With the use of manual annotations and statistical analysis techniques, scientists have demonstrated that a considerable portion of mobile app reviews are relevant to app evolution. Based on such demonstrations, variety of automatic classification and extraction approaches are proposed to assist developers in parsing and processing user reviews. However, there is a major concern among both scientific community and development teams over the quality and usefulness of user reviews from developers’ point of view. Justifying that a review is relevant to software evolution does not necessarily indicate that it is useful for developers. Therefore, it is not incorrect to argue that, despite attempts of existing exploratory studies, the usefulness of user reviews from developers’ viewpoint is still obscure. Accordingly, effectiveness of existing extraction tools developed based on such hypothesis is under question. In this project, useful mobile app reviews from developers’ perspective are elicited by definition of a set of criteria and application of it using qualitative content analysis techniques. An automatic model is then to be proposed to assess the usefulness of a mobile app review based on the predefined criteria. Performance of the model would be examined on real world data and fine-tuned accordingly.