GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion

Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, Rajiv Ratn Shah


Abstract
Parliamentary debates present a valuable language resource for analyzing comprehensive options in electing representatives under a functional, free society. However, the esoteric nature of political speech coupled with non-linguistic aspects such as political cohesion between party members presents a complex and underexplored task of contextual parliamentary debate analysis. We introduce GPolS, a neural model for political speech sentiment analysis jointly exploiting both semantic language representations and relations between debate transcripts, motions, and political party members. Through experiments on real-world English data and by visualizing attention, we provide a use case of GPolS as a tool for political speech analysis and polarity prediction.
Anthology ID:
2020.coling-main.426
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Editors:
Donia Scott, Nuria Bel, Chengqing Zong
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4847–4859
Language:
URL:
https://aclanthology.org/2020.coling-main.426
DOI:
10.18653/v1/2020.coling-main.426
Bibkey:
Cite (ACL):
Ramit Sawhney, Arnav Wadhwa, Shivam Agarwal, and Rajiv Ratn Shah. 2020. GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion. In Proceedings of the 28th International Conference on Computational Linguistics, pages 4847–4859, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Cite (Informal):
GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion (Sawhney et al., COLING 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.426.pdf