Abstract—It is the nature of qualitative research data to be collected in large amounts of interview narratives or conversations about human behaviour that cannot be analysed using conventional quantitative computer software. Such data need to be mined using qualitative digital software that can make sense of letters and words rather than numbers and equations. One such software that appears to be very efficient at mining qualitative data is called Leximancer and is the topic of this paper. Thanks to researchers at the University of New England and the University of Queensland in Australia, Leximancer can be used to mine large amounts of qualitative textual documents, extract information at super-electronic speeds and display the results visually in a graphic organiser of the contents generally called a Concept Map. The researcher is able to mine the data deeply to discover the meaning embedded in its digital structures through conceptual (thematic) analysis as well as relational (semantic) analysis in a manner which can be a great time saver for the researcher.

Index Terms—qualitative data mining, conceptual analysis, relational analysis, knowledge discovery

Cite: Charles Kivunja, "Qualitative Data Mining and Knowledge Discovery Using Leximancer Digital Software," Lecture Notes on Information Theory, Vol.1, No.1, pp. 53-55, March 2013. doi: 10.12720/lnit.1.1.53-55