Show me the money!: deriving the pricing power of product features by mining consumer reviews

The increasing pervasiveness of the Internet has dramatically changed the way that consumers shop for goods. Consumergenerated product reviews have become a valuable source of information for customers, who read the reviews and decide whether to buy the product based on the information provided. In this paper, we use techniques that decompose the reviews into segments that evaluate the individual characteristics of a product (e.g., image quality and battery life for a digital camera). Then, as a major contribution of this paper, we adapt methods from the econometrics literature, specifically the hedonic regression concept, to estimate: (a) the weight that customers place on each individual product feature, (b) the implicit evaluation score that customers assign to each feature, and (c) how these evaluations affect the revenue for a given product. Towards this goal, we develop a novel hybrid technique combining text mining and econometrics that models consumer product reviews as elemen...

• Type: Conference
• Year: 2007
• Where: KDD
• Authors: Nikolay Archak, Anindya Ghose, Panagiotis G. Ipeirotis
• Keywords: Data Mining, Individual Product Feature, KDD 2007, Product Reviews, Tensor Product Space