? CSLabel:一种为移动评论添加标签的方法
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Journal of Computer Science and Technology 2017, Vol. 32 Issue (6) :1076-1089    DOI: 10.1007/s11390-017-1784-1
Special Section on Software Systems 2017 << Previous Articles | Next Articles >>
CSLabel:一种为移动评论添加标签的方法
Li Zhang, Senior Member, CCF, Member, Xin-Yue Huang, Jing Jiang*, Member, CCF, Ya-Kun Hu
State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China
CSLabel:An Approach for Labelling Mobile App Reviews
Li Zhang, Senior Member, CCF, Member, Xin-Yue Huang, Jing Jiang*, Member, CCF, Ya-Kun Hu
State Key Laboratory of Software Development Environment, Beihang University, Beijing 100191, China

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摘要 移动应用程序是一种目前流行的软件形式,用户对于移动应用程序的评论是重要的反馈资源。用户在评论中可能会提到使用应用时遇到的一些问题,例如功能缺陷,网络延迟或请求添加功能等。理解这些问题一方面可以帮助开发人员了解用户的关注点,另一方面也可以帮助用户决定是否下载或购买某些应用程序。但是,我们尚不了解用户评论中包含哪些类型的问题;同时,用户评论的数量十分巨大,而评论的文本又是非结构化和非正式的。因此,在本文中,我们首先分析了一个中国应用商店-360手机助手中的11个应用下的3,902条用户评论,并发现了17种问题类型。然后,我们提出一种可以基于问题类型来标注用户评论的方法-CSLabel。CSLabel采用了代价敏感的学习方法来减轻不平衡数据的影响,并优化了SVM分类器核函数的设置。结果表明,CSLabel准确率达到66.5%,召回率达到69.8%,F值为69.8%。与当前最好的方法相比,CSLabel将准确率提高了14%,召回率提高了30%,F值提高了22%。最后,将我们的方法应用于实际场景中:我们提供了360手机助手中1100个应用的1,076,786条用户评论的问题占比概览,并发现某些问题类型与用户对应用的评价存在负相关关系。
关键词移动应用   用户评论   分类     
Abstract: Mobile apps (applications) have become a popular form of software, and the app reviews by users have become an important feedback resource. Users may raise some issues in their reviews when they use apps, such as a functional bug, a network lag, or a request for a feature. Understanding these issues can help developers to focus on users' concerns, and help users to evaluate similar apps for download or purchase. However, we do not know which types of issues are raised in a review. Moreover, the amount of user reviews is huge and the nature of the reviews' text is unstructured and informal. In this paper, we analyze 3 902 user reviews from 11 mobile apps in a Chinese app store-360 Mobile Assistant, and uncover 17 issue types. Then, we propose an approach CSLabel that can label user reviews based on the raised issue types. CSLabel uses a cost-sensitive learning method to mitigate the effects of the imbalanced data, and optimizes the setting of the support vector machine (SVM) classifier's kernel function. Results show that CSLabel can correctly label reviews with the precision of 66.5%, the recall of 69.8%, and the F1 measure of 69.8%. In comparison with the state-of-the-art approach, CSLabel improves the precision by 14%, the recall by 30%, the F1 measure by 22%. Finally, we apply our approach to two real scenarios:1) we provide an overview of 1 076 786 user reviews from 1 100 apps in the 360 Mobile Assistant and 2) we find that some issue types have a negative correlation with users' evaluation of apps.
Keywordsmobile app   user review   classification     
Received 2017-04-20;
本文基金:

This work is supported by the National Natural Science Foundation of China under Grant No. 61672078, and the State Key Laboratory of Software Development Environment of China under Grant No. SKLSDE-2017ZX-06.

通讯作者: Jing Jiang     Email: jiangjing@buaa.edu.cn
About author: Li Zhang received her Bachelor's,Master's and Ph.D.degrees in computer science and technology from Beihang University,Beijing,in 1989,1992 and 1996,respectively.She is now a professor in the State Key Laboratory of Software Development Environment of Beihang University,Beijing,where she is leading the expertise area of system and software modeling.
引用本文:   
Li Zhang, Xin-Yue Huang, Jing Jiang, Ya-Kun Hu.CSLabel:一种为移动评论添加标签的方法[J]  Journal of Computer Science and Technology , 2017,V32(6): 1076-1089
Li Zhang, Xin-Yue Huang, Jing Jiang, Ya-Kun Hu.CSLabel:An Approach for Labelling Mobile App Reviews[J]  Journal of Computer Science and Technology, 2017,V32(6): 1076-1089
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