# Multi-aspect Multilingual and Cross-lingual Parliamentary Speech Analysis

arXiv: https://arxiv.org/abs/2207.01054 · HTML: https://mlsanity.com/paper/2207.01054/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 91 references that are in the map.
- First posted on arXiv 3 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection](https://mlsanity.com/paper/2011.01612/) (2020)
- [To BAN or not to BAN: Bayesian Attention Networks for Reliable Hate Speech Detection](https://mlsanity.com/paper/2007.05304/) (2020)
- [Prediction Uncertainty Estimation for Hate Speech Classification](https://mlsanity.com/paper/1909.07158/) (2019)
- [Cross-National Measurement of Polarization in Political Discourse: Analyzing floor debate in the U.S. and the Japanese legislatures](https://mlsanity.com/paper/1711.02977/) (2017)
- [Gender Detection on Social Networks using Ensemble Deep Learning](https://mlsanity.com/paper/2004.06518/) (2020)

## Built on by (most cited first, 1 of 1)

- [Language Models Learn Metadata: Political Stance Detection Case Study](https://mlsanity.com/paper/2409.13756/) (2024)

## Abstract (arXiv)

Parliamentary and legislative debate transcripts provide informative insight into elected politicians' opinions, positions, and policy preferences. They are interesting for political and social sciences as well as linguistics and natural language processing (NLP) research. While existing research studied individual parliaments, we apply advanced NLP methods to a joint and comparative analysis of six national parliaments (Bulgarian, Czech, French, Slovene, Spanish, and United Kingdom) between 2017 and 2020. We analyze emotions and sentiment in the transcripts from the ParlaMint dataset collection and assess if the age, gender, and political orientation of speakers can be detected from their speeches. The results show some commonalities and many surprising differences among the analyzed countries.