User-stories are commonly used to define requirements in agile project management. In Software Product Lines (SPL), a user-story corresponds to a feature description (or part of it), that can be shared by several products. In practice, large SPL include a huge number of user-stories, making variability hard to grasp and handle. In this paper we present an exploratory approach that aims to guide the synthesis of Feature Models that capture and structure the commonalities and the variability expressed in these user-stories. The built Feature Models aim to help the project understanding, maintenance and evolution. Our approach first decomposes the user-stories to extract the roles and the features, using natural language processing techniques. In a second step, we group userstories having the same topics thanks to a clustering method. This contributes to extract more general features. In a third step, we leverage the use of Formal Concept Analysis to extract logical constraints between the features that guide Feature Model synthesis. We illustrate our approach using a dataset from our industrial partner.