A proposal of review extraction method considering the knowledge amount of consumers and the purchase stage

Abstract

: Today internet shopping sites are widely used. On such sites, consumers cannot take products in their hands, so user reviews of products must be informative for the consumers. However, number of reviews are very large, so consumers cannot read them all. The may miss a very important information which can catch reading all reviews. Purpose of our research is to make a method of indicating only useful reviews for reducing consumer’s burden. We assume that useful reviews are different by consumers. We found that usefulness of reviews differs from consumers’ purpose of browsing review, and also from consumers’ knowledge about purchase target product group. We can classify consumers into six groups. Next we identified factors of useful reviews for each group. Finally we constructed models for evaluating usefulness of each reviews for each group. We made dictionaries for each factor to judge which factors are included in each review. For validating our models, we conducted questionnaires’ survey. Consumers were shown two groups of user reviews, one group contains top 10 useful reviews based on our model, and the other contains original 10 reviews which shopping site shows. We could confirm that our model can show more useful reviews to consumers.