User feedback is a valuable source of information for app developers. in this context reviews on app stores can often give insights on problems experienced by the userbase as well as requests for new features. However the usually immense amount of these reviews poses a relevant challenge to the knowledge extraction process. Therefore the proposal of this work is to exploit Natural Language Processing and Machine Learning techniques to help developers in the identification and assessment of anomalies and feature requests signaled by their users via app reviews. To this end the first part is dedicated to review the current state of the art in terms of methods and results.