Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154256
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dc.contributor.authorValdivia, A.en_US
dc.contributor.authorMartínez-Cámara, E.en_US
dc.contributor.authorChaturvedi, Itien_US
dc.contributor.authorLuzón, M. V.en_US
dc.contributor.authorCambria, Eriken_US
dc.contributor.authorOng, Yew-Soonen_US
dc.contributor.authorHerrera, F.en_US
dc.date.accessioned2021-12-16T06:43:35Z-
dc.date.available2021-12-16T06:43:35Z-
dc.date.issued2020-
dc.identifier.citationValdivia, A., Martínez-Cámara, E., Chaturvedi, I., Luzón, M. V., Cambria, E., Ong, Y. & Herrera, F. (2020). What do people think about this monument? Understanding negative reviews via deep learning, clustering and descriptive rules. Journal of Ambient Intelligence and Humanized Computing, 11(1), 39-52. https://dx.doi.org/10.1007/s12652-018-1150-3en_US
dc.identifier.issn1868-5137en_US
dc.identifier.urihttps://hdl.handle.net/10356/154256-
dc.description.abstractAspect-based sentiment analysis enables the extraction of fine-grained information, as it connects specific aspects that appear in reviews with a polarity. Although we detect that the information from these algorithms is very accurate at local level, it does not contribute to obtain an overall understanding of reviews. To fill this gap, we propose a methodology to portray opinions through the most relevant associations between aspects and polarities. Our methodology combines three off-the-shelf algorithms: (1) deep learning for extracting aspects, (2) clustering for joining together similar aspects, and (3) subgroup discovery for obtaining descriptive rules that summarize the polarity information of set of reviews. Concretely, we aim at depicting negative opinions from three cultural monuments in order to detect those features that need to be improved. Experimental results show that our approach clearly gives an overview of negative aspects, therefore it will be able to attain a better comprehension of opinions.en_US
dc.language.isoenen_US
dc.relation.ispartofJournal of Ambient Intelligence and Humanized Computingen_US
dc.rights© 2018 Springer-Verlag GmbH Germany, part of Springer Nature. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleWhat do people think about this monument? Understanding negative reviews via deep learning, clustering and descriptive rulesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.identifier.doi10.1007/s12652-018-1150-3-
dc.identifier.scopus2-s2.0-85058975565-
dc.identifier.issue1en_US
dc.identifier.volume11en_US
dc.identifier.spage39en_US
dc.identifier.epage52en_US
dc.subject.keywordsSentiment Analysisen_US
dc.subject.keywordsDeep Learningen_US
dc.description.acknowledgementWe would like to thank the reviewers for their thoughtful comments and eforts towards improving our manuscript. This research work was supported by the TIN2017-89517-P project from the Spanish Government. Eugenio Martínez-Cámara was supported by the Juan de la Cierva Formación Programme (FJCI-2016- 28353) also from the Spanish Government.en_US
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