? CSLabel:An Approach for Labelling Mobile App Reviews
Journal of Computer Science and Technology
Quick Search in JCST
 Advanced Search 
      Home | PrePrint | SiteMap | Contact Us | FAQ
Indexed by   SCIE, EI ...
Bimonthly    Since 1986
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 Current Issue | Archive | Adv Search << Previous Articles | Next Articles >>
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

Related Articles
Download: [PDF 325KB]     Export: BibTeX or EndNote (RIS)  
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.
Articles by authors
Keywordsmobile app   user review   classification     
Received 2017-04-20;

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.

Corresponding Authors: 10.1007/s11390-017-1784-1     Email: jiangjing@buaa.edu.cn
About author:
Cite this article:   
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
Copyright 2010 by Journal of Computer Science and Technology