  Networks, representing attitudinal survey data, expose 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 groups. Our goal is to present a method for revealing
polarisation and opinion-based in attitudinal surveys. This method can be
broken down into the following steps: data preparation, construction of
similarity-based networks, algorithmic identification of opinion-based groups,
and identification of important items for community structure. We assess the
method's performance and possible scope for 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 polarisation), whereas the
synthetic data sets mark 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 for identifying
polarisation can classify non-overlapping opinion-based groups even in the
absence of extreme opinions.
