  Text mining approaches are being used increasingly for business analytics. In
particular, such approaches are now central to understanding users' feedback
regarding systems delivered via online application distribution platforms such
as Google Play. In such settings, large volumes of reviews of potentially
numerous apps and systems means that it is infeasible to use manual mechanisms
to extract insights and knowledge that could inform product improvement. In
this context of identifying software system improvement options, text mining
techniques are used to reveal the features that are mentioned most often as
being in need of correction (e.g., GPS), and topics that are associated with
features perceived as being defective (e.g., inaccuracy of GPS). Other
approaches may supplement such techniques to provide further insights for
online communities and solution providers. In this work we augment text mining
approaches with social network analysis to demonstrate the utility of using
multiple techniques. Our outcomes suggest that text mining approaches may
indeed be supplemented with other methods to deliver a broader range of
insights.
