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Mobile app distribution platforms such as Google Play Store allow users toshare their feedback about downloaded apps in the form of a review comment anda corresponding star rating. Typically, the star rating ranges from one to fivestars, with one star denoting a high sense of dissatisfaction with the app andfive stars denoting a high sense of satisfaction.  Unfortunately, due to a variety of reasons, often the star rating provided bya user is inconsistent with the opinion expressed in the review. For example,consider the following review for the Facebook App on Android; "Awesome App".One would reasonably expect the rating for this review to be five stars, butthe actual rating is one star!  Such inconsistent ratings can lead to a deflated (or inflated) overallaverage rating of an app which can affect user downloads, as typically userslook at the average star ratings while making a decision on downloading an app.Also, the app developers receive a biased feedback about the application thatdoes not represent ground reality. This is especially significant for smallapps with a few thousand downloads as even a small number of mismatched reviewscan bring down the average rating drastically.  In this paper, we conducted a study on this review-rating mismatch problem.We manually examined 8600 reviews from 10 popular Android apps and found that20% of the ratings in our dataset were inconsistent with the review. Further,we developed three systems; two of which were based on traditional machinelearning and one on deep learning to automatically identify reviews whoserating did not match with the opinion expressed in the review. Our deeplearning system performed the best and had an accuracy of 92% in identifyingthe correct star rating to be associated with a given review.

Fault in your stars: An Analysis of Android App Reviews