Abstract
The literary theorist Kenneth Burke (1945) outlined a methodology for identifying the basic “grammar of motives” that operate within texts. His strategy was to identify the logical form that is used for attributing meaning to human situations. We imagine how a variant of Burke's method might be applied in the era of automated text analysis, and then we explore an implementation of that variant (using a combination of natural language process, semantic parsers and statistical topic models) in analyzing a corpus of eleven U.S. “National Security Strategy” documents that were produced between 1990 and 2010. This “automated process” for textual coding and analysis is shown to have much utility for analyzing these types of texts and to hold out the promise for being useful for other types of text corpora, as well—thereby opening up new possibilities for the scientific study of rhetoric.
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