Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154256
Title: What do people think about this monument? Understanding negative reviews via deep learning, clustering and descriptive rules
Authors: Valdivia, A.
Martínez-Cámara, E.
Chaturvedi, Iti
Luzón, M. V.
Cambria, Erik
Ong, Yew-Soon
Herrera, F.
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Valdivia, 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/s12652-018-1150-3
Journal: Journal of Ambient Intelligence and Humanized Computing
Abstract: Aspect-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.
URI: https://hdl.handle.net/10356/154256
ISSN: 1868-5137
DOI: 10.1007/s12652-018-1150-3
Rights: © 2018 Springer-Verlag GmbH Germany, part of Springer Nature. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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