Quality-Aware Review Selection Based On Product Feature Taxonomy

User-generated information such as online reviews has become increasingly significant for customers in decision making processes. Meanwhile, as the volume of online reviews proliferates, there is an insistent demand to help users in tackling the information overload problem. A considerable amount of research has addressed the problem of extracting useful information from overwhelming reviews; among the proposed approaches we remind review summarization and review selection. Particularly, to address the issue of reducing 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, a 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 if its statistical property is close to that of the original review corpus, which is not preferred by the users. In this paper, we propose a review selection method which takes the reviews' 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 to 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.