TY - GEN
T1 - Mining millions of reviews
T2 - 13th International Conference on Electronic Commerce, ICEC'11
AU - Zhang, Kunpeng
AU - Cheng, Yu
AU - Liao, Wei Keng
AU - Choudhary, Alok
PY - 2011
Y1 - 2011
N2 - 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 Amazon.com 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 Amazon.com 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.
AB - 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 Amazon.com 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 Amazon.com 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.
UR - http://www.scopus.com/inward/record.url?scp=84867724826&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867724826&partnerID=8YFLogxK
U2 - 10.1145/2378104.2378116
DO - 10.1145/2378104.2378116
M3 - Conference contribution
AN - SCOPUS:84867724826
SN - 9781450314282
T3 - ACM International Conference Proceeding Series
BT - Proceedings of the 13th International Conference on Electronic Commerce, ICEC'11
Y2 - 3 August 2011 through 5 August 2011
ER -