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
- 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)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/D17-1318.pdf