Abstract
User reviews submitted to app marketplaces contain information that falls into different categories, e.g., feature evaluation, feature request, and bug report. This information is valuable for developers to improve the quality of mobile applications. However, due to the large volume of reviews received every day, manual classification of user reviews into these categories is not feasible. Therefore, developing automatic classification methods using machine learning approaches is desirable. In this study, we address the problem of automatic classification of app review sentences (as opposed to full reviews) into different categories. We compare the simplest textual machine learning classifier using only lexical features - the so-called Bag-of-Words (BoW) approach - with more complex models used in previous work adopting rich linguistic features. We find that the performance of the simple BoW model is very competitive and has the advantage of not requiring any external linguistic tools to extract the features. Moreover, we experiment with deep learning based Convolutional Neural Network (CNN) models that have recently achieved state-of-the-art results in many classification tasks. We find that, on average, the CNN models do not perform significantly better than the simple BoW model. Finally, the manual analysis of misclassification errors and data annotations suggests that classifying review sentences in isolation does not always contain enough information to make a correct prediction. Thus, we suggest that adopting neural models to incorporate additional contextual knowledge might improve the classification performance.