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Monday 6 August - Friday 10 August
09:00-10:30 / 11:00-12:30
This applied course will provide you with advanced techniques of quantitative text analysis methods that allow you to systematically extract information from political texts. The course will start with a quick review of Dictionary based approaches to Scaling, Topic coding, and Sentiment Analysis, but quickly moves to more sophisticated techniques for the quantitative analysis of texts. The course will point out the differences between supervised and unsupervised mechanisms of text analysis and will –to the extent to which the setting allows- delve somewhat on the mathematical and statistical background of some of these techniques. The final class of this course will look into how to incorporate the outcome of applying QTA to a Corpus for interpretation and to be used in further statistical analysis. The course will combine theoretical sessions with practical exercises to allow participants to immediately apply the presented techniques. Previous experience with text analysis –in the form of an Introductory Text Analysis class- is required.
Tasks for ECTS Credits
To receive 2 ECTS, you will have done the readings and taken part actively in the course.
For an additional credit you will need to complete the daily assignments (easy to moderate demand), due in by the following day's class.
For an additional two credits, a take home exam will be set. The deadline for returning the exam will be set during the class.
Iñaki Sagarzazu is an Assistant Professor in Political Science at Texas Tech University. Prior to joining Texas Tech he was a Lecturer in Comparative Politics at the University of Glasgow and a postdoctoral researcher at Nuffield College, Oxford. He earned his PhD at the University of Houston.
Iñaki's research focuses on comparative politics, with a special focus on statistical content analysis with applications to political communication and institutions.
He has taught courses on Text Analysis at the IPSA Summer Schools in São Paulo and Singapore, and at the ECPR Winter School.
@YVPolisThis applied course will provide you with advanced techniques of quantitative text analysis methods that allow you to systematically extract information from political texts. In the first day the course will quickly review introductory techniques such as Dictionary based approaches to Scaling, Topic coding, and Sentiment Analysis, as well as necessary concepts for QTA. After the initial review the course quickly moves to more sophisticated techniques for the quantitative analysis of texts. The course will point out the differences between supervised and unsupervised mechanisms of text analysis and will –to the extent to which the setting allows- delve somewhat on the mathematical and statistical background of some of these techniques. Specifically we will study one unsupervised scaling technique and two topic coding techniques (one supervised and one unsupervised). These three techniques are state-of-the-art in the literature on text analysis for the social sciences. The final class of this course will look into how to incorporate the outcome of applying QTA to a Corpus for interpretation and to be used in further statistical analysis. This class rounds up the course as it allows the usability of the previously generated measures. The course will combine theoretical sessions with practical exercises to allow participants to immediately apply the presented techniques. Previous experience with text analysis –in the form of an Introductory Text Analysis class- is required.
The following skills are required to be able to follow the course:
R
None.
Burt L. Monroe and Philip A. Schrodt. 2008 Introduction to the special issue: The statistical analysis of political text. Political Analysis, 16(4):351-355,
Grimmer, Justin. 2010. A bayesian hierarchical topic model for political texts: Measuring expressed agendas in senate press releases. Political Analysis, 18(1):1-35
Grimmer, Justin and Brandon Stewart. 2013. Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political Analysis
Hjorth, Frederik, Robert Klemmensen, Sara Hobolt, Martin Ejnar Hansen, and Peter Kurrild-Klitgaard. 2015. Computers, coders, and voters: Comparing automated methods for estimating party positions. Research and Politics, April-June:1-9,
Hu, M. and B. Liu. 2004. Mining and summarizing customer reviews. In proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, page 168-177,
Klüver, Heike. 2009. “Measuring interest group influence using quantitative text analysis.”
European Union Politics 10(4):535–549.
Laver, Michael and John Garry. 2000. “Estimating Policy Positions from Political Texts.”
American Journal of Political Science 44(3):619–634.
Laver, Michael, Kenneth Benoit and John Garry. 2003. “Extracting policy positions from political texts using word as data.” American Political Science Review 97(2):311–331.
Sergi Pardos-Prado and Iñaki Sagarzazu. 2015The political conditioning of subjective economic evaluations: The role of party discourse. British Journal of Political Science
Slapin, Jonathan and Sven-Oliver Proksch. 2008. A scaling model for estimating time series policy positions from texts. American Journal of Political Science, 52(8): 705-722,
Sagarzazu, Iñaki and Heike Kluver. 2015. Coalition governments and party competition Political communication strategies of coalition parties. Political Science Research & Methods, 2015
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