Leximancer is text-analytic software designed for analysing the conceptual content of natural language, e.g. qualitative interviews, news articles and reports and/or academic journal articles. Leximancer can assist in extracting meaning from voluminous and disparate bodies of text. The program analyses the text and displays the extracted information as a ‘concept map’ (e.g. see Figure 1). This displays the main concepts in the text data and depicts the relationships among concepts. In this way, Leximancer illustrates the conceptual structure of the text. Using the concept map, the user can develop propositions, hypotheses and/or perform directed searches of the text which can assist in their interpretations of meaning (Smith and Humphries 2006).

Figure 1: Discovery Map: JBBM papers 1993-2014




Figure 1 provides an example of scientometric analysis of the contents of the Journal of Business to Business Marketing (JBBM) from 1993 to 2014 (Young, Wilkinson and Smith 2015). The map summarizes the JBBM papers in terms of: the structure of the network of concepts that describe the corpus of papers, the way concepts group into themes and the content of each five volume set of text relative to the concept network. The size and number of the coloured balloons – themes – are set by the researcher in order to facilitate interpretation. Here the setting is such that there are only four themes, Relationship, Business, Marketing and Study. The themes are named for the most prominent concept in them. Leximancer also computes how strongly connected the themes are to each other (not shown or explained here). This is based on how frequently the concepts in different themes co-occur. Only the four more frequent concepts (i.e. those that have the most blocks of text coded as that concept) are shown for clarity. These are relationship, relationships, business and customer (hereafter concept names are shown in italics).

The discovery map (above) is a visual presentation of summarised data. It does not provide you with your findings. However, the map is useful as a guide for a manually-done categorical, discourse, conversation or metaphor based analysis. If instead the simple descriptions and comparison of the kind presented here highlights interesting insights, there are additional kinds of computer-aided analysis that can be further undertaken for a more in depth analysis.