An aspect-driven method for enriching product catalogs with user opinions

Abstract

Abstract In this paper, we propose a method for enriching product catalogs, which traditionally include only objective data provided by manufacturers or retailers, with subjective information extracted from reviews written by customers. Our method was designed to associate opinions taken from reviews with the product attributes they refer to. This is done by matching aspect expression identified in opinions with attributes from the product, which we model here as aspect classes. To verify the effectiveness of our method, we executed an extensive experimental evaluation that revealed that customers frequently mention aspects related to product attributes in their reviews. The attributes often receive more mentions than the product itself. Our method consistently reached almost 0.7 of F 1 measure in the task of associating the opinion with the correct attribute (or with the product as a whole), across four product categories, in two different scenarios. These results significantly improved the results achieved by a representative baseline.

Keywords

• Product catalog enrichment
• Aspect-based summarization
• Opinion mining
• Sentiment analysis
• e-Commerce
• Online reviews