Replication data for: Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Textsdoi:10.7910/DVN/FQBHP8Harvard Dataverse2012-07-232Grimmer, Justin; Stewart, Brandon, 2012, "Replication data for: Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts", https://doi.org/10.7910/DVN/FQBHP8, Harvard Dataverse, V2Replication data for: Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Textsdoi:10.7910/DVN/FQBHP8Grimmer, JustinStewart, BrandonPolitical Analysis2012-07-09Harvard DataverseIQSS Dataverse NetworkBrandon Stewart2012-07-09Volume 21, Issue 3statistics, text analysis, content analysisReplication Materials (Data and Code) for 'Text as Data' 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.United States<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a>Justin Grimmer and Brandon Stewart. 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts." Political Analysis (Summer 2013) 21 (3): 267-297. <a href= "http://stanford.edu/~jgrimmer/tad2.pdf" target= "_new">article available here</a>10.1093/pan/mps028Justin Grimmer and Brandon Stewart. 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts." Political Analysis (Summer 2013) 21 (3): 267-297. <a href= "http://stanford.edu/~jgrimmer/tad2.pdf" target= "_new">article available here</a>ReplicationFile.zipReplication Code and Data in R Statistical Computing Languageapplication/octet-stream