Machine learning (ML) is increasingly being used as a key component of most software systems, yet serious concerns have been raised about the fairness of ML predictions. Researchers have been proposing novel methods to support the development of fair machine learning solutions. Nonetheless, most of them can only be used in late development stages, e.g., during model training, while there is a lack of methods that may provide practitioners with early fairness analytics enabling the treatment of fairness throughout the development lifecycle. This paper proposes ReFair, a novel context-aware requirements engineering framework that allows to classify sensitive features from User Stories. By exploiting natural language processing and word embedding techniques, our framework first identifies both the use case domain and the machine learning task to be performed in the system being developed; afterward, it recommends which are the context-specific sensitive features to be considered during the implementation. We assess the capabilities of ReFair by experimenting it against a synthetic dataset—which we built as part of our research—composed of 12,401 User Stories related to 34 application domains. Our findings showcase the high accuracy of ReFair, other than highlighting its current limitations.