What do people think about this monument? Understanding negative reviews via deep learning, clustering and descriptive rules
Journal Publication ResearchOnline@JCUAbstract
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.
Journal
Journal of Ambient Intelligence and Humanized Computing
Publication Name
N/A
Volume
11
ISBN/ISSN
1868-5145
Edition
N/A
Issue
N/A
Pages Count
14
Location
N/A
Publisher
Springer
Publisher Url
N/A
Publisher Location
N/A
Publish Date
N/A
Url
N/A
Date
N/A
EISSN
N/A
DOI
10.1007/s12652-018-1150-3