Additive manifesto decomposition: A policy domain aware method for
  understanding party positioning
Tanise Ceron Dmitry Nikolaev Sebastian Padó
Automatic extraction of party (dis)similarities from texts such as party
election manifestos or parliamentary speeches plays an increasing role in
computational political science. However, existing approaches are fundamentally
limited to targeting only global party (dis)-similarity: they condense the
relationship between a pair of parties into a single figure, their similarity.
In aggregating over all policy domains (e.g., health or foreign policy), they
do not provide any qualitative insights into which domains parties agree or
disagree on. This paper proposes a workflow for estimating policy domain aware
party similarity that overcomes this limitation. The workflow covers (a)
definition of suitable policy domains; (b) automatic labeling of domains, if no
manual labels are available; (c) computation of domain-level similarities and
aggregation at a global level; (d) extraction of interpretable party positions
on major policy axes via multidimensional scaling. We evaluate our workflow on
manifestos from the German federal elections. We find that our method (a)
yields high correlation when predicting party similarity at a global level and
(b) provides accurate party-specific positions, even with automatically
labelled policy domains.