摘要:随着APP软件应用的普及，针对APP软件的用户评论数量急剧增加，基于用户评论挖掘有价值的软件使用反馈，可以帮助开发人员有针对性地维护和改进APP软件.针对不同类型的APP软件使用反馈，提出了评价对象和评价观点抽取规则，给出了评论模式和评论种子的定义；应用评论种子挖掘与之相同或相似的体现相同使用反馈类型的用户评论，基于人工标注的少量初始评论种子持续构建候选评论模式库；应用半监督自学习的方式，基于候选评论模式库动态扩充评论种子库；通过循环挖掘的方式，动态扩大挖掘体现不同使用反馈类型的APP软件用户评论的范围.实验结果表明，所提方法可以有效地挖掘体现使用反馈的APP软件用户评论，平均挖掘率达到77.82%.



Abstract: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%.



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http://jos.org.cn/jos/article/pdf/5794