Summarizing consumer reviews

摘要

E-commerce giants like Amazon rely on consumer reviews to allow buyers to inform other potential buyers about a product’s pros and cons. While these reviews can be useful, they are less so when the number of reviews is large; no consumer can be expected to read hundreds or thousands of reviews in order to gain better understanding about a product. In an effort to provide an aggregate representation of reviews, Amazon offers an average user rating represented by a 1- to 5-star score. This score only represents how reviewers feel about a product without providing insight into why they feel that way. In this work, we propose an AI technique that generates an easy-to-read, concise summary of a product based on its reviews. It provides an overview of the different aspects reviewers emphasize in their reviews and, crucially, how they feel about those aspects. Our methodology generates a list of the topics most-mentioned by reviewers, conveys reviewer sentiment for each topic and calculates an overall summary score that reflects reviewers’ overall sentiment about the product. These sentiment scores adapt the same 1- to 5-star scoring scale in order to remain familiar to Amazon users.