Identifying helpful reviews based on customer's mentions about experiences

As numerous on-line product reviews that vary in quality are published every day, much attention is being paid to quality assessment of such reviews. The current metric of using the number of votes by other customers such as 'helpful vote', despite its dominance, does not yield a fully effective outcome. In this article, we propose a novel metric to rank product reviews by 'mentions about experiences', accounting for customer's personal experiences, as a way of identifying high quality reviews. The proposed metric has two parameters that capture time expressions related to the use of products and product entities over different purchasing time periods by linguistic clues. The empirical results show that this metric is not only as helpful as the best existing metrics, 'helpful vote' or 'reviewer rank', but is also free from undesirable biases that either penalize recency or are driven solely by popularity. Our usability study also shows that ordering reviews by our metric is considered helpful on the accounts of both usefulness and satisfaction. (C) 2012 Elsevier Ltd. All rights reserved.