Election manifestos document the intentions, motives, and views of political parties. They are often used for analysing a party's fine-grained position on a particular issue, as well as for coarse-grained positioning of a party on the left--right spectrum. In this paper we propose a two-stage model for automatically performing both levels of analysis over manifestos. In the first step we employ a hierarchical multi-task structured deep model to predict fine- and coarse-grained positions, and in the second step we perform post-hoc calibration of coarse-grained positions using probabilistic soft logic. We empirically show that the proposed model outperforms state-of-art approaches at both granularities using manifestos from twelve countries, written in ten different languages.
@article{arxiv.1805.02823,
title = {Hierarchical Structured Model for Fine-to-coarse Manifesto Text Analysis},
author = {Shivashankar Subramanian and Trevor Cohn and Timothy Baldwin},
journal= {arXiv preprint arXiv:1805.02823},
year = {2018}
}