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.
BibTeX
@inproceedings{sawhney-etal-2020-gpols,
title = "{GP}ol{S}: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion",
author = "Sawhney, Ramit and
Wadhwa, Arnav and
Agarwal, Shivam and
Shah, Rajiv Ratn",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.426/",
doi = "10.18653/v1/2020.coling-main.426",
pages = "4847--4859"
}