Automated Text Analysis in Political Science

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Course Description: 
Automated text analysis has become very popular across the social sciences over the last few years. With the massive availability of text data on the web, political scientists increasingly recognize automated text analysis as viable approach to analyzing social and political behavior. This course – in which we use R – introduces students to a variety of its methods and tools, ranging from dictionary methods and other supervised and unsupervised methods to learn about, for example, content, ideology and sentiment in text. The course – which combines lectures and practical sessions – will be hands-on, with an emphasis on dealing with practical issues in each step of the research process (ranging from collecting and pre-processing text data to validating and visualizing output of the analysis). Students who have finished this course are well-positioned to apply automated text analysis methods in their own work, and will be able to critically evaluate existing work. 

Learning Outcomes: 
This course introduces students to various approaches of automated text analysis in social science research, emphasizing hands on analysis of real (political) texts. Students will learn how to extract useful information from text, evaluate the outcomes and write up the results of an analysis of a research question which applies automated text analysis. 



The class will be assessed through a written research note in which students will use automated text analysis to address a research question of their interest.


This course assumes familiarity with R.
Students who have not used R before will need to get themselves up to speed before the start of the course, for example by working their way through a free online R resource, like or the resources that are listed on Students working on their own laptops will need to have R and RStudio installed.

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