Fightin' Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict

Peer-reviewed Journal Article

Monroe, Burt; Michael Colaresi & Kevin Quinn (2008) Fightin' Words: Lexical Feature Selection and Evaluation for Identifying the Content of Political Conflict, Political Analysis 16(4): 372–403.

​Entries in the burgeoning “text-as-data” movement are often accompanied by lists or visualizations of how word (or other lexical feature) usage differs across some pair or set of documents. These are intended either to establish some target semantic concept (like the content of partisan frames) to estimate word-specific measures that feed forward into another analysis (like locating parties in ideological space) or both. We discuss a variety of techniques for selecting words that capture partisan, or other, differences in political speech and for evaluating the relative importance of those words. We introduce and emphasize several new approaches based on Bayesian shrinkage and regularization. We illustrate the relative utility of these approaches with analyses of partisan, gender, and distributive speech in the U.S. Senate.

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Authors

Michael Colaresi

Michael Colaresi

Associate Professor, Michigan State University