Text as data: The promise and pitfalls of automatic content analysis methods for political texts

Political Analysis

Volumn 21, Issue 3, 2013, Pages 267-297

  Grimmer, Justin ^a   Stewart, Brandon M ^b  

^a STANFORD UNIVERSITY   (United States)

^b HARVARD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84880655688     PISSN: 10471987     EISSN: 14764989     Source Type: Journal    
DOI: 10.1093/pan/mps028     Document Type: Article

References (88)

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