App reviews in mobile app stores contain useful information which is used to improve applications and promote software evolution. This information is processed by automatic tools which prioritize reviews. In order to carry out this prioritization, reviews are decomposed into features like category and sentiment. Then, a weighted function assigns a weight to each feature and a review ranking is calculated. Unfortunately, in order to extract category and sentiment from reviews, its is required at least a classifier trained in an annotated corpus. Therefore this task is computational demanding. Thus, in this work, we propose Shannon Entropy as a simple feature which can replace standard features. Our results show that a Shannon Entropy based ranking is better than a standard ranking according to the NDCG metric. This result is promising even if we require fairness by means of algorithmic bias. Finally, we highlight a computational limit which appears in the search of the best ranking.