Estimating True Ratings from Online Consumer Reviews

Online consumer reviews have emerged in the last decade as a promising starting point for monitoring and analyzing individual opinions about products and services. Especially the corresponding “star” ratings are frequently used by marketing researchers to address various aspects of electronic word-of-mouth (eWOM). But there also exist several studies which raise doubts about the general reliability of posted ratings. Against this background, we introduce a new framework based on the Beta Binomial True Intentions Model suggested by Morrison (J Mark Res 43(2):65–74, 1979) to accommodate the possible uncertainty inherent in the ratings contained in online consumer reviews. We show that, under certain conditions, the suggested framework is suitable to estimate “true” ratings from posted ones which proves advantageous in the case of rating-based predictions, e.g. with respect to the willingness to recommend a product or service. The theoretical considerations are illustrated by means of synthetic and real data.