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Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet

Emotion lexicons are useful in research across various disciplines, but the availability of such resources remains limited for most languages. While existing emotion lexicons typically comprise words, it is a particular meaning of a word (rather than the word itself) that conveys emotion. To mitigat...

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Autores principales: Wierzba, Małgorzata, Riegel, Monika, Kocoń, Jan, Miłkowski, Piotr, Janz, Arkadiusz, Klessa, Katarzyna, Juszczyk, Konrad, Konat, Barbara, Grimling, Damian, Piasecki, Maciej, Marchewka, Artur
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579083/
https://www.ncbi.nlm.nih.gov/pubmed/34893969
http://dx.doi.org/10.3758/s13428-021-01697-0
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author Wierzba, Małgorzata
Riegel, Monika
Kocoń, Jan
Miłkowski, Piotr
Janz, Arkadiusz
Klessa, Katarzyna
Juszczyk, Konrad
Konat, Barbara
Grimling, Damian
Piasecki, Maciej
Marchewka, Artur
author_facet Wierzba, Małgorzata
Riegel, Monika
Kocoń, Jan
Miłkowski, Piotr
Janz, Arkadiusz
Klessa, Katarzyna
Juszczyk, Konrad
Konat, Barbara
Grimling, Damian
Piasecki, Maciej
Marchewka, Artur
author_sort Wierzba, Małgorzata
collection PubMed
description Emotion lexicons are useful in research across various disciplines, but the availability of such resources remains limited for most languages. While existing emotion lexicons typically comprise words, it is a particular meaning of a word (rather than the word itself) that conveys emotion. To mitigate this issue, we present the Emotion Meanings dataset, a novel dataset of 6000 Polish word meanings. The word meanings are derived from the Polish wordnet (plWordNet), a large semantic network interlinking words by means of lexical and conceptual relations. The word meanings were manually rated for valence and arousal, along with a variety of basic emotion categories (anger, disgust, fear, sadness, anticipation, happiness, surprise, and trust). The annotations were found to be highly reliable, as demonstrated by the similarity between data collected in two independent samples: unsupervised (n = 21,317) and supervised (n = 561). Although we found the annotations to be relatively stable for female, male, younger, and older participants, we share both summary data and individual data to enable emotion research on different demographically specific subgroups. The word meanings are further accompanied by the relevant metadata, derived from open-source linguistic resources. Direct mapping to Princeton WordNet makes the dataset suitable for research on multiple languages. Altogether, this dataset provides a versatile resource that can be employed for emotion research in psychology, cognitive science, psycholinguistics, computational linguistics, and natural language processing. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13428-021-01697-0.
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spelling pubmed-95790832022-10-20 Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet Wierzba, Małgorzata Riegel, Monika Kocoń, Jan Miłkowski, Piotr Janz, Arkadiusz Klessa, Katarzyna Juszczyk, Konrad Konat, Barbara Grimling, Damian Piasecki, Maciej Marchewka, Artur Behav Res Methods Article Emotion lexicons are useful in research across various disciplines, but the availability of such resources remains limited for most languages. While existing emotion lexicons typically comprise words, it is a particular meaning of a word (rather than the word itself) that conveys emotion. To mitigate this issue, we present the Emotion Meanings dataset, a novel dataset of 6000 Polish word meanings. The word meanings are derived from the Polish wordnet (plWordNet), a large semantic network interlinking words by means of lexical and conceptual relations. The word meanings were manually rated for valence and arousal, along with a variety of basic emotion categories (anger, disgust, fear, sadness, anticipation, happiness, surprise, and trust). The annotations were found to be highly reliable, as demonstrated by the similarity between data collected in two independent samples: unsupervised (n = 21,317) and supervised (n = 561). Although we found the annotations to be relatively stable for female, male, younger, and older participants, we share both summary data and individual data to enable emotion research on different demographically specific subgroups. The word meanings are further accompanied by the relevant metadata, derived from open-source linguistic resources. Direct mapping to Princeton WordNet makes the dataset suitable for research on multiple languages. Altogether, this dataset provides a versatile resource that can be employed for emotion research in psychology, cognitive science, psycholinguistics, computational linguistics, and natural language processing. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.3758/s13428-021-01697-0. Springer US 2021-12-10 2022 /pmc/articles/PMC9579083/ /pubmed/34893969 http://dx.doi.org/10.3758/s13428-021-01697-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Wierzba, Małgorzata
Riegel, Monika
Kocoń, Jan
Miłkowski, Piotr
Janz, Arkadiusz
Klessa, Katarzyna
Juszczyk, Konrad
Konat, Barbara
Grimling, Damian
Piasecki, Maciej
Marchewka, Artur
Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet
title Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet
title_full Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet
title_fullStr Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet
title_full_unstemmed Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet
title_short Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet
title_sort emotion norms for 6000 polish word meanings with a direct mapping to the polish wordnet
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579083/
https://www.ncbi.nlm.nih.gov/pubmed/34893969
http://dx.doi.org/10.3758/s13428-021-01697-0
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