With the popularity of App software applications, the number of user's comments for App software has increased dramatically. Mining valuable software usage feedback based on user's comments can help developers to maintain and improve App software pertinently. Aimed at different types of usage feedback for App software, this study proposes the extracting rules of evaluation object and evaluation opinion. Moreover, the comment modes and comment seeds are defined. User's comments that are same or similar to comment seeds reflecting usage feedback are mined. Based on the initial comment seeds labeled manually, a candidate comment mode library is built continuously. A semi-supervised learning method is used to dynamically expand the comment seed library based on the candidate comment mode library. The scope of mining user's comments reflecting usage feedback is expanded by interactive mining process. Finally, the experimental results show that the proposed method can effectively mine App software user's comments reflecting usage feedback with an average mining rate of 77.82%.