A time and opinion quality-weighted model for aggregating online reviews

Shaalan, Y and Zhang, X 2016, 'A time and opinion quality-weighted model for aggregating online reviews', in Muhammad Aamir Cheema, Wenjie Zhang, Lijun Chang (ed.) Proceedings of the 27th Australasian Database Conference (ADC 2016), Sydney, Australia, 28-29 September 2016, pp. 269-282.


Document type: Conference Paper
Collection: Conference Papers

Title A time and opinion quality-weighted model for aggregating online reviews
Author(s) Shaalan, Y
Zhang, X
Year 2016
Conference name ADC 2016: Databases Theory and Applications
Conference location Sydney, Australia
Conference dates 28-29 September 2016
Proceedings title Proceedings of the 27th Australasian Database Conference (ADC 2016)
Editor(s) Muhammad Aamir Cheema, Wenjie Zhang, Lijun Chang
Publisher Springer
Place of publication Switzerland
Start page 269
End page 282
Total pages 14
Abstract Online reviews are playing important roles for the online shoppers to make buying decisions. However, reading all or most of the reviews is an overwhelming and time consuming task. Many online shopping websites provide aggregate scores for products to help consumers to make decisions. Averaging star ratings from all online reviews is widely used but is hardly effective for ranking products. Recent research proposed weighted aggregation models, where weighting heuristics include opinion polarities from mining review textual contents as well as distribution of star ratings. But the quality of opinions in reviews is largely ignored in existing aggregation models. In this paper we propose a novel review weighting model combining the information on the posting time and opinion quality of reviews. In particular, we make use of helpfulness votes for reviews from the online review communities to measure opinion quality. Our model generates aggregate scores to rank products. Extensive experiments on an Amazon dataset showed that our model ranked products in strong correspondence with customer purchase rank and outperformed several other approaches.
Subjects Database Management
Keyword(s) Product Ranking
Rating Aggregation
Online Review
Opinion Evaluation
DOI - identifier 10.1007/978-3-319-46922-5
Copyright notice © Springer International Publishing AG 2016
ISBN 9783319469218
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