# The ParlaSent-BCS dataset of sentiment-annotated parliamentary debates from Bosnia-Herzegovina, Croatia, and Serbia

arXiv: https://arxiv.org/abs/2206.00929 · HTML: https://mlsanity.com/paper/2206.00929/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 43 references that are in the map.
- First posted on arXiv 2 Jun 2022.

## Builds on (most cited first, 5 of 5)

- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [BERTi'c -- The Transformer Language Model for Bosnian, Croatian, Montenegrin and Serbian](https://mlsanity.com/paper/2104.09243/) (2021)
- [FinEst BERT and CroSloEngual BERT: less is more in multilingual models](https://mlsanity.com/paper/2006.07890/) (2020)
- [Sentiment and position-taking analysis of parliamentary debates: A systematic literature review](https://mlsanity.com/paper/1907.04126/) (2019)

## Built on by (most cited first, 2 of 2)

- [The ParlaSent Multilingual Training Dataset for Sentiment Identification in Parliamentary Proceedings](https://mlsanity.com/paper/2309.09783/) (2023)
- [A Survey of Resources and Methods for Natural Language Processing of Serbian Language](https://mlsanity.com/paper/2304.05468/) (2023)

## Abstract (arXiv)

Expression of sentiment in parliamentary debates is deemed to be significantly different from that on social media or in product reviews. This paper adds to an emerging body of research on parliamentary debates with a dataset of sentences annotated for detection sentiment polarity in political discourse. We sample the sentences for annotation from the proceedings of three Southeast European parliaments: Croatia, Bosnia-Herzegovina, and Serbia. A six-level schema is applied to the data with the aim of training a classification model for the detection of sentiment in parliamentary proceedings. Krippendorff's alpha measuring the inter-annotator agreement ranges from 0.6 for the six-level annotation schema to 0.75 for the three-level schema and 0.83 for the two-level schema. Our initial experiments on the dataset show that transformer models perform significantly better than those using a simpler architecture. Furthermore, regardless of the similarity of the three languages, we observe differences in performance across different languages. Performing parliament-specific training and evaluation shows that the main reason for the differing performance between parliaments seems to be the different complexity of the automatic classification task, which is not observable in annotator performance. Language distance does not seem to play any role neither in annotator nor in automatic classification performance. We release the dataset and the best-performing model under permissive licences.