  Network visualisation, drawn from attitudinal survey data, exposes the
structure of opinion-based groups. We make use of these network projections to
identify the groups reliably through community detection algorithms and to
examine social-identity-based polarisation.Our goal is to present a method for
revealing polarisation in attitudinal surveys. This method can be broken down
into the following steps: data preparation, construction of similarity-based
net-works, algorithmic identification of opinion-based groups, and
identification of item importance for community structure. We examine the
method's performance and possible scope through applying it to empirical data
and to a broad range of synthetic data sets. The empirical data application
points out possible conclusions (i.e. social-identity polarization), whereas
the synthetic data sets marks out the method's boundaries. Next to an
application example on political attitude survey, our results suggest that the
method works for various surveys but is also moderated by the efficacy of the
community detection algorithms. Concerning the identification of opinion-based
groups, we provide a solid method to rank the item's influence on group
formation and as a group identifier. We discuss how this network approach to
identifying polarization can classify non-overlapping opinion-based groups even
in the absence of extreme opinions.
