Application Case 5.5 (Continued) natural groupings of the articles (by putting them into separate clusters) and then to list the most descriptive terms that characterized those clusters. They used...

Application Case 5.5 (Continued)

natural groupings of the articles (by putting them into separate clusters) and then to list the most descriptive terms that characterized those clusters. They used singular value decomposition to reduce the dimensionality of the term-by-document matrix and then an expectation-maximization algorithm to create the clusters. They conducted several experiments to identify the optimal number of clusters, which turned out to be nine. After the construction of the nine clusters, they analyzed the content of those clusters from two perspectives: (1)  representation of the journal type (see Figure 5.8) and (2) representation of time. The idea was to explore the potential differences and/or commonalities among the three journals and potential changes in the emphasis on those clusters; that is, to answer questions such as “Are there clusters that represent different research themes specific to a single journal?” and “Is there a time-varying characterization of those clusters?” They discovered and discussed several interesting patterns using tabular and graphical representation of their findings (for further information see Delen and Crossland, 2008).

1. How can text mining be used to ease the insurmountable task of literature review?

2. What are the common outcomes of a text mining project on a specific collection of journal articles? Can you think of other potential outcomes not mentioned in this case?