Scholars increasingly theorize about the power of communication to organize and structure social collectives. However, two factors threaten to impede research on these theories: limitations in the scope and range of existing methods for studying complex systems of communication and the large volume of communication produced by even small collectives. Centering resonance analysis (CRA) is a new text analysis method that has broad scope and range and can be applied to large quantities of written text and transcribed conversation. It identifies discursively important words and represents these as a network, then uses structural properties of the network to index word importance. CRA networks can be directly visualized and can be scored for resonance with other networks to support a number of spatial analysis methods. Following a critique of existing methodologies, this paper describes the theoretical basis and operational details of CRA, describes its advantages relative to other techniques, demonstrates its face validity and representational validity, and demonstrates its utility in modeling organizational knowledge. The conclusion argues for its applicability in several organizational research contexts before describing its potential for use in a broader range of applications, including media content analysis, conversation analysis, computer simulations, and models of communication systems.