Searching for Significance in Unstructured Data: Text Mining with Leximancer

Abstract

Scholars in many knowledge domains rely on sophisticated information technologies to search for and retrieve records and publications pertinent to their research interests. But what is a scholar to do when a search identifies hundreds of documents, any of which might be vital or irrelevant to his or her work? The problem is further complicated by the unstructured nature of most documents which, unlike databases, are difficult to search systematically. More and more, scholars are turning to content analysis technologies to achieve what they do not have time to do themselves — characterize the global features of a large corpus of work and identify relationships between particular concepts, themes, and methodologies. This article begins with a brief discussion of content analysis as a research methodology. The utility of this methodology is then illustrated using Leximancer, an automated content analysis and concept mapping technology. Against this conceptual and technical background, the value of content analysis is discussed in a variety of academic contexts, including individual and collaborative scholarship and academic advising.