Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-24T08:39:44.006Z Has data issue: false hasContentIssue false
  • English
  • Français

Introduction to the Special Issue: The Statistical Analysis of Political Text

Published online by Cambridge University Press:  04 January 2017

Burt L. Monroe  and
Philip A. Schrodt
Burt L. Monroe*
Affiliation:
Department of Political Science, Pennsylvania State University, University Park, PA 16802
Philip A. Schrodt
Affiliation:
Department of Political Science, University of Kansas, Lawrence, KS 66045
*
e-mail: [email protected] (corresponding author)
Get access Rights & Permissions [Opens in a new window]

Extract

Text is arguably the most pervasive—and certainly the most persistent—artifact of political behavior. Extensive collections of texts with clearly recognizable political—as distinct from religious—content go back as far as 2500 BCE in the case of Mesopotamia and 1300 BCE for China, and 2400-year-old political discussions dating back to the likes of Plato, Aristotle, and Thucydides are common fare even in the introductory study of political thought. Political tracts were among the earliest productions following the introduction of low-cost printing in Europe—fueling more than a few revolutions and social upheavals—and continuous printed records of legislative debates, such as the British parliament's Hansard and precursors tracing to 1802, cover centuries of political discussion.

Type
Special Issue: The Statistical Analysis of Political Text
Information
Political Analysis , Volume 16 , Issue 4: Special Issue: The Statistical Analysis of Political Text , Autumn 2008 , pp. 351 - 355
Copyright
Copyright © The Author 2009. Published by Oxford University Press on behalf of the Society for Political Methodology 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Advanced Research Projects Agency (ARPA). 1993. Proceedings of the Fifth Message Understanding Conference (MUC-5). Los Altos, CA: Morgan Kaufmann.Google Scholar
Alexa, Melina, and Zuell, Cornelia. 1999. A review of software for text analysis. Mannheim, Germany: Zentrum für Umfragen, Methoden und Analysen.Google Scholar
Beigman Klebanov, Beata. 2006. “Measuring semantic relatedness using people and WordNet.” Proceedings of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers, pp 13–7. New York, NY: Association for Computational Linguistics.Google Scholar
Benoit, Kenneth, and Laver, M. 2003. Estimating Irish party positions using computer wordscoring: The 2002 elections. Irish Political Studies 17: 97–107.Google Scholar
Bond, Doug, Craig Jenkins, J., Taylor, Charles L., and Schock, Kurt. 1997. Mapping mass political conflict and civil society: The automated development of event data. Journal of Conflict Resolution 41: 553–79.Google Scholar
Cardie, Claire, and Wilkerson, John. 2008. Special issue: Text annotation for political science. Journal of Information Technology and Politics 5: 1–6.Google Scholar
Goodrich, Melanie. 2008. “A coding methodology for open-ended survey questions. Poster presented to the Political Methodology Society, Ann Arbor, MI, July 10–12, 2008.Google Scholar
Grimmer, Justin. 2008. “Expanding the study of political representation: Measuring and explaining representatives’ expressed agenda. Poster presented to the Political Methodology Society, Ann Arbor, MI, July 10–12, 2008.Google Scholar
Holsti, Ole R. 1969. Content analysis for the social sciences and humanities. Reading, MA: Addison-Wesley.Google Scholar
Janowitz, Morris. 1968. Harold D. Lasswell's contribution to content analysis. Public Opinion Quarterly 32: 646–53.Google Scholar
Jurafsky, Daniel, and Martin, James H. 2008. Speech and language processing: An introduction to natural language processing, speech recognition, and computational linguistics. 2nd ed. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
Krippendorff, Klaus. 2004. Content analysis: An introduction to its methodology. 2nd ed. Thousand Oaks, CA: Sage.Google Scholar
Laver, Michael, Benoit, Kenneth, and Garry, John. 2003. Extracting policy positions from political texts using words as data. American Political Science Review 97: 311–31.Google Scholar
Manning, Christopher D., and Schütze, Hinrich. 2002. Foundations of statistical natural language processing. 5th ed. Cambridge, MA: MIT Press.Google Scholar
Miller, George. 1990. “WordNet: An on-line lexical database.” International Journal of Lexicography 3: 235–312.Google Scholar
Monroe, Burt L., and Maeda, Ko. 2004. “Talk's cheap: Text-based ideal point estimation. Paper presented to the Political Methodology Society, Palo Alto, July 29–31, 2004.Google Scholar
Neuendorf, Kimberly A. 2001. The content analysis guidebook. New York: Sage.Google Scholar
Pemstein, Daniel. 2008. “Predicting strategic roll calls with legislative text. Poster presented to the Political Methodology Society, Ann Arbor, MI, July 10–12, 2008.Google Scholar
Popping, Roel. 2000. Computer-assisted text analysis. New York: Sage.Google Scholar
Quinn, Kevin M., Monroe, Burt L., Colaresi, Michael P., Crespin, Michael H., and Radev, Dragomir. 2006. “An automated method of topic-coding legislative speech over time with application to the 105th-108th United States Senate. Paper presented to the Political Methodology Society, Davis, CA, July 20–22, 2006.Google Scholar
Roberts, Carl W. 1997. Text analysis for the social sciences: Methods for drawing inferences from texts and transcripts. Mahwah, NJ: Lawrence Erlbaum.Google Scholar
Sagarzazu, Inaki. 2008. “Look who's talking: Analyzing the Dynamics of Political Discourse. Poster presented to the Political Methodology Society, Ann Arbor, MI, July 10–12, 2008.Google Scholar
Salton, Gerard. 1989. Automatic Text Processing. Reading, MA: Addison-Wesley.Google Scholar
Schrenk, Michael. 2007. Webbots, spiders, and screen scrapers. San Francisco, CA: No Starch Press.Google Scholar
Schrodt, Philip. 2003. “Analyzing text using statistical methods. Workshop presented to the Political Methodology Society, Minneapolis, MN, July 17–19, 2003.Google Scholar
Simon, Adam F., and Xenos, Michael. 2004. “Dimensional reduction of word-frequency data as a substitute for intersubjective content analysis.” Political Analysis 12: 63–75.Google Scholar
Smith, Peter D. 1990. An introduction to text processing. Cambridge, MA: MIT Press.Google Scholar
Stone, Philip J., Dunphry, Dexter C., Smith, Marshall S., and Ogilvie, Daniel M. 1966. The general inquirer: A computer approach to content analysis. Cambridge, MA: MIT Press.Google Scholar
Weber, Robert P. 1990. Basic content analysis. 2nd ed. Newbury Park, CA: Sage Publications.Google Scholar