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
Editors:
Martha Palmer, Rebecca Hwa, Sebastian Riedel
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2938–2944
Language:
URL:
https://aclanthology.org/D17-1318
DOI:
10.18653/v1/D17-1318
Bibkey:
Cite (ACL):
Stefano Menini, Federico Nanni, Simone Paolo Ponzetto, and Sara Tonelli. 2017. Topic-Based Agreement and Disagreement in US Electoral Manifestos. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2938–2944, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Topic-Based Agreement and Disagreement in US Electoral Manifestos (Menini et al., EMNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/D17-1318.pdf
Video:
 https://vimeo.com/238236263