Spanish corpora for sentiment analysis: a survey

Navas Loro, María ORCID: https://orcid.org/0000-0003-1011-5023 and Rodríguez Doncel, Víctor ORCID: https://orcid.org/0000-0003-1076-2511 (2020). Spanish corpora for sentiment analysis: a survey. "Language Resources and Evaluation", v. 54 ; pp. 303-340. ISSN 1574020X. https://doi.org/10.1007/s10579-019-09470-8.

Descripción

Título: Spanish corpora for sentiment analysis: a survey
Autor/es:
Tipo de Documento: Artículo
Título de Revista/Publicación: Language Resources and Evaluation
Fecha: 1 Junio 2020
ISSN: 1574020X
Volumen: 54
Materias:
ODS:
Palabras Clave Informales: Corpora, Corpus, Emotion, Emotions, Knowledge, Model, Negation, Opinion mining, Opinions, Polarity, Quality education, Sentiment analysis, TASS
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Inteligencia Artificial
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Corpora play an important role when training machine learning systems for sentiment analysis. However, Spanish is underrepresented in these corpora, as most primarily include English texts. This paper describes 20 Spanish-language text corpora—collected to support different tasks related to sentiment analysis, ranging from polarity to emotion categorization. We present a brand-new framework for the characterization of corpora. This includes a number of features to help analyze resources at both corpus level and document level. This survey—besides depicting the overall landscape of corpora in Spanish—supports sentiment analysis practitioners with the task of selecting the most suitable resources.

Proyectos asociados

Tipo
Código
Acrónimo
Responsable
Título
Gobierno de España
TIN2016-78011-C4-2-R
Sin especificar
Sin especificar
Project Datos 4.0

Más información

ID de Registro: 93616
Identificador DC: https://oa.upm.es/93616/
Identificador OAI: oai:oa.upm.es:93616
URL Portal Científico: https://portalcientifico.upm.es/es/ipublic/item/5643816
Identificador DOI: 10.1007/s10579-019-09470-8
URL Oficial: https://link.springer.com/article/10.1007/s10579-0...
Depositado por: iMarina Portal Científico
Depositado el: 04 Feb 2026 14:04
Ultima Modificación: 06 Feb 2026 11:36