Mining millions of reviews: A technique to rank products based on importance of reviews

Kunpeng Zhang*, Yu Cheng, Wei Keng Liao, Alok Choudhary

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

46 Scopus citations


As online shopping becomes increasingly more popular, many shopping web sites encourage existing customers to add reviews of products purchased. These reviews make an impact on the purchasing decisions of potential customers. At for instance, some products receive hundreds of reviews. It is overwhelming and time restrictive for most customers to read, comprehend and make decisions based on all of these reviews. Customers most likely end up reading only a small fraction of the reviews usually in the order which they are presented on the product page. Incorporating various product review factors, such as: content related to product quality, time of the review, content related to product durability and historically older positive customer reviews will have different impacts on the products rankings. Thus, the automated mining of product reviews and opinions to produce a re-calculated product ranking score is a valuable tool which would allow potential customers to make more informed decisions. In this paper, we present a product ranking model that applies weights to product review factors to calculate a products ranking score. Our experiments use the customer reviews from as input to our product ranking model which produces product ranking results that closely relate to the products sales ranking as reported by the retailer.

Original languageEnglish (US)
Title of host publicationProceedings of the 13th International Conference on Electronic Commerce, ICEC'11
StatePublished - 2011
Event13th International Conference on Electronic Commerce, ICEC'11 - Liverpool, United Kingdom
Duration: Aug 3 2011Aug 5 2011

Publication series

NameACM International Conference Proceeding Series


Other13th International Conference on Electronic Commerce, ICEC'11
Country/TerritoryUnited Kingdom

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications


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