What do people think about this monument? Understanding negative reviews via deep learning, clustering and descriptive rules

Valdivia, Ana, Martínez-Cámara, Eugenio, Chaturvedi, Iti, Luzón, M. Victoria, Cambria, Erik, Ong, Yew Soon, and Herrera, Francisco (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. pp. 39-52.

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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.

Item ID: 63340
Item Type: Article (Research - C1)
ISSN: 1868-5145
Keywords: Aspect clustering, Deep learning, Sentiment analysis, Subgroup discovery
Copyright Information: © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
Funders: Spanish Government (SG), Juan de la Cierva Formación Programme (FJCI)
Projects and Grants: SG TIN2017-89517-P project, FJCI-2016-28353
Date Deposited: 14 Jul 2020 00:28
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing @ 100%
SEO Codes: 95 CULTURAL UNDERSTANDING > 9599 Other Cultural Understanding > 959999 Cultural Understanding not elsewhere classified @ 100%
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