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Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts

  • Justin Grimmer (a1) and Brandon M. Stewart (a2)


Politics and political conflict often occur in the written and spoken word. Scholars have long recognized this, but the massive costs of analyzing even moderately sized collections of texts have hindered their use in political science research. Here lies the promise of automated text analysis: it substantially reduces the costs of analyzing large collections of text. We provide a guide to this exciting new area of research and show how, in many instances, the methods have already obtained part of their promise. But there are pitfalls to using automated methods—they are no substitute for careful thought and close reading and require extensive and problem-specific validation. We survey a wide range of new methods, provide guidance on how to validate the output of the models, and clarify misconceptions and errors in the literature. To conclude, we argue that for automated text methods to become a standard tool for political scientists, methodologists must contribute new methods and new methods of validation.

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Authors' note: For helpful comments and discussions, we thank participants in Stanford University's Text as Data class, Mike Alvarez, Dan Hopkins, Gary King, Kevin Quinn, Molly Roberts, Mike Tomz, Hanna Wallach, Yuri Zhurkov, and Frances Zlotnick. Replication data are available on the Political Analysis Dataverse at Supplementary materials for this article are available on the Political Analysis Web site.



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Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts

  • Justin Grimmer (a1) and Brandon M. Stewart (a2)


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