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)
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.coling-main.426.pdf