Dynamic PScore: A Dynamic Method to Prioritize User Reviews

Abstract

Abstract Since the emergence of mobile apps, user reviews have been of great importance for app developers as they contain users’ sentiment, bugs and new requests. Due to the large number of reviews, it is a difficult, time-consuming and error-prone task to prioritize them manually. Having a tool or method for automated prioritization of reviews could save the development team’s time, help application development and maintenance cycle and prevent the development team’s errors. Various methods have been presented for prioritizing reviews, most of which have focused on old features that are no longer valid or ignored new features provided by the store. This study provides a method, called Dynamic PScore, for dynamic prioritization of reviews into five categories of hot, serious, pay attention, getting traction and not serious in the Google Play store. In this method, the score is calculated using ThumbsUp features (popularity of each review), extracting sentiment and considering the number of words in the review and, then, prioritization is done through the obtained score. To experimentally evaluate the proposed method in PPrior database, the corresponding score is calculated for each review and prioritization is done. Finally, the results indicate the presented method has the Accuracy and PSP of 99.94% and 99.86%, respectively, in prioritizing and scoring reviews and its prioritization accuracy has improved by 13.34% compared to recent research.