The sentiment corpus of parliamentary debates ParlaSent-BCS v1.0

Name: The sentiment corpus of parliamentary debates ParlaSent-BCS v1.0
License: https://creativecommons.org/licenses/by-sa/4.0/

Mochtak, Michal; Rupnik, Peter; Ljubešić, Nikola

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dc.contributor.author	Mochtak, Michal
dc.contributor.author	Rupnik, Peter
dc.contributor.author	Ljubešić, Nikola
dc.date.accessioned	2022-06-08T08:33:44Z
dc.date.available	2022-06-08T08:33:44Z
dc.date.issued	2022-06-08
dc.identifier.uri	http://hdl.handle.net/11356/1585
dc.description	The dataset consists of mid-length sentences from the Bosnian, Croatian and Serbian parliamentary proceedings, annotated with a 6-level sentiment schema (defined below). The first 1,300 instances were annotated by two annotators, and a reconciliation procedure was performed if there was disagreement on the simplified 3-level schema (Positive, Negative, Neutral). The latter 1,300 instances were annotated by second annotator only. Besides having the annotations of the two annotators and potential reconciliation annotations, there is also a handy 3-level label available for all instances. Each sentence can be followed back to the original datasets (https://doi.org/10.5281/zenodo.6517697, https://doi.org/10.5281/zenodo.6521372, https://doi.org/10.5281/zenodo.6521648) via a document and sentence identifier. Date of the speech and the speaker name are given as well. If the speaker is MP, information on party, gender and year of birth are available as well. The dataset is split into a training (2,150 instances), development (150 instances) and testing subset (300 instances). The full 6-level annotation schema 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 in a strict binary classification - M_Negative for sentences that convey an ambiguous sentiment or a mixture of sentiments, but lean more towards the negative sentiment in a strict binary classification - P_Neutral for sentences that only contain non-sentiment-related statements, but still lean more towards the positive sentiment in a strict binary classification - N_Neutral for sentences that only contain non-sentiment-related statements, but still lean more towards the negative sentiment in a strict binary classification
dc.language.iso	bos
dc.language.iso	hrv
dc.language.iso	srp
dc.publisher	Jožef Stefan Institute
dc.rights	Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
dc.rights.uri	https://creativecommons.org/licenses/by-sa/4.0/
dc.rights.label	PUB
dc.source.uri	https://www.clarin.eu/parlamint
dc.subject	sentiment classification
dc.subject	parliamentary debates
dc.title	The sentiment corpus of parliamentary debates ParlaSent-BCS v1.0
dc.type	corpus
metashare.ResourceInfo#ContentInfo.mediaType	text
has.files	yes
branding	CLARIN.SI data & tools
demo.uri	https://huggingface.co/classla/bcms-bertic-parlasent-bcs-ter
contact.person	Nikola Ljubešić nikola.ljubesic@ijs.si Jožef Stefan Institute
sponsor	CLARIN ERIC - ParlaMint: Towards Comparable Parliamentary Corpora Other
sponsor	ARRS (Slovenian Research Agency) P6-0411 Language Resources and Technologies for Slovene nationalFunds
sponsor	ARRS (Slovenian Research Agency) N6-0099 LiLaH: Linguistic Landscape of Hate Speech nationalFunds
size.info	2600 sentences
files.count	1
files.size	1187107