Mining and Comparing User Reviews across Similar Mobile Apps

被引:5
|
作者
Su, Yanqi [1 ]
Wang, Yongchao [1 ]
Yang, Wenhua [1 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Coll Comp Sci & Technol, Nanjing, Peoples R China
关键词
User review; Mobile app; Release Planning;
D O I
10.1109/MSN48538.2019.00070
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
With the rapid development of the market for mobile apps, there are a number of apps with similar functions. To gain an advantage in such a competitive environment, developers need to understand not only the strengths and weaknesses of their app but also competitive apps. User reviews contain valuable information for comparing similar apps from user preference. In this paper, we propose UISAT (User-review mining via topic Identification, Sentiment Analysis and Topic matching across apps), an automated approach to compare user reviews from similar apps with the goal of mining user feedback from competitive apps by (i) extracting the hidden topics from large volumes of user reviews using topic modeling, (ii) combining a rule-based model, user rating and user-helpful for sentiment analysis of topics and (iii) matching relevant topics across apps. Empirical studies demonstrate that UISAT is effective and promising for developers to build and maintain a more competitive app.
引用
收藏
页码:338 / 342
页数:5
相关论文
共 50 条
  • [1] User behavior pattern mining and reuse across similar Android apps
    Mao, Qun
    Wang, Weiwei
    You, Feng
    Zhao, Ruilian
    Li, Zheng
    [J]. JOURNAL OF SYSTEMS AND SOFTWARE, 2022, 183
  • [2] Release Planning of Mobile Apps Based on User Reviews
    Villarroel, Lorenzo
    Bavota, Gabriele
    Russo, Barbara
    Oliveto, Rocco
    Di Penta, Massimiliano
    [J]. 2016 IEEE/ACM 38TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), 2016, : 14 - 24
  • [3] Localizing Function Errors in Mobile Apps with User Reviews
    Yu, Le
    Chen, Jiachi
    Zhou, Hao
    Luo, Xiapu
    Liu, Kang
    [J]. 2018 48TH ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS (DSN), 2018, : 418 - 429
  • [4] Crowdsourcing user reviews to support the evolution of mobile apps
    Palomba, Fabio
    Linares-Vasquez, Mario
    Bavota, Gabriele
    Oliveto, Rocco
    Di Penta, Massimiliano
    Poshyvanyk, Denys
    De Lucia, Andrea
    [J]. JOURNAL OF SYSTEMS AND SOFTWARE, 2018, 137 : 143 - 162
  • [5] Mining User Reviews From Hypertension Management Mobile Health Apps to Explore Factors Influencing User Satisfaction and Their Asymmetry: Comparative Study
    He, Yunfan
    Zhu, Wei
    Wang, Tong
    Chen, Han
    Xin, Junyi
    Liu, Yongcheng
    Lei, Jianbo
    Liang, Jun
    [J]. JMIR MHEALTH AND UHEALTH, 2024, 12
  • [6] Improving the User Experience on Mobile Apps Through Data Mining
    Auad, Tassio de O. S.
    Mendes, Luiz Felipe C.
    Stroele, Victor
    David, Jose Maria N.
    [J]. 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD), 2016, : 158 - 163
  • [7] What People Like in Mobile Finance Apps - An Analysis of User Reviews
    Huebner, Johannes
    Frey, Remo Manuel
    Ammendola, Christian
    Fleisch, Elgar
    Ilic, Alexander
    [J]. 17TH INTERNATIONAL CONFERENCE ON MOBILE AND UBIQUITOUS MULTIMEDIA (MUM 2018), 2018, : 293 - 304
  • [8] User Reviews of Top Mobile Apps in Apple and Google App Stores
    Mcilroy, Stuart
    Shang, Weiyi
    Ali, Nasir
    Hassan, Ahmed E.
    [J]. COMMUNICATIONS OF THE ACM, 2017, 60 (11) : 62 - 67
  • [9] Recommending and Localizing Change Requests for Mobile Apps based on User Reviews
    Palomba, Fabio
    Salza, Pasquale
    Ciurumelea, Adelina
    Panichella, Sebastiano
    Gall, Harald
    Ferrucci, Filomena
    De Lucia, Andrea
    [J]. 2017 IEEE/ACM 39TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), 2017, : 106 - 117
  • [10] Towards Automatically Localizing Function Errors in Mobile Apps With User Reviews
    Yu, Le
    Wang, Haoyu
    Luo, Xiapu
    Zhang, Tao
    Liu, Kang
    Chen, Jiachi
    Zhou, Hao
    Tang, Yutian
    Xiao, Xusheng
    [J]. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 2023, 49 (04) : 1464 - 1486