In mobile apps (applications), the app reviews by users have become an important feedback resource. Users may raise some issues when they use apps, such as system compatibility issues, application crashes, and so on. With the development of mobile apps, users provide a large number of unstructured feedback comments. In order to extract effective information from user complaint comments, a review analysis method is proposed based on support vector machine (SVM) and latent dirichlet allocation (LDA) (RASL) which can help developers to understand user feedback better and faster. Firstly, features are extracted from the user neutral reviews and negative reviews, and then the support vector machine (SVM) is used to label comments on multiple tags. Next, the LDA topic model is used to get topic extraction and representative sentence extraction which are performed on the comments under each question type. 5141 original reviews are crawled from two mobile apps. Then the proposed method (RASL) and ASUM are used to process these comments to get new texts. In comparison with the classical approach ASUM, RASL has less perplexity, better understandability, more complete original review information, and less redundant information.