The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings

Core Concepts

Training a multilingual model on parliamentary data significantly improves sentiment identification in political discourse.

Abstract

The paper introduces the ParlaSent dataset, manually annotated for sentiment in 7 languages.
A new transformer language model pre-trained on parliamentary data is presented.
Experiments show improved performance with additional pre-training on parliamentary data.
The dataset includes annotated sentences from European parliaments.
Sentences are focused on for sentiment analysis, contrary to traditional approaches.
The annotation schema allows for nuanced sentiment labeling.
Data sampling and annotation processes are detailed and rigorous.
Results show the effectiveness of the XLM-R-parla model across different languages and parliaments.

Stats

The paper presents a new training dataset of sentences in 7 languages, manually annotated for sentiment.
The additional pre-training on parliamentary data can significantly improve the model performance.
Large multilingual models perform better than smaller ones.

Quotes

"Emotions and sentiment play an essential role in political arenas."
"Understanding these mechanisms is highly important."

Key Insights Distilled From

The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings

by Mich... at arxiv.org 03-21-2024

https://arxiv.org/pdf/2309.09783.pdf

Deeper Inquiries

How can this dataset be utilized beyond sentiment analysis?

The ParlaSent Multilingual Training Dataset can be utilized beyond sentiment analysis in various ways. One potential application is in machine translation and natural language processing tasks. By training models on this dataset, researchers can improve the accuracy of translation systems for parliamentary proceedings across multiple languages. Additionally, the dataset could be used for speech recognition applications to transcribe parliamentary debates accurately. Furthermore, researchers could explore using this dataset for studying linguistic patterns and variations across different parliaments, contributing to sociolinguistic research.

What potential biases could arise from using a multilingual approach?

One potential bias that could arise from using a multilingual approach is related to language-specific nuances and cultural differences present in parliamentary proceedings. Different languages may have unique expressions, idioms, or rhetorical devices that might not translate directly into other languages. This discrepancy could lead to inaccuracies or misinterpretations when training models on multilingual data. Another bias could stem from unequal representation of languages within the dataset, where certain languages may have more instances than others, leading to imbalances in model performance across languages.

How might this research impact other fields outside of political science?

This research has the potential to impact several fields outside of political science by providing valuable insights into sentiment analysis and language processing techniques. In social sciences such as sociology and psychology, the findings from analyzing sentiments in parliamentary proceedings can offer new perspectives on public opinion dynamics and emotional responses to political discourse. In computational linguistics and natural language processing, the development of domain-specific transformer models trained on parliamentary data sets a precedent for creating specialized tools for analyzing text data in specific domains like legal documents or medical records. Moreover, advancements in multilingual modeling techniques can benefit industries like translation services, customer feedback analysis, and market research by improving cross-language understanding and communication capabilities.