ExtRA: Extracting Prominent Review Aspects from Customer Feedback
Zhiyi Luo, Shanshan Huang, Frank F. Xu, Bill Yuchen Lin, Hanyuan Shi, Kenny Qili Zhu · 2018
Many existing systems for analyzing and summarizing customer reviews about products or service are based on a number of prominent review aspects.Conventionally, the prominent review aspects of a product type are determined manually.This costly approach cannot scale to large and cross-domain services such as Amazon.com,Taobao.com or Yelp.comwhere there are a large number of product types and new products emerge almost everyday.In this paper, we propose a novel framework, for extracting the most prominent aspects of a given product type from textual reviews.The proposed framework, ExtRA, extracts K most prominent aspect terms or phrases which do not overlap semantically automatically without supervision.Extensive experiments show that ExtRA is effective and achieves the state-of-the-art performance on a dataset consisting of different product types.