Mining and exploring customer feedback using language models and treemaps

Abstract

We propose an approach for exploring large corpora of textual customer feedback in a guided fashion, bringing order to massive amounts of unstructured information. The prototypical system we implemented allows an analyst to assess labelled clusters in a graphical fashion, based on treemap visualization techniques, and perform drill-down operations in order to investigate the topic of interest in a more fine-grained manner. Labels are chosen by simple but effective term weighting schemes and lay the foundations for assigning feedback postings to clusters. In order to allow for drilldown operations leading to new clusters of refined information, we present an approach that contrasts foreground and background models of feedback texts when stepping into the currently selected set of feedback messages. The prototype we present is already in tentative use at various Siemens business units and has been warmly embraced by marketing analysts. © 2012 Springer-Verlag Berlin Heidelberg.