowles, Matthew, and Melendez, Steven. 2010. ReadMe: Software for automated content analysis. http://gking.harvard.edu/readme.Google Scholar
Jackman, Simon. 2006. Data from Web into R. The Political Methodologist 14(2): 11–6.Google Scholar
Jain, A. K., Murty, M. N., and Flynn, P. J. 1999. Data clustering: A review. ACM Computing Surveys 31(3): 264–323.CrossRefGoogle Scholar
Jones, Bryan, Wilkerson, John, and Baumgartner, Frank. 2009. The policy agendas project. http://www.policyagendas.org.Google Scholar
Jurafsky, Dan, and Martin, James. 2009. Speech and natural language processing: An introduction to natural language processing, computational linguistics, and speech recognition. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Jurka, Timothy P., Collingwood, Loren, Boydstun, Amber, Grossman, Emiliano, and van Atteveldt, Wouter. 2012. RTextTools: Automatic text classification via supervised learning. http://cran.r-project.org/web/packages/RTextTools/index.html.Google Scholar
Kellstedt, Paul. 2000. Media framing and the dynamics of racial policy preferences. American Journal of Political Science 44(2): 245–60.CrossRefGoogle Scholar
Krippendorff, Klaus. 2004. Content analysis: An introduction to its methodology. New York: Sage.Google Scholar
Krosnick, Jon. 1999. Survey research. Annual Review of Psychology 50(1): 537–67.CrossRefGoogle ScholarPubMed
Laver, Michael, and Garry, John. 2000. Estimating policy positions from political texts. American Journal of Political Science 44(3): 619–34.CrossRefGoogle Scholar
Laver, Michael, Benoit, Kenneth, and Garry, John. 2003. Extracting policy positions from political texts using words as data. American Political Science Review 97(02): 311–31.CrossRefGoogle Scholar
Lodhi, H., Saunders, C., Shawe-Taylor, J., Christianini, N., and Watkins, C. 2002. Text classifications using string kernels. Journal of Machine Learning Research 2: 419–44.Google Scholar
Loughran, Tim, and McDonald, Bill. 2011. When is a liability not a liability? Textual analysis, dictionaries, and 10-Ks. Journal of Finance 66(1): 35–65.Google Scholar
Lowe, Will. 2008. Understanding wordscores. Political Analysis 16(4): 356–71.Google Scholar
Lowe, Will, Benoit, Ken, Mihaylov, Slava, and Laver, M. 2011. Scaling policy preferences from coded political texts. Legislative Studies Quarterly 36(1): 123–55.CrossRefGoogle Scholar
MacQueen, J. 1967. Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1: 281–97. London, UK: Cambridge University Press.Google Scholar
Manning, Christopher, Raghavan, Prabhakar, and Schütze, Hinrich. 2008. Introduction to information retrieval. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Maron, M. E., and Kuhns, J. L. 1960. On relevance, probabilistic indexing, and information retrieval. Journal of the Association for Computing Machinery 7(3): 216–44.Google Scholar
Martin, Lanny, and Vanberg, Georg. 2007. A robust transformation procedure for interpreting political text. Political Analysis 16(1): 93–100.Google Scholar
Mayhew, David. 1974. Congress: The electoral connection. New Haven, CT: Yale University Press.Google Scholar
Mikhaylov, S., Laver, M., and Benoit, K. 2010. Coder reliability and misclassification in the human coding of party manifestos. 66th MPSA annual national conference, Palmer House Hilton Hotel and Towers.Google Scholar
Monroe, Burt, and Maeda, Ko. 2004. Talk's cheap: Text-based estimation of rhetorical ideal points. Paper presented at the 21st annual summer meeting of the Society of Political Methodology.Google Scholar
Monroe, Burt, Colaresi, Michael, and Quinn, Kevin. 2008. Fightin' words: Lexical feature selection and evaluation for identifying the content of political conflict. Political Analysis 16(4): 372.CrossRefGoogle Scholar
Mosteller, F., and Wallace, D. L. 1963. Inference in an authorship problem. Journal of the American Statistical Association 58: 275–309.Google Scholar
Neuendorf, K. A. 2002. The content analysis guidebook. Thousand Oaks, CA: Sage Publications, Inc.Google Scholar
Ng, Andrew, Jordan, Michael, and Weiss, Yair. 2001. On spectral clustering: Analysis and an algorithm. In Advances in neural information processing systems 14: Proceeding of the 2001 conference, eds. Dietterich, T., Becker, S., and Gharamani, Z., 849–56. Cambridge, MA: The MIT Press.Google Scholar
Pang, B., Lee, L., and Vaithyanathan, S. 2002. Thumbs up?: Sentiment classification using machine learning techniques. Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing 10: 79–86.CrossRefGoogle Scholar
Pennebaker, James, Francis, Martha, and Booth, Roger. 2001. Linguistic inquiry and word count: LIWC 2001. Mahway, NJ: Erlbaum Publishers.Google Scholar
Poole, Keith, and Rosenthal, Howard. 1997. Congress: A political-economic history of roll call voting. Oxford, UK: Oxford University Press.Google Scholar
Porter, Martin. 1980. An algorithm for suffix stripping. Program 14(3): 130–37.CrossRefGoogle Scholar
Quinn, Kevin. 2010. How to analyze political attention with minimal assumptions and costs. American Journal of Political Science 54(1): 209–28.CrossRefGoogle Scholar
Schrodt, Philip. 2000. Pattern recognition of international crises using Hidden Markov Models. In Political complexity: Nonlinear models of politics, ed. Richards, Diana, 296–328. Ann Arbor, MI: University of Michigan Press.Google Scholar
Schrodt, Philip A. 2006. Twenty years of the Kansas event data system project. Political Methodologist 14(1): 2–6.Google Scholar
Slapin, Jonathan, and Proksch, Sven-Oliver. 2008. A scaling model for estimating time-series party positions from texts. American Journal of Political Science 52(3): 705–22.CrossRefGoogle Scholar
Spirling, Arthur. 2012. US treaty-making with American Indians. American Journal of Political Science 56(1): 84–97.CrossRefGoogle Scholar
Spirling, Arthur, and McLean, Iain. 2007. UK OC OK? Interpreting optimal classification scores for the UK House of Commons. Political Analysis 15(1): 85–96.CrossRefGoogle Scholar
Stewart, Brandon M., and Zhukov, Yuri M. 2009. Use of force and civil-military relations in Russia: An automated content analysis. Small Wars & Insurgencies 20: 319–43.Google Scholar
Stone, Phillip, Dunphy, Dexter, Smith, Marshall, and Ogilvie, Daniel. 1966. The general inquirer: A computer approach to content analysis. Cambridge, MA: The MIT Press.Google Scholar
Taddy, Matthew A. 2010. Inverse regression for analysis of sentiment in text. Arxiv preprint arXiv:1012.2098.Google Scholar
Turney, P., and Littman, M. L. 2003. Measuring praise and criticism: Inference of semantic orientation from association. ACM Transactions on Information Systems (TOIS) 21(4): 315–46.CrossRefGoogle Scholar
van der Laan, Mark, Polley, Eric, and Hubbard, Alan. 2007. Super learner. Statistical Applications in Genetics and Molecular Biology 6(1): 1544–6115.Google Scholar
van der Vaart, A. W., Dudoit, S., and van der Laan, M. J. 2006. Oracle inequalities for multifold cross validation. Statistics and Decisions 24(3): 351–71.Google Scholar
Venables, W. N., and Ripley, B. D. 2002. Modern applied statistics with S. 4th ed. New York: Springer.CrossRefGoogle Scholar
Wallach, Hanna, Dicker, Lee, Jensen, Shane, and Heller, Katherine. 2010. An alternative prior for nonparametric Bayesian Clustering. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 9: 892–99.Google Scholar
Weber, Robert P. 1990. Basic content analysis. Newbury Park, CA: Sage University Paper Series on Quantitative Applications in the Social Sciences.CrossRefGoogle Scholar
Weingast, Barry, Shepsle, Kenneth, and Johnsen, Christopher. 1981. The political economy of benefits and costs: A neoclassical approach to distributive politics. The Journal of Political Economy 89(4): 642.CrossRefGoogle Scholar
Yiannakis, Diana Evans. 1982. House members' communication styles: Newsletter and press releases. The Journal of Politics 44(4): 1049–71.Google Scholar
Young, Lori, and Soroka, Stuart. 2011. Affective news: The automated coding of sentiment in political texts. Political Communication 29(2): 205–31.Google Scholar
Political Analysis (2013) 21:350–367 CrossRefGoogle Scholar