Abstract

Customers buy products without watching and using products directly in online-shopping. Customers cannot know product's details. Customers can make mistake in selecting products and regret this selection. Our research aims to extract useful information for deciding products customers buy in product review of online-shopping websites. We can prevent customers' regret by using this information. In this paper, we discover common factors between useful reviews for deciding products customers buy in online-shopping. We use factor analyses of actual reviews to discover common factors. We make the dataset for factor analyses in an experiment. The dataset is the set of critical sentences of actual reviews and their scores to evaluate their characteristics, which the examinees give. In this result, we can extract the common factors by which the useful reviews can be derived with the precision over 50 %. The common factors can be used for extracting useful information in reviews. Customers can shop without regrets in online-shopping by showing this information.

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