Skip to main content
SpringerLink
Log in

Computergestützte Textanalysen

  • Living reference work entry
  • First Online:
Handbuch Methoden der Politikwissenschaft

Part of the book series: Springer Reference Sozialwissenschaften ((SRS))

Zusammenfassung

Texte stellen eine der bedeutsamsten Datenquellen in der Politikwissenschaft dar. Mit der computergestützten Textanalyse steht Politikwissenschaftlern ein immer mächtigeres Werkzeug zur Verfügung, um alte und neue Fragen aus verschiedenen Subdisziplinen der Politikwissenschaft zu beantworten. Diese Methoden werden konstant weiterentwickelt und verfeinert, während gleichzeitig immer mehr Textdaten auch elektronisch zur Verfügung stehen. Der vorliegende Beitrag beschreibt die Annahmen und grundsätzlichen Vorgehensweisen und bietet einen Überblick über die wichtigsten computergestützten Textanalyseverfahren von der wörterbuchbasierten Analyse bis hin zu Textskalierung, Textklassifikation und Topic Models. Zudem wird auf geeignete Software verwiesen. Anschließend werden vier politikwissenschaftliche Anwendungsbereiche vorgestellt und mehrere methodische Herausforderungen diskutiert.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Literatur

  • Atteveldt, Van, Jan Kleinnijenhuis Wouter, Nel Ruigrok, und Stefan Schlobach. 2008. Good news or bad news? Conducting sentiment analysis on Dutch text to distinguish between positive and negative relations. Journal of Information Technology & Politics 5(1): 73–94.

    Article  Google Scholar 

  • Benoit, Kenneth. 2017. quanteda: Quantitative analysis of textual data. https://doi.org/10.5281/zenodo.1004683, R package version 0.99.22. http://quanteda.io.

  • Benoit, Kenneth, und Michael Laver. 2008. Compared to What? A Comment on ‚A Robust Transformation Procedure for Interpreting Political Text‘ by Martin und Vanberg. Political Analysis 16(1): 101–111.

    Article  Google Scholar 

  • Bernauer, Julian, und Thomas Bräuninger. 2009. Intra-Party Preference Heterogeneity und Faction Membership in the 15th German Bundestag: A Computational Text Analysis of Parliamentary Speeches. German Politics 18(3): 385–402.

    Article  Google Scholar 

  • Blei, David, Andrew Ng, und Michael Jordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research 3:993–1022.

    Google Scholar 

  • Budge, Ian, und Paul Pennings. 2007. Do they work? Validating computerised word frequency estimates against policy series. Electoral Studies 26(1): 121–129.

    Article  Google Scholar 

  • Bunea, Adriana, und Raimondas Ibenskas. 2015. Quantitative text analysis und the study of EU lobbying und interest groups. European Union Politics 16(3): 429–455.

    Article  Google Scholar 

  • Collingwood, Loren, und John Wilkerson. 2012. Tradeoffs in Accuracy und Efficiency in Supervised Learning Methods. Journal of Information Technology & Politics 9(3): 298–318.

    Article  Google Scholar 

  • Debus, Marc. 2009. Pre-Electoral Commitments und Government Formation. Public Choice 138(1–2): 45–64.

    Article  Google Scholar 

  • Denny, Matthew J., und Arthur Spirling. 2018. Text preprocessing for unsupervised learning: Why it matters, when it misleads, and what to do about it. Political Analysis.

    Article  Google Scholar 

  • DiMaggio, Paul. 2015. Adapting computational text analysis to social science (and vice versa). Big Data & Society 1–5. First published December 1, 2015.

    Google Scholar 

  • Egami, Naoki, Christian J. Fong, Justin Grimmer, Margaret E. Roberts, und Brandon M. Stewart. 2017. How to make causal inferences using texts. Working Paper.

    Google Scholar 

  • Flesch, Rudolf. 1948. A new readability yardstick. Journal of Applied Psychology 32(3): 221–233.

    Article  Google Scholar 

  • Gianetti, Daniela, und Michael Laver. 2005. Policy Positions und Jobs in the Government. European Journal of Political Research 44:91–120.

    Article  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.

    Article  Google Scholar 

  • Grimmer, Justin, und Brandon Stewart. 2013. Text as Data: The Promise und Pitfalls of Automatic Content Analysis Methods for Political Texts. Political Analysis 21(3): 267–297.

    Article  Google Scholar 

  • Hakhverdian, Armèn. 2009. Capturing government policy on the left-right scale: Evidence from the United Kingdom, 1956–2006. Political Studies 57:720–745.

    Article  Google Scholar 

  • Haselmayer, Martin, und Marcelo Jenny. 2017. Sentiment analysis of political communication: Combining a dictionary approach with crowdcoding. Quality and Quantity 51:2623–2646.

    Article  Google Scholar 

  • Herzog, Alexander, und Kenneth Benoit. 2015. The most unkindest cuts: Speaker selection und expressed government dissent during economic crisis. Journal of Politics 77(4): 1157–1175.

    Article  Google Scholar 

  • Klemmensen, Robert, Sara B. Hobolt, und Martin E. Hansen. 2007. Estimating policy positions using political texts: An evaluation of the wordscores approach. Electoral Studies 26:746–755.

    Article  Google Scholar 

  • Lauderdale, Benjamin E., und Alexander Herzog. 2016. Measuring political positions from legislative speech. Political Analysis 24(3): 374–394.

    Article  Google Scholar 

  • Laver, Michael, und John Garry. 2000. Estimating policy positions from political texts. American Journal of Political Science 44(3): 619–634.

    Article  Google Scholar 

  • Laver, Michael, Kenneth Benoit, und John Garry. 2003. Extracting policy positions from political texts using words as data. American Political Science Review 97(2): 311–332.

    Article  Google Scholar 

  • Lo, James, Sven-Oliver Proksch, und Jonathan B. Slapin. 2016. Ideological clarity in multiparty competition: A new measure und test using election manifestos. British Journal of Political Science 46(3): 591–610.

    Article  Google Scholar 

  • Lowe, Will. 2008. Understanding wordscores. Political Analysis 16(4): 356–371.

    Article  Google Scholar 

  • Lowe, Will. 2017. Putting it all on the line: Some unified theory for text scaling. Working Paper.

    Google Scholar 

  • Lucas, Christopher, Richard Nielsen, Margeret Roberts, Brandon Stewart, Alex Storer, und Dustin Tingley. 2015. Computer-assisted text analysis for comparative politics. Political Analysis 23(2): 254–277.

    Article  Google Scholar 

  • Martin, Lanny W., und Georg Vanberg. 2008. A robust transformation procedure for interpreting political text. Political Analysis 16(1): 93–100.

    Article  Google Scholar 

  • Munzert, Simon, Christian Rubba, Peter Meißner, und Dominic Nyhuis. 2014. Automated data collection with R – A practical guide to web scraping and text mining. Chichester: Wiley.

    Google Scholar 

  • Proksch, Sven-Oliver, und Jonathan B. Slapin. 2006. Institutions und Coalition Formation: The German election of 2005. West European Politics 29(3): 540–559.

    Article  Google Scholar 

  • Proksch, Sven-Oliver, und Jonathan B. Slapin. 2010. Position taking in European Parliament speeches. British Journal of Political Science 40(3): 587–611.

    Article  Google Scholar 

  • Proksch, Sven-Oliver, und Jonathan B. Slapin. 2012. Institutional foundations of legislative speech. American Journal of Political Science 56(3): 520–537.

    Article  Google Scholar 

  • Proksch, Sven-Oliver, und Jonathan B. Slapin. 2015. The Politics of Parliamentary Debate: Parties, Rebels, und Representation. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Quinn, Kevin M., Burt L. Monroe, Michael Colaresi, Michael H. Crespin, und Dragomir Radev. 2010. How to analyze political attention with minimal assumptions und costs. American Journal of Political Science 54(1): 209–228.

    Article  Google Scholar 

  • Roberts, Margeret E., Brandon Stewart, Dustin Tingley, Christopher Lucas, Jetson Leder-Luis, Shana Gadarian, Bethany Albertson, und David Rand. 2014. Structural topic models for open-ended survey responses. American Journal of Political Science 58(4): 1064–1082.

    Article  Google Scholar 

  • Slapin, Jonathan B., und Sven-Oliver Proksch. 2008. A scaling model for estimating time-series party positions from texts. American Journal of Political Science 52(3): 705–722.

    Article  Google Scholar 

  • Slapin, Jonathan B., und Sven-Oliver Proksch. 2014. Words as data: Content analysis in legislative studies. In The Oxford handbook of legislative studies, Hrsg. S. Martin, T. Saalfeld und K. Strom. Oxford: Oxford University Press.

    Google Scholar 

  • Soroka, Stuart. 2006. Good News und Bad News: Asymmetric Responses to Economic Information. Journal of Politics 68(2): 372–385.

    Article  Google Scholar 

  • Soroka, Stuart. 2012. The Gatekeeping Function: Distributions of Information in Media und the Real World. Journal of Politics 74(2): 514–528.

    Article  Google Scholar 

  • Soroka, Stuart. 2014. Negativity in Democratic Politics: Causes und Consequences. Cambridge Studies in Public Opinion und Political Psychology. New York: Cambridge University Press.

    Google Scholar 

  • Soroka, Stuart, Lori Young, und Meital Balmas. 2015a. Bad news or mad news? Sentiment scoring of negativity, fear, and anger in news content. The Annals of the American Academy of Political and Social Science 659(1): 108–121.

    Article  Google Scholar 

  • Soroka, Stuart, Dominik A. Stecula, und Christopher Wlezien. 2015b. It’s (Change in) the (future) economy, stupid: Economic indicators, the media, and public opinion. American Journal of Political Science 59(2): 457–474.

    Article  Google Scholar 

  • Spirling, Arthur. 2016. Democratization of linguistic complexity: The effect of Franchise extension on parliamentary discourse, 1832–1915. Journal of Politics 78(1): 120–136.

    Article  Google Scholar 

  • Wilkerson, John, David Smith, und Nicholas Stramp. 2015. Tracing the flow of policy ideas in legislatures: A text reuse approach. American Journal of Political Science 59:943–956.

    Article  Google Scholar 

  • Young, Lori, und Stuart Soroka. 2012. Affective news: The automated coding of sentiment in political texts. Political Communication 29:205–231.

    Article  Google Scholar 

  • Zipf, George Kingsley. 1935. The psycho-biology of language. An introduction to dynamic philology. Boston: Mifflin.

    Google Scholar 

  • Zipf, George Kingsley. 1949. Human behavior und the principle of least effort. An introduction to human ecology. Cambridge, MA: Addison-Wesley.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Cologne Center for Comparative Politics, Wirtschafts- und Sozialwissenschaftliche Fakultät, Universität zu Köln, Köln, Deutschland

    Sven-Oliver Proksch

Authors
  1. Sven-Oliver Proksch
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sven-Oliver Proksch .

Editor information

Editors and Affiliations

  1. Methodenzentrum Sozialwissenschaft, Goethe-Universität Frankfurt Methodenzentrum Sozialwissenschaft, Frankfurt, Hessen, Germany

    Claudius Wagemann

  2. Institute for Political Sciences, University of Duisburg-Essen Institute for Political Sciences, Duisburg, Germany

    Achim Goerres

  3. Institut für Politikwissenschaft, Goethe-Universität Frankfurt am Main Institut für Politikwissenschaft, Frankfurt, Hessen, Germany

    Markus Siewert

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Proksch, SO. (2018). Computergestützte Textanalysen. In: Wagemann, C., Goerres, A., Siewert, M. (eds) Handbuch Methoden der Politikwissenschaft. Springer Reference Sozialwissenschaften. Springer VS, Wiesbaden. https://doi.org/10.1007/978-3-658-16937-4_38-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-658-16937-4_38-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer VS, Wiesbaden

  • Print ISBN: 978-3-658-16937-4

  • Online ISBN: 978-3-658-16937-4

  • eBook Packages: Springer Referenz Sozialwissenschaften und Recht

Publish with us

Policies and ethics

Search

Navigation