Search functionality in web shops is limited today. Consumers can only search for a restricted set of standard product features for each product group. A major part of the relevant information, especially reviews, is only available as an extension to the product description, that is, after a product has been found and therefore quite late in the purchase process. With the growing numbers of reviews, reading review texts is a burden for consumers, and there is a need to rearrange and organize user-generated content. Integrating mined product features into the product search might therefore add value to the customer experience. Following design science principles, we propose an approach to mine frequent product feature sets from social media content and enhance product search with sets of product features to create a “social product search”. We contribute a design science artefact in form of a situated implementation. For illustration, we present an example for the product group notebooks with 22480 reviews of 2745 products that we crawled from amazon.com. Further, we depict three application scenarios how mined frequent product feature sets can be integrated into the product search and enhance the consumer search experience.