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

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.