Topic-Based Agreement and Disagreement in US Electoral Manifestos

Stefano Menini, Federico Nanni, Simone Paolo Ponzetto, Sara Tonelli


Abstract
We present a topic-based analysis of agreement and disagreement in political manifestos, which relies on a new method for topic detection based on key concept clustering. Our approach outperforms both standard techniques like LDA and a state-of-the-art graph-based method, and provides promising initial results for this new task in computational social science.
Anthology ID:
D17-1318
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2938–2944
Language:
URL:
https://www.aclweb.org/anthology/D17-1318
DOI:
10.18653/v1/D17-1318
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1318.pdf
Video:
 https://vimeo.com/238236263