Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts
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[1] Samuel B. Williams,et al. Association for Computing Machinery , 2009 .
[2] M. E. Maron,et al. On Relevance, Probabilistic Indexing and Information Retrieval , 1960, JACM.
[3] F. Mosteller,et al. Inference in an Authorship Problem , 1963 .
[4] J. Armstrong,et al. Derivation of Theory by Means of Factor Analysis or Tom Swift and His Electric Factor Analysis Machine , 2015 .
[5] Philip J. Stone,et al. Extracting Information. (Book Reviews: The General Inquirer. A Computer Approach to Content Analysis) , 1967 .
[6] J. MacQueen. Some methods for classification and analysis of multivariate observations , 1967 .
[7] Marshall S. Smith,et al. The general inquirer: A computer approach to content analysis. , 1967 .
[8] William E. Grieb. The general inquirer: A computer approach to content analysis: Philip J. Stone, Dexter C. Dunphy, Marshall S. Smith, Daniel M. Ogilvie, with associates. The MIT Press, Cambridge, Massachusetts, 1966. 651 pp. plus xx , 1968 .
[9] H. L. Le Roy,et al. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV , 1969 .
[10] David R. Mayhew. Congress: The Electoral Connection , 1975 .
[11] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[12] Richard F. Fenno. Home Style : House Members in Their Districts , 1978 .
[13] W. Cleveland. Robust Locally Weighted Regression and Smoothing Scatterplots , 1979 .
[14] Klaus Krippendorff,et al. Content Analysis: An Introduction to Its Methodology , 1980 .
[15] Martin F. Porter,et al. An algorithm for suffix stripping , 1997, Program.
[16] Kenneth A. Shepsle,et al. The Political Economy of Benefits and Costs: A Neoclassical Approach to Distributive Politics , 1981, Journal of Political Economy.
[17] Diana Evans Yiannakis. House Members' Communication Styles: Newsletters and Press Releases , 1982, The Journal of Politics.
[18] B. Efron,et al. A Leisurely Look at the Bootstrap, the Jackknife, and , 1983 .
[19] M. Aldenderfer,et al. Cluster Analysis. Sage University Paper Series On Quantitative Applications in the Social Sciences 07-044 , 1984 .
[20] R. Weber. Basic Content Analysis , 1986 .
[21] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[22] S. Iyengar,et al. Going Negative: How Political Advertisements Shrink and Polarize the Electorate , 1995 .
[23] R. Morgan. Genetics and molecular biology. , 1995, Current opinion in lipidology.
[24] K. T. Poole,et al. Congress: A Political-Economic History of Roll Call Voting , 1997 .
[25] Janet M. Martin. Congress: A Political-Economic History of Roll Call Voting . By Keith T. Poole and Howard Rosenthal. (New York: Oxford University Press, 1997. Pp. 297. $85.00.) , 1998 .
[26] J. Krosnick,et al. Survey research. , 1999, Annual review of psychology.
[27] Anil K. Jain,et al. Data clustering: a review , 1999, CSUR.
[28] M. Bradley,et al. Affective Norms for English Words (ANEW): Instruction Manual and Affective Ratings , 1999 .
[29] Thomas G. Dietterich. Ensemble Methods in Machine Learning , 2000, Multiple Classifier Systems.
[30] Paul M. Kellstedt. The Mass Media and the Dynamics of American Racial Attitudes: Media Framing and the Dynamics of Racial Policy Preferences , 2000 .
[31] Virginia Reviewer-Teller,et al. Review of Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition by Daniel Jurafsky and James H. Martin. Prentice Hall 2000. , 2000 .
[32] M. Laver,et al. Estimating policy positions from political texts , 2000 .
[33] James H. Martin,et al. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition , 2000 .
[34] Diana Richards,et al. Political Complexity: Nonlinear Models of Politics , 2000 .
[35] Nello Cristianini,et al. Classification using String Kernels , 2000 .
[36] Michael I. Jordan,et al. On Spectral Clustering: Analysis and an algorithm , 2001, NIPS.
[37] Kimberly A. Neuendorf,et al. The Content Analysis Guidebook , 2001 .
[38] Bo Pang,et al. Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.
[39] Beyond the Median : Voter Preferences , District Heterogeneity , and Representation 1 , 2002 .
[40] Speak Softly and Carry a Big Stick? Veterans in the Political Elite and the American Use of Force , 2002, American Political Science Review.
[41] Michael L. Littman,et al. Measuring praise and criticism: Inference of semantic orientation from association , 2003, TOIS.
[42] M. Laver,et al. Extracting Policy Positions from Political Texts Using Words as Data , 2003, American Political Science Review.
[43] Barry C. Burden,et al. Budget Rhetoric in Presidential Campaigns from 1952 to 2000 , 2003 .
[44] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[45] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[46] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[47] Elisabeth R. Gerber,et al. Beyond the Median: Voter Preferences, District Heterogeneity, and Political Representation , 2004, Journal of Political Economy.
[48] Joshua D. Clinton,et al. The Statistical Analysis of Roll Call Data , 2004, American Political Science Review.
[49] Pranab Kumar Sen,et al. Statistics and Decisions , 2006 .
[50] A. V. D. Vaart,et al. Oracle inequalities for multi-fold cross validation , 2006 .
[51] Michael C. Herron. Twenty Years of the Kansas Event Data System Project , 2006 .
[52] David J. Hand,et al. Classifier Technology and the Illusion of Progress , 2006, math/0606441.
[53] Michael I. Jordan,et al. Variational inference for Dirichlet process mixtures , 2006 .
[54] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[55] Sven-Oliver Proksch,et al. A Scaling Model for Estimating Time-Series Party Positions from Texts , 2007 .
[56] M. J. van der Laan,et al. Statistical Applications in Genetics and Molecular Biology Super Learner , 2010 .
[57] Mark J van der Laan,et al. Super Learning: An Application to the Prediction of HIV-1 Drug Resistance , 2007, Statistical applications in genetics and molecular biology.
[58] I. McLean,et al. UK OC OK? Interpreting Optimal Classification Scores for the U.K. House of Commons , 2007, Political Analysis.
[59] Delbert Dueck,et al. Clustering by Passing Messages Between Data Points , 2007, Science.
[60] Lanny W. Martin,et al. A Robust Transformation Procedure for Interpreting Political Text , 2007, Political Analysis.
[61] Philip A. Schrodt. Pattern Recognition of International Crises using Hidden Markov Models , 2007 .
[62] I. Budge,et al. Do they work?: Validating computerised word frequency estimates against policy series , 2007 .
[63] James W. Pennebaker,et al. Linguistic Inquiry and Word Count (LIWC2007) , 2007 .
[64] Gary King,et al. Extracting Systematic Social Science Meaning from Text 1 , 2007 .
[65] Dustin Hillard,et al. Computer-Assisted Topic Classification for Mixed-Methods Social Science Research , 2008 .
[66] Legislative Productivity in Comparative Perspective: An Introduction to the Comparative Agendas Project , 2008 .
[67] W. Lowe,et al. Understanding Wordscores , 2008, Political Analysis.
[68] Burt L. Monroe,et al. Fightin' Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict , 2008, Political Analysis.
[69] Jens Hainmueller,et al. MPs for Sale? Returns to Office in Postwar British Politics , 2009, American Political Science Review.
[70] Brandon M. Stewart,et al. Use of force and civil–military relations in Russia: an automated content analysis , 2009 .
[71] N. Stanietsky,et al. The interaction of TIGIT with PVR and PVRL2 inhibits human NK cell cytotoxicity , 2009, Proceedings of the National Academy of Sciences.
[72] Kenneth Benoit,et al. Treating Words as Data with Error: Uncertainty in Text Statements of Policy Positions , 2009 .
[73] W. Greene,et al. 计量经济分析 = Econometric analysis , 2009 .
[74] Chong Wang,et al. Reading Tea Leaves: How Humans Interpret Topic Models , 2009, NIPS.
[75] Speak softly and carry a big stick , 2010 .
[76] Tim Loughran,et al. When is a Liability not a Liability? Textual Analysis, Dictionaries, and 10-Ks , 2010 .
[77] Dragomir R. Radev,et al. How to Analyze Political Attention with Minimal Assumptions and Costs , 2010 .
[78] Christopher D. Manning,et al. Introduction to Information Retrieval , 2010, J. Assoc. Inf. Sci. Technol..
[79] David M. Blei,et al. Probabilistic topic models , 2012, Commun. ACM.
[80] Gary King,et al. ReadMe: Software for Automated Content Analysis , 2010 .
[81] Matthew Eshbaugh-Soha,et al. The Tone of Local Presidential News Coverage , 2010 .
[82] Justin Grimmer,et al. A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases , 2010, Political Analysis.
[83] Katherine A. Heller,et al. An Alternative Prior Process for Nonparametric Bayesian Clustering , 2008, AISTATS.
[84] William N. Venables,et al. Modern Applied Statistics with S , 2010 .
[85] Matt Taddy,et al. Inverse Regression for Analysis of Sentiment in Text , 2010 .
[86] Slava J. Mikhaylov,et al. Scaling policy preferences from coded political texts , 2011 .
[87] Stefan Kaufmann,et al. Language and Ideology in Congress , 2011, British Journal of Political Science.
[88] Gary King,et al. General purpose computer-assisted clustering and conceptualization , 2011, Proceedings of the National Academy of Sciences.
[89] Kenneth Benoit,et al. Coder Reliability and Misclassification in the Human Coding of Party Manifestos , 2012, Political Analysis.
[90] A. Spirling. U.S. Treaty Making with American Indians: Institutional Change and Relative Power, 1784–1911 , 2012 .
[91] Stuart Soroka,et al. Affective News: The Automated Coding of Sentiment in Political Texts , 2012 .
[92] Adam J. Berinsky,et al. Evaluating Online Labor Markets for Experimental Research: Amazon.com's Mechanical Turk , 2012, Political Analysis.
[93] Doron Shultziner. Genes and Politics: A New Explanation and Evaluation of Twin Study Results and Association Studies in Political Science , 2013, Political Analysis.
[94] Amber E. Boydstun,et al. RTextTools: A Supervised Learning Package for Text Classification , 2013, R J..
[95] Justin Grimmer,et al. Appropriators not Position Takers: The Distorting Effects of Electoral Incentives on Congressional Representation , 2013 .
[96] Ethan Bueno de Mesquita,et al. Delivering the Goods: Legislative Particularism in Different Electoral and Institutional Settings , 2006, The Journal of Politics.
[97] Jstor. The American political science review , 2022 .