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Published online by Cambridge University Press: 04 January 2017
Recent advances in research tools for the systematic analysis of textual data are enabling exciting new research throughout the social sciences. For comparative politics, scholars who are often interested in non-English and possibly multilingual textual datasets, these advances may be difficult to access. This article discusses practical issues that arise in the processing, management, translation, and analysis of textual data with a particular focus on how procedures differ across languages. These procedures are combined in two applied examples of automated text analysis using the recently introduced Structural Topic Model. We also show how the model can be used to analyze data that have been translated into a single language via machine translation tools. All the methods we describe here are implemented in open-source software packages available from the authors.
Authors' note: Our thanks to Sam Brotherton and Jetson Leder-Luis for research assistance and Amy Catilinac for discussion about text analyses in comparative politics. We also thank Christopher Blattman, Dan Corstange, Macartan Humphreys, Amaney Jamal, Gary King, Helen Milner, Tamar Mitts, Brendan O’Connor, Arthur Spirling, and the Columbia University Comparative Politics Workshop for comments. Our software discussed in this article is open source and available.