Language and ideology in congress

British Journal of Political Science

Volumn 42, Issue 1, 2012, Pages 31-55

  Diermeier, Daniel ^a   Godbout, Jean François ^b   Yu, Bei ^c   Kaufmann, Stefan ^d  

^a NORTHWESTERN UNIVERSITY   (United States)

^b UNIVERSITÉ DE MONTRÉAL   (Canada)

^c SYRACUSE UNIVERSITY   (United States)

^d NORTHWESTERN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 82655170415     PISSN: 00071234     EISSN: 14692112     Source Type: Journal    
DOI: 10.1017/S0007123411000160     Document Type: Article

References (92)

1. Keith T. Poole, 'Changing Minds? Not in Congress', Public Choice, 131(2007), 435-51.
2. Philip E. Converse, 'The Nature of Belief Systems in Mass Publics', in David E. Apter, ed., Ideology and Discontent (New York: The Free Press, 1964), pp. 206-61.
3. Converse, 'The Nature of Belief Systems and Mass Publics', p. 207.
4. Keith T. Poole and Howard Rosenthal, 'Patterns of Congressional Voting', American Journal of Political Science, 35(1991), 228-78;
5. Keith T. Poole and Howard Rosenthal, Congress: A Political-Economic History of Roll Call Voting (New York: Oxford University Press, 1997);
6. Nolan McCarty, Keith T. Poole and Howard Rosenthal, Income Redistribution and the Realignment of American Politics (Washington, D. C.: American Enterprise Institute, 1997);
7. Nolan McCarty, Keith T. Poole and Howard Rosenthal, Polarized America: The Dance of Ideology and Unequal Riches (Boston, Mass.: MIT Press, 2006);
8. Keith T. Poole and Howard Rosenthal, Ideology and Congress (New Brunswick, N. J.: Transaction Publishers, 2007).
9. Poole and Rosenthal, Congress.
10. Keith T. Poole, Spatial Models of Parliamentary Voting (New York: Cambridge University Press, 2005).
11. Initially, this finding met with widespread disbelief. See Poole and Rosenthal, Congress, p. 8.
12. However, the low-dimensionality of legislative voting has been confirmed by other scholars using different estimation methodologies, such as Bayesian procedures (Joshua Clinton, Simon Jackman and Doug Rivers, 'The Statistical Analysis of Roll Call Data', American Political Science Review, 98(2004), 355-70) (Pubitemid 38860581)
13. or factor analysis (James J. Heckman and James M. Snyder Jr, 'Linear Probability Models of the Demand for Attribution with an Empirical Application to Estimating the Preferences of Legislators', RAND Journal of Economics, 28(1997), S142-89) for estimating ideal points.
14. Institutional features such as gate-keeping powers of committees (Kenneth A. Shepsle and Barry R. Weingast, 'Structure-induced Equilibrium and Legislative Choice', Public Choice, 37(1981), 503-19), prefloor legislative activities (such as co-sponsorship)
15. strategic voting (Jeffery C. Talbert and Matthew Potoski, 'Setting the Legislative Agenda: The Dimensional Structure of Bill Cosponsoring and Floor Voting', Journal of Politics, 64(2002), 864-91) or institutional constraints such as the presidential veto
16. (Jason M. Roberts, 'The Statistical Analysis of Roll Call Data: A Cautionary Tale', Legislative Studies Quarterly, 22(2007), 341-60;
17. Joshua D. Clinton, 'Lawmaking and Roll Calls', Journal of Politics, 69(2007), 457-69) can all affect the measurement of ideal points and reduce the dimensionality of legislative voting in Congress. It is also possible that exogenous factors, such as electoral incentives, could help explain why parties aim to present a coherent legislative agenda, and avoid intra-party voting divisions.
18. Indeed, Snyder and Ting (James M. Snyder and Michael M. Ting, 'An Informational Rationale for Political Parties', American Journal of Political Science, 46(2002), 90-110; (Pubitemid 33374089)
19. James M. Snyder and Michael M. Ting, 'Party Labels, Roll Calls, and Elections', Political Analysis, 11(2003), 419-44) (Pubitemid 37453350)
20. and Woon and Pope (Jonathan Woon and Jeremy C. Pope, 'Made in Congress? Testing the Electoral Implications of Party Ideological Brand Names', Journal of Politics, 70(2008), 823-36) argue that parties can use their aggregate roll-call record to produce a coherent ideological brand name in order to communicate with the electorate. In this context, the observed unidimensionality in legislative voting would be facilitated by electoral incentives, rather than by institutional rules or agenda control.
21. On the Westminster style parliamentary systems, see Spirling and McLean (Arthur Spirling and Iain McLean, 'UK OC OK? Interpreting Optimal Classification Scores for the U. K. House of Commons', Political Analysis, 15(2006), 85-6).
22. On the US Congress case, see Clinton, 'Lawmaking and Roll Calls'
23. and Roberts, 'The Statistical Analysis of Roll Call Data'.
24. See, for example, the NPAT candidate survey of Stephen Ansolabehere, James M. Snyder Jr and Charles Stewart III, 'The Effects of Party and Preferences on Congressional Roll-Call Voting', Legislative Studies Quarterly, 26(2001), 533-72, which looks at the correlation between first factor NOMINATE and first factor NPAT scores; or the Poole and Rosenthal study of NOMINATE scores and interest group ratings
25. (Poole and Rosenthal, Congress;
26. Poole and Rosenthal, Ideology and Congress).
27. One such example is Cheryl Schonhardt-Bailey, 'The Congressional Debate on Partial-Birth Abortion: Constitutional Gravitas and Moral Passion', British Journal of Political Science, 38(2008), 383-410. In her study of the US Senate debates on partial-birth abortion, Schonhardt-Bailey identifies two dimensions of conflict, where the first dimension represents an emotive conflict over the abortion procedure, while the second dimension is related to the constitutionality of the bill. Schonhardt-Bailey argues that legislative voting correlates with this second dimension.
28. Ian Budge, Hans-Dieter Klingemann, Andrea Volkens, Judith Bara and Eric Tanenbaum, Mapping Policy Preferences: Estimates for Parties, Electors, and Governments 1945-1998 (Oxford: Oxford University Press, 2001);
29. Frank R. Baumgartner and Bryan D. Jones, Agendas and Instability in American Politics (Chicago: University of Chicago Press, 1993);
30. Frank R. Baumgartner and Bryan D. Jones, eds, Policy Dynamics (Chicago: University of Chicago Press, 2002);
31. Frank R. Baumgartner and Bryan D. Jones, The Politics of Attention: How Government Prioritizes Problems (Chicago: University of Chicago Press, 2005).
32. For examples, see Michael Laver and Kenneth Benoit, 'Locating TDs in Policy Spaces: Wordscoring Dail Speeches', Irish Political Studies, 17(2002), 59-73;
33. Michael Laver, Kenneth Benoit and John Garry, 'Extracting Policy Positions from Political Texts Using Words as Data', American Political Science Review, 97(2003), 311-37;
34. Kenneth Benoit and Michael Laver, 'Estimating Irish Party Positions Using Computer Wordscoring: The 2002 Elections', Irish Political Studies, 18(2003), 97-107; (Pubitemid 37450046)
35. Kenneth Benoit and Michael Laver, 'Mapping the Irish Policy Space: Voter and Party Spaces in Preferential Elections', Economic and Social Review, 36(2005), 83-108; (Pubitemid 41404373)
36. Burt L. Monroe and Ko Maeda, 'Rhetorical Ideal Point Estimation: Mapping Legislative Speech' (presented at the Society for Political Methodology, Palo Alto: Stanford University, 2004);
37. Adam F. Simon and Michael Xenos, 'Dimensional Reduction of Word-frequency Data as a Substitute for Intersubjective Content Analysis', Political Analysis, 12(2004), 63-75;
38. Jonathan B. Slapin and Sven O. Proksch, 'A. Scaling Model for Estimating Time-Series Party Positions from Texts', American Journal of Political Science, 52(2008), 705-22;
39. Kevin M. Quinn, Burt L. Monroe, Michael Colaresi, Michael H. Crespin and Dragomir R. Radev, 'How to Analyze Political Attention with Minimal Assumptions and Costs', American Journal of Political Science, 54(2010), 209-28.
40. For a recent review, see Burt Monroe and Philipp A. Schrodt, 'Introduction to the Special Issue: The Analysis of Political Text', Political Analysis, 16(2008), 351-5;
41. and also Ken Cousins and Wayne McIntosh, 'More than Typewriters, More than Adding Machines: Integrating Information Technology into Political Research', Quality and Quantity, 39(2005), 591-614;
42. and Tae Yano, William W. Cohen and Noah A. Smith, 'Predicting Response to Political Blog Posts with Topic Models', Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics conference (NAACL) (2009), 477-85.
43. Examples include Laver, Benoit and Garry, 'Extracting Policy Positions from Political Texts Using Words as Data'.
44. See also Benoit and Laver, 'Estimating Irish Party Positions Using Computer Wordscoring';
45. Benoit and Laver, 'Mapping the Irish Policy Space';
46. Laver and Benoit, 'Locating TDs in Policy Spaces';
47. Kenneth Benoit, Michael Laver, Christine Arnold, Paul Pennings and Madeleine O. Hosli, 'Measuring National Delegate Positions at the Convention on the Future of Europe Using Computerized Wordscoring', European Union Politics, 6(2005), 291-313. (Pubitemid 41826229)
48. For a critical view, see Ian Budge and Paul Pennings, 'Do They work? Validating Computerized Word Frequency Estimates against Policy Series', Electoral Studies, 26(2007), 121-9. (Pubitemid 46202129)
49. Monroe and Maeda, 'Rhetorical Ideal Point Estimation';
50. Slapin and Proksch, 'A Scaling Model for Estimating Time-Series Party Positions from Texts'.
51. Stephen Purpura and Dustin Hillard, 'Automated Classification of Congressional Legislation', Proceedings of the 2006 International Conference on Digital Government Research (2006), 219-25, retrieved 28 May 2007, from the ACM Digital Library;
52. Bo Pang, Lillian Lee and Shivakumar Vaithyanathan, 'Thumbs up? Sentiment Classification Using Machine Learning Techniques', Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, (2002), 79-86, retrieved 28 May 2007, from the ACM Digital Library;
53. Matt Thomas, Bo Pang and Lillian Lee, 'Get out the Vote: Determining Support or Opposition from Congressional Floor-debate Transcripts', Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (2006), 327-35, retrieved from the ACL Digital Archive, predicted speakers' opinions about a specific bill (support or opposition) based on their speeches. Their classifier was trained on 2, 740 speech segments in 38 bill debates and achieved an accuracy of 66 per cent in predicting the opinions expressed in 860 speech segments from ten different legislative debates.
54. We used the Poole and Rosenthal DW-NOMINATE SCORES available at http://voteview.com/dwnomin.htm.
55. The performance of classification algorithms is tested using common benchmark datasets. The Reuters-21578 news collection, the OHSUMED Medline abstract collection, and the 20 Usenet newsgroups collection are the most widely used benchmark datasets. The Reuters-21578 collection is available at http://kdd.ics.uci.edu/databases/20newsgroups/20newsgroups.html. The OHSUMED collection is available at http://trec.nist.gov/data/t9-filtering.html. The 20 newsgroups collection is available at http://kdd.ics.uci.edu/databases/ 20newsgroups.html.
56. Susan Dumais, John Platt, David Heckerman and Mehran Sahami, 'Inductive Learning Algorithms and Representations for Text Categorization', Proceedings of the 7th International Conference on Information and Knowledge Management (1998), 48-155, retrieved 28 May 2007, from the ACM Digital Library;
57. Isabelle Guyon, Jason Weston, Stephen Barnhilland, Vladimir Vapnik, 'Gene Selection for Cancer Classification Using Support Vector Machines', Machine Learning, 46(2002), 389-422; (Pubitemid 34129977)
58. George Forman, 'An Extensive Empirical Study of Feature Selection Metrics for Text Categorization', Journal of Machine Learning Research, 3(2003), 1289-305;
59. Thorsten Joachims, 'Text Categorization with Support Vector Machines: Learning with Many Relevant Features', 10th European Conference on Machine Learning, Vol. 1398 of Lecture Notes in Computer Science (Berlin: Springer Verlag, 1998), pp. 137-42; (Pubitemid 128067178)
60. Dunja Mladenic, Janez Brank, Marko Grobelnik and Natasa Milic-Frayling, 'Feature Selection Using Linear Classifier Weights: Interaction with Classification Models', Proceedings of the 27nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'04), (Sheffield: 25-29 July 2004), pp. 234-41;
61. Yiming Yang and Xin Liu, 'A Re-evaluation of Text Categorization Methods', Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (1999), 42-9, retrieved 28 May 2007, from the ACM Digital Library;
62. Fabrizio Sebastiani, 'Machine Learning in Automated Text Categorization', ACM Computing Surveys, 34(2002), 1-47. We also compared our SVM algorithm to naïve Bayes, another popular classification method. Our experiment results show that SVM is slightly superior to naïve Bayes for ideological position classification.
63. Pang, Lee and Vaithyanathan, 'Thumbs up?'
64. Vladimir Vapnik, Estimating of Dependences Based on Empirical Data (New York: Springer-Verlag, 1982);
65. Corinna Cortes and Vladimir Vapnik, 'Support-vector Networks', Machine Learning, 20(1995), 273-97;
66. Vladimir Vapnik, The Nature of Statistical Learning Theory (New York: Springer-Verlag, 1999).
67. light package with its default setting in this study.
68. See Chih C. Chang and Chih J. Lin, 'LIBSVM: A Library for Support Vector Machines' (2001), Software available at http://www.csie.ntu.edu.tw/, cjlin/libsvm.
69. David D. Lewis, 'An Evaluation of Phrasal and Clustered Representations on a Text Categorization Task', Proceedings of the 15th Annual International Conference on Research and Development of Information Retrieval (1992), pp. 37-50, retrieved 28 May 2007, from the ACM Digital Library;
70. William W. Cohen and Yoram Singer, 'Context-sensitive Learning Methods for Text Categorization', ACM Transactions on Information Systems, 17(1999), 141-73; (Pubitemid 129541510)
71. Sam Scott and Stan Matwin, 'Feature Engineering for Text Classification', Proceedings of the 16th International Conference on Machine Learning (San Francisco: Morgan Kaufmann, 1999), pp. 379-88;
72. Alessandro Moschitti and Roberto Basili, 'Complex Linguistic Features for Text Classification: A Comprehensive Study', European Conference on Information Retrieval, Vol. 2997 of Lecture Notes in Computer Science (Berlin: Springer Verlag, 2004), pp. 181-96.
73. Kushal Dave, Steve Lawrence and David M. Pennock, 'Mining the Peanut Gallery: Opinion Extraction and Semantic Classification of Product Reviews', Proceedings of the 12th International Conference on World Wide Web (2003), 519-2, retrieved 28 May 2007, from the ACM Digital Library;
74. Pang, Lee and Vaithyanathan, 'Thumbs up?'
75. Aidan Finn and Nicholas Kushmerick, 'Learning to Classify Documents according to Genre', Journal of American Society of Information Science and Technology, 57(2006), 1506-18. For example, some typical adjectives in movie reviews (like hilarious and boring) are unlikely to occur in restaurant reviews, although some opinion descriptors (like terrific and bad) are universal. (Pubitemid 44351045)
76. Bei Yu, Daniel Diermeier and Stefan Kaufmann 'Classifying Party Affiliation from Political Speech', Journal of Information Technology & Politics, 5(2008), 33-48.
77. M. F. Porter, 'An Algorithm for Suffix Stripping', Program, 14(1980), 130-7.
78. This is a standard approach in classification tasks; see, e.g., Tom Mitchell, Machine Learning (Toronto: McGraw Hill, 1997). An alternative approach consists in setting aside a sizeable portion of the data as a 'held-out' set which is ignored during training and only used for testing. This approach is sound for datasets with large numbers of labelled examples. However, for small datasets such as ours, it is problematic since the arbitrary training/test split may accidentally lead to two datasets that are unlikely to have been produced by the same source.
79. This is related to the literature on framing. For a recent review, see Jamie Druckman and Dennis Chong, 'Framing Theory', Annual Review of Political Science, 10(2007), 103-26.
80. To compare the two chambers directly, it is necessary to use a common space score for both the House and the Senate. See, for example, Royce Carroll, Jeff Lewis, James Lo, Nolan McCarty, Keith Poole and Howard Rosenthal, "'Common Space" (Joint House and Senate) DW-NOMINATE Scores with Bootstrapped Standard Errors' (2009).
81. The kappa coefficient is often used to measure inter-rater agreement in annotation. We followed the kappa computation procedure described at http://faculty.vassar.edu/lowry/kappa.html.
82. Bei Yu, Stefan Kaufmann and Daniel Diermeier, 'Classifying Party Affiliation from Political Speech' Journal of Information Technology and Politics, 5(2008), 33-48. The lower accuracy is a consequence of a smaller dataset.
83. Bei Yu, Stefan Kaufmann and Daniel Diermeier, 'Exploring the Characteristics of Opinion Expressions for Political Opinion Classification', Proceedings of the 9th Annual International Conference on Digital Government Research (dg.o 2008) (Montreal, May 2008), pp. 82-9.
84. Bjørn Høyland and Jean-Francois Godbout, 'Predicting Party Group Affiliation from European Parliament Debates' (paper presented at the European Consortium for Political Research Meeting of the Standing Group on the European Union (Riga: Latvia, 2008)).
85. Poole, Spatial Models of Parliamentary Voting.
86. Except during the Era of Good Feelings (1817-25) and the period surrounding the Civil War (1853-76);
87. Poole and Rosenthal, 'Congress';
88. Poole and Rosenthal, Ideology and Congress.
89. See, for example, George Lakoff, Moral Politics: How Liberals and Conservatives Think (Chicago: The University of Chicago Press, 2002).
90. Edda Leopold and Jorg Kindermann, 'Text Categorization with Support Vector Machines: How to Represent Texts in Input Space?' Machine Learning, 46(2002), 423-44. (Pubitemid 34129978)
91. light'.
92. Chang and Lin, 'Library for Support Vector Machines'.