Consumers researching products for the purposes of making purchasing decisions frequently visit online shopping portals sites. These sites such as Google Product Search1, Bing Shopping2 or Yahoo! Shopping3 aggregate many types of content for the consumer: editorial and user reviews, buying guides, and price comparison tools. But missing from the current product research landscape is the presence of large-scale conversational reviews, such as those found on online forums and discussion boards. In these sites, frequently many authors share their first-hand experiences with products, as well as troubleshooting tips, advice, or general discussion. There are an enormous variety of online forums on the web, generally topically focused and often cultivating active communities of enthusiastic contributors. These types of social media outlets, however, can be difficult to discover by individuals who may not already be familiar with the community. The current tools to access online forum archives are lacking, and although web search engines index online forum data, many distinguishing characteristics of online forums are ignored by traditional ad-hoc information retrieval techniques. Additionally, to our knowledge, there are no publicly available tools to help in identifying forums, rather than forum threads or posts. This paper addresses the task of identifying discussion forums rich with product-related discussion. In these forums a potential shopper may find first-hand reviews, product comparisons or other user experiences. We approach this task as an information retrieval problem, ranking forums with respect to product search related information needs. This system is designed to integrate with a shopping portal to provide users with access to archives of community generated commentary as well as a forum to interact with experts and enthusiasts when making purchasing decisions. The main contribution of this work is on a novel forum ranking model (Section 4.3), aimed at identifying online forums with a high density of discussions on product-related topics. This ranking model leverages a rich set of document annotations: document classifications, identification of the structure within the forum, annotation of product mentions, and categorization of those mentions to a product ontology. The ranking model achieves greater than 85% precision at the top ranked result and is preferred or equivalent to web results restricted to online forum pages 80% of the time.