Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts by Justin Grimmer and Brandon M. Stewart.
Abstract:
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.
As a former political science major, I had to stop to read this article.
A wide ranging survey of an “exciting new area of research” but I remember content/text analysis as an undergraduate, North of forty years ago now.
True, some of the measures are new, along with better visualization techniques.
On the other hand, many of the problems of textual analysis now were the problems in textual analysis then (and before).
Highly recommended as a survey of current techniques.
A history of the “problems” of textual analysis and their resistance to various techniques will have to await another day.