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Why do people (not) like me?: Mining opinion influencing factors from reviews

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Bilici, Eda and Saygin, Yucel (2017) Why do people (not) like me?: Mining opinion influencing factors from reviews. Expert Systems with Applications, 68 . pp. 185-195. doi:10.1016/j.eswa.2016.10.001

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Official URL: http://dx.doi.org/10.1016/j.eswa.2016.10.001

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Abstract

Feedback, without doubt, is a very important mechanism for companies or political parties to re-evaluate and improve their processes or policies. In this paper, we propose opinion influencing factors (OIFs) as a means to provide feedback about what influences the opinions of people. We also describe a methodology to mine OIFs from textual documents with the intention to bring a new perspective to the existing recommendation systems by concentrating on service providers (or policy makers) rather than customers. This new perspective enables one to discover the reasons why people like or do not like something by learning relationships among the traits/products via semantic rules and the factors that lead to change on the opinions such as from positive to negative. As a case study we target the healthcare domain, and experiment with the patients’ reviews on doctors. Experimental results show the gist of thousands of comments on particular aspects (also called as factors) associated with semantic rules in an e↵ective way.

Item Type: Journal Article
Divisions: Faculty of Science > WMG (Formerly the Warwick Manufacturing Group)
Journal or Publication Title: Expert Systems with Applications
Publisher: Pergamon-Elsevier Science Ltd.
ISSN: 0957-4174
Official Date: February 2017
Dates:
DateEvent
February 2017Published
8 October 2016Available
3 October 2016Accepted
27 July 2016Submitted
Volume: 68
Page Range: pp. 185-195
DOI: 10.1016/j.eswa.2016.10.001
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
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