Blurb Mining: Discovering Interesting Excerpts from E-commerce Product Reviews

Abstract

Product reviews on modern e-commerce websites have evolved into repositories of valuable firsthand opinions on products. Showcasing the opinions that reviewers express on a product in a succinct way can not only promote the product, but also provide an engaging experience that simplifies the shopping journey for online shoppers. In the case of traditional media such as movies and books, employingblurbs or excerpts from critic reviews for promotional purposes is an established practice among movie publicists and book editors that has proven to be an effective way of capturing attention of customers. Such excerpts can be discovered from e-commerce product reviews to highlight interesting reviewer opinions and add emotive elements to otherwise bland e-commerce product pages. While traditional movie or book blurbs are manually extracted, they must be automatically extracted from e-commerce product reviews owing to the scale of catalogues. Further, traditional blurbs are generally phrased to be very positive in tone and sometimes may take some words out of context. However, excerpts for e-commerce products must represent the true opinions of the reviewers and must capture the context in which the words were used to retain trust of users. To that end, we introduce the problem of extracting engaging excerpts from e-commerce product reviews in this paper. We present methods to automatically discover such excerpts from reviews at scale by leveraging natural language properties such as syntactic structure of sentences and sentiment, and discuss some of the underlying challenges. We further present an evaluation of the effectiveness of the proposed methods in terms of the quality of the blurbs generated and their ranking orders produced.