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Improving Attitude Words Classification for Opinion Mining using Word Embedding

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Improving Attitude Words Classification for Opinion Mining using Word Embedding

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dc.contributor.author Ortega-Bueno, Reynier es_ES
dc.contributor.author Medina-Pagola, José E. es_ES
dc.contributor.author Muñiz-Cuza, Carlos Enrique es_ES
dc.contributor.author Rosso, Paolo es_ES
dc.date.accessioned 2021-01-15T04:31:43Z
dc.date.available 2021-01-15T04:31:43Z
dc.date.issued 2019 es_ES
dc.identifier.issn 0302-9743 es_ES
dc.identifier.uri http://hdl.handle.net/10251/159146
dc.description.abstract [EN] Recognizing and classifying evaluative expressions is an important issue of sentiment analysis. This paper presents a corpus-based method for classifying attitude types (Affect, Judgment and Appreciation) and attitude orientation (positive and negative) of words in Spanish relying on the Attitude system of the Appraisal Theory. The main contribution lies in exploring large and unlabeled corpora using neural network word embedding techniques in order to obtain semantic information among words of the same attitude and orientation class. Experimental results show that the proposed method achieves a good effectiveness and outperforms the state of the art for automatic classification of attitude words in Spanish language. es_ES
dc.description.sponsorship The work of the fourth author was partially supported by the SomEMBED TIN2015-71147-C2-1-P research project (MINECO/FEDER). es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Lecture Notes in Computer Science es_ES
dc.relation.ispartof Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Appraisal framework es_ES
dc.subject Attitude classification es_ES
dc.subject Opinion mining es_ES
dc.subject Neural network word embedding es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Improving Attitude Words Classification for Opinion Mining using Word Embedding es_ES
dc.type Artículo es_ES
dc.type Comunicación en congreso es_ES
dc.type Capítulo de libro es_ES
dc.identifier.doi 10.1007/978-3-030-13469-3_112 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2015-71147-C2-1-P/ES/COMPRENSION DEL LENGUAJE EN LOS MEDIOS DE COMUNICACION SOCIAL - REPRESENTANDO CONTEXTOS DE FORMA CONTINUA/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Ortega-Bueno, R.; Medina-Pagola, JE.; Muñiz-Cuza, CE.; Rosso, P. (2019). Improving Attitude Words Classification for Opinion Mining using Word Embedding. Lecture Notes in Computer Science. 11401:971-982. https://doi.org/10.1007/978-3-030-13469-3_112 es_ES
dc.description.accrualMethod S es_ES
dc.relation.conferencename 23rd Iberoamerican Congress on Pattern Recognition (CIARP 2018) es_ES
dc.relation.conferencedate Noviembre 19-22,2018 es_ES
dc.relation.conferenceplace Madrid, Spain es_ES
dc.relation.publisherversion https://doi.org/10.1007/978-3-030-13469-3_112 es_ES
dc.description.upvformatpinicio 971 es_ES
dc.description.upvformatpfin 982 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 11401 es_ES
dc.relation.pasarela S\409384 es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.description.references Bloom, K., Argamon, S.: Automated learning of appraisal extraction patterns. In: Gries, S.T., Wulff, S., Davies, M. (eds.) Corpus Linguistic Applications: Current Studies, New Directions, Third edn., pp. 249–260. Rodopi B.V., Amsterdam, New York (2010) es_ES
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