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DISCO PAL: Diachronic Spanish sonnet corpus with psychological and affective labels

dc.contributor.authorBarbado, Alberto
dc.contributor.authorFresno Fernández, Víctor Diego
dc.contributor.authorManjarrés Riesco, Ángeles
dc.contributor.authorRos Muñoz, Salvador
dc.date.accessioned2025-07-10T13:18:36Z
dc.date.available2025-07-10T13:18:36Z
dc.date.issued2021-10-13
dc.descriptionThe registered version of this article, first published in “Language Resources and Evaluation, 56(2), 501-542. (2021)", is available online at the publisher's website: Springer, https://doi.org/10.1007/S10579-021-09557-1 La versión registrada de este artículo, publicado por primera vez en “Language Resources and Evaluation, 56(2), 501-542. (2021)", está disponible en línea en el sitio web del editor: Springer, https://doi.org/10.1007/S10579-021-09557-1
dc.description.abstractNowadays, there are many applications of text mining over corpora from different languages. However, most of them are based on texts in prose, lacking applications that work with poetry texts. An example of an application of text mining in poetry is the usage of features derived from their individual words in order to capture the lexical, sublexical and interlexical meaning, and infer the General Affective Meaning (GAM) of the text. However, even though this proposal has been proved as useful for poetry in some languages, there is a lack of studies for both Spanish poetry and for highly-structured poetic compositions such as sonnets. This article presents a study over an annotated corpus of Spanish sonnets, in order to analyse if it is possible to build features from their individual words for predicting their GAM. The purpose of this is to model sonnets at an affective level. The article also analyses the relationship between the GAM of the sonnets and the content itself. For this, we consider the content from a psychological perspective, dentifying with tags when a sonnet is related to a specific term. Then, we study how GAM changes according to each of those psychological terms. The corpus used contains 274 Spanish sonnets from authors of different centuries, from fifteenth to nineteenth. This corpus was annotated by different domain experts. The experts annotated the poems with affective and lexico-semantic features, as well as with domain concepts that belong to psychology. Thanks to this, the corpus of sonnets can be used in different applications, such as poetry recommender systems, per- sonality text mining studies of the authors, or the usage of poetry for therapeutic purposes.en
dc.description.versionversión publicada
dc.identifier.citationBarbado, A., Fresno, V., Riesco, Á. M., & Ros, S. (2022). DISCO PAL: Diachronic Spanish sonnet corpus with psychological and affective labels. Language Resources and Evaluation, 56(2), 501-542. https://doi.org/10.1007/S10579-021-09557-1
dc.identifier.doihttps://doi.org/10.1007/S10579-021-09557-1
dc.identifier.issn1574-020X, eISSN 1574-0218
dc.identifier.urihttps://hdl.handle.net/20.500.14468/29387
dc.journal.titleLanguage Resources and Evaluation
dc.journal.volume56
dc.language.isoen
dc.page.final542
dc.page.initial501
dc.publisherSpringer
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentSistemas de Comunicación y Control
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.uriAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.subject1203.17 Informática
dc.subject3325 Tecnología de las telecomunicaciones
dc.subject5701 Lingüística aplicada
dc.subject.keywordsPoetryen
dc.subject.keywordsSpanish sonnetsen
dc.subject.keywordsAffectiveen
dc.subject.keywordsSemanticen
dc.subject.keywordsPsychologyen
dc.subject.keywordsNLPen
dc.titleDISCO PAL: Diachronic Spanish sonnet corpus with psychological and affective labelsen
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication80cd3492-0ff8-4c8e-a904-2858623c7fc1
relation.isAuthorOfPublicationf1dc37a9-f950-4aa8-b83c-632da9961d8d
relation.isAuthorOfPublicationd25ad74f-42fc-47ac-911d-1e5515319a58
relation.isAuthorOfPublication.latestForDiscovery80cd3492-0ff8-4c8e-a904-2858623c7fc1
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