TY - GEN
T1 - Mining and analyzing user feedback from app reviews
T2 - 4th IEEE SmartWorld, 15th IEEE International Conference on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
AU - Tong, Guo
AU - Guo, Bin
AU - Yi, Ouyang
AU - Zhiwen, Yu
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/4
Y1 - 2018/12/4
N2 - Mobile application distribution platforms such as Google Play and Apple Store allow users to submit feedback in form of ratings and reviews towards downloaded apps, which actually serve as the communication channel between app users and developers. User reviews of mobile apps often contain complaints or suggestions that are valuable for developers to improve user experience and satisfaction. However, the manual analysis of a large amount of user reviews is a tedious and time consuming task. In this paper, we present CrowdApp, a novel computational framework that reexamines the impact of user reviews on mobile apps (app downloads, etc). Our approach explores multiple app aspects from user reviews, and further analyses the effects of different user feedback towards app downloads using the econometric method. Our econometric analysis reveals that user feedback has impact to app downloads. This work is an exploratory study that integrates econometric methodologies and text mining techniques towards a more complete analysis of the information captured from app reviews, and the results help app developers address the most complained app problems at an early stage.
AB - Mobile application distribution platforms such as Google Play and Apple Store allow users to submit feedback in form of ratings and reviews towards downloaded apps, which actually serve as the communication channel between app users and developers. User reviews of mobile apps often contain complaints or suggestions that are valuable for developers to improve user experience and satisfaction. However, the manual analysis of a large amount of user reviews is a tedious and time consuming task. In this paper, we present CrowdApp, a novel computational framework that reexamines the impact of user reviews on mobile apps (app downloads, etc). Our approach explores multiple app aspects from user reviews, and further analyses the effects of different user feedback towards app downloads using the econometric method. Our econometric analysis reveals that user feedback has impact to app downloads. This work is an exploratory study that integrates econometric methodologies and text mining techniques towards a more complete analysis of the information captured from app reviews, and the results help app developers address the most complained app problems at an early stage.
KW - App review
KW - Econometric analysis
KW - Text mining
UR - https://www.scopus.com/pages/publications/85060282463
U2 - 10.1109/SmartWorld.2018.00155
DO - 10.1109/SmartWorld.2018.00155
M3 - 会议稿件
AN - SCOPUS:85060282463
T3 - Proceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
SP - 841
EP - 848
BT - Proceedings - 2018 IEEE SmartWorld, Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
A2 - Loulergue, Frederic
A2 - Wang, Guojun
A2 - Bhuiyan, Md Zakirul Alam
A2 - Ma, Xiaoxing
A2 - Li, Peng
A2 - Roveri, Manuel
A2 - Han, Qi
A2 - Chen, Lei
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 October 2018 through 11 October 2018
ER -