Quality-aware review selection based on product feature taxonomy

Tian, Nan, Xu, Yue, Li, Yuefeng, & Pasi, Gabriella (2015) Quality-aware review selection based on product feature taxonomy. In Asia Information Retrieval Societies Conference (AIRS 2015), 2-4 December 2015, Brisbane, Queensland, Australia. (Unpublished)

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As of today, user-generated information such as online reviews has become increasingly significant for customers in decision making process. Meanwhile, as the volume of online reviews proliferates, there is an insistent demand to help the users tackle the information overload problem. In order to extract useful information from overwhelming reviews, considerable work has been proposed such as review summarization and review selection. Particularly, to avoid the redundant information, researchers attempt to select a small set of reviews to represent the entire review corpus by preserving its statistical properties (e.g., opinion distribution). However, one significant drawback of the existing works is that they only measure the utility of the extracted reviews as a whole without considering the quality of each individual review. As a result, the set of chosen reviews may consist of low-quality ones even its statistical property is close to that of the original review corpus, which is not preferred by the users. In this paper, we proposed a review selection method which takes review quality into consideration during the selection process. Specifically, we examine the relationships between product features based upon a domain ontology to capture the review characteristics based on which to select reviews that have good quality and preserve the opinion distribution as well. Our experimental results based on real world review datasets demonstrate that our proposed approach is feasible and able to improve the performance of the review selection effectively.

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ID Code: 87301
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: review selection, review quality, product feature taxonomy
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 [please consult the authors]
Deposited On: 08 Sep 2015 05:19
Last Modified: 30 Apr 2016 23:48

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