Map analysis, a type of network analysis, is a technique for systematically extracting, representing, and comparing the networks of ties between concepts in a set of texts. The network of ties is the text’s “map.” Managing issues of self-presentation is a central goal of many different types of texts, and in this paper we present map analysis results that capture the self-presentation strategies authors use in a specific set of texts. The texts we study are a set of applications on behalf of entrepreneurs for an “Entrepreneur of the Year” award. Our research focuses on both interpreting self-presentation strategies from these networks, and comparing the networks created by different coding and data reduction decisions. We use an automated text analysis program (AutoMap©) to extract the concepts in the text, link them into statements based on their proximity in the text, and then into networks of statements within the entire text. The author’s specific strategic intent in the text is reflected in different statements formed from the concepts in the text and the arrangement of those statements. The structures of texts’ concept networks leads us to extract four general self-presentation strategies: the prepared entrepreneur, the driven entrepreneur, the creative niche entrepreneur, and the humble entrepreneur (a single entrepreneur may employ multiple strategies).