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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 7/2020

21.05.2020 | Regular Paper

Review selection based on content quality

verfasst von: Nan Tian, Yue Xu, Yuefeng Li

Erschienen in: Knowledge and Information Systems | Ausgabe 7/2020

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Abstract

Consumer-generated reviews have become increasingly important in decision-making processes for customers. Meanwhile, the overwhelming quantity of review data makes it extremely difficult to find useful information from it. A considerable amount of studies have attempted to address this problem by selecting reviews that might be helpful for and preferred by users. However, the performance of existing methods is far from ideal. One reason is because of lacking effective criteria to assess the quality of reviews. In this paper, we propose two novel measures, i.e. feature relevance and feature comprehensiveness, to assess the quality of reviews in terms of review content. A review selection approach is presented to select a set of reviews with high quality based on the two measures. Experiments on real-world review datasets show that our proposed method can assess the review quality effectively to improve the performance of review selection.
Vorheriger Artikel Coverage-based query subtopic diversification leveraging semantic relevance
Nächster Artikel Cyber security incidents analysis and classification in a case study of Korean enterprises

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Metadaten
Titel
Review selection based on content quality
verfasst von
Nan Tian
Yue Xu
Yuefeng Li
Publikationsdatum
21.05.2020
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 7/2020
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-020-01474-z

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