Analysis of Parliamentary Debate Transcripts Using Community-Based Graphical Approaches (Student Abstract)
Abstract
Gauging political sentiments and analyzing stances of elected representatives pose an important challenge today, and one with wide-ranging ramifications. Community-based analysis of parliamentary debate sentiments could pave a way for better insights into the political happenings of a nation and help in keeping the voters informed. Such analysis could be given another dimension by studying the underlying connections and networks in such data. We present a sentiment classification method for UK Parliament debate transcripts, which is a combination of a graphical method based on DeepWalk embeddings and text-based analytical methods. We also present proof for our hypothesis that parliamentarians with similar voting patterns tend to deliver similar speeches. We also provide some further avenues and future work towards the end.
Cite
Text
Bhavan et al. "Analysis of Parliamentary Debate Transcripts Using Community-Based Graphical Approaches (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7148Markdown
[Bhavan et al. "Analysis of Parliamentary Debate Transcripts Using Community-Based Graphical Approaches (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/bhavan2020aaai-analysis/) doi:10.1609/AAAI.V34I10.7148BibTeX
@inproceedings{bhavan2020aaai-analysis,
title = {{Analysis of Parliamentary Debate Transcripts Using Community-Based Graphical Approaches (Student Abstract)}},
author = {Bhavan, Anjali and Sharma, Mohit and Sawhney, Ramit and Shah, Rajiv Ratn},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2020},
pages = {13753-13754},
doi = {10.1609/AAAI.V34I10.7148},
url = {https://mlanthology.org/aaai/2020/bhavan2020aaai-analysis/}
}