metadata

license: cc-by-sa-4.0
language:
  - sl
  - en
  - cs
  - bs
  - hr
  - sr
  - sk
tags:
  - sentiment
  - classification
  - parliament
  - parlament
pretty_name: ParlaSent
size_categories:
  - 10K<n<100K
configs:
  - config_name: EN
    data_files: ParlaSent_EN.jsonl
  - config_name: BCS
    data_files: ParlaSent_BCS.jsonl
  - config_name: CZ
    data_files: ParlaSent_CZ.jsonl
  - config_name: SK
    data_files: ParlaSent_SK.jsonl
  - config_name: SL
    data_files: ParlaSent_SL.jsonl
  - config_name: EN_additional_test
    data_files: ParlaSent_EN_test.jsonl
  - config_name: BCS_additional_test
    data_files: ParlaSent_BCS_test.jsonl
task_categories:
  - text-classification

The multilingual sentiment dataset of parliamentary debates ParlaSent 1.0

Dataset Description

Repository: Clarin.si repo
Paper: https://arxiv.org/abs/2309.09783

Dataset Summary

This dataset was created and used for sentiment analysis experiments.

The dataset consists of five training datasets and two test sets. The test sets have a _test.jsonl suffix and appear in the Dataset Viewer as _additional_test.

Each test set consists of 2,600 sentences, annotated by one highly trained annotator. Training datasets were internally split into "train", "dev" and "test" portions" for performing language-specific experiments.

The 6-level annotation schema, used by annotators, is the following:

Positive for sentences that are entirely or predominantly positive
Negative for sentences that are entirely or predominantly negative
M_Positive for sentences that convey an ambiguous sentiment or a mixture of sentiments, but lean more towards the positive sentiment
M_Negative for sentences that convey an ambiguous sentiment or a mixture of sentiments, but lean more towards the negative sentiment
P_Neutral for sentences that only contain non-sentiment-related statements, but still lean more towards the positive sentiment
N_Neutral for sentences that only contain non-sentiment-related statements, but still lean more towards the negative sentiment

Dataset is described in detail in our paper.

Data Attributes

The attributes in training data are the following:

sentence - the sentence labeled for sentiment
country - the country of the parliament the sentence comes form
annotator1 - first annotator's annotation
annotator2 - second annotator's annotation
reconciliation - the final label agreed upon after reconciliation
label - three level (positive, negative, neutral) label based on the reconciliation label
document_id - internal identifier of the document the sentence comes form
sentence_id - internal identifier of the sentence inside the document
term - the term of the parliament the sentence comes from
date - the date the sentence was uttered as part of a speech in the parliament
name - name of the MP giving the speech
party - the party of the MP
gender - binary gender of the MP
birth year - year of birth of the MP
split - whether the sentence is to be used as a training, development or testing instance in case evaluation is done of the training portion of the dataset
ruling - whether the MP was in a coalition or an opposition at the time of giving the speech

The attributes in the test data (_test.jsonl files) are the following:

sentence - the sentence labeled for sentiment
country - the country of the parliament the sentence comes form
annotator1 - first (only) annotator's annotation, used as a final annotation
label - three level (positive, negative, neutral) label based on the annotator1 label
document_id - internal identifier of the document the sentence comes form
sentence_id - internal identifier of the sentence inside the document
term - the term of the parliament the sentence comes from
date - the date the sentence was uttered as part of a speech in the parliament
name - name of the MP giving the speech
party - the party of the MP
gender - binary gender of the MP
birth year - year of birth of the MP
ruling - whether the MP was in a coalition or an opposition at the time of giving the speech

Citation information

Please quote the following paper:

@article{
 Mochtak_Rupnik_Ljubešić_2023,
 title={The ParlaSent multilingual training dataset for sentiment identification in parliamentary proceedings},
 rights={All rights reserved},
 url={http://arxiv.org/abs/2309.09783},
 abstractNote={Sentiments inherently drive politics. How we receive and process information plays an essential role in political decision-making, shaping our judgment with strategic consequences both on the level of legislators and the masses. If sentiment plays such an important role in politics, how can we study and measure it systematically? The paper presents a new dataset of sentiment-annotated sentences, which are used in a series of experiments focused on training a robust sentiment classiﬁer for parliamentary proceedings. The paper also introduces the ﬁrst domain-speciﬁc LLM for political science applications additionally pre-trained on 1.72 billion domain-speciﬁc words from proceedings of 27 European parliaments. We present experiments demonstrating how the additional pre-training of LLM on parliamentary data can signiﬁcantly improve the model downstream performance on the domain-speciﬁc tasks, in our case, sentiment detection in parliamentary proceedings. We further show that multilingual models perform very well on unseen languages and that additional data from other languages signiﬁcantly improves the target parliament’s results. The paper makes an important contribution to multiple domains of social sciences and bridges them with computer science and computational linguistics. Lastly, it sets up a more robust approach to sentiment analysis of political texts in general, which allows scholars to study political sentiment from a comparative perspective using standardized tools and techniques.},
 note={arXiv:2309.09783 [cs]},
 number={arXiv:2309.09783},
 publisher={arXiv},
 author={Mochtak, Michal and Rupnik, Peter and Ljubešić, Nikola},
 year={2023},
 month={Sep},
 language={en}
}