Method of Relevance Judgment for App Software’s User Reviews

  • Qixin Xiang
  • Ying JiangEmail author
  • Meng Ran
  • Jiaman Ding
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 728)


In order to judge whether the user reviews are relevant to App software, this paper proposed a method to judge the relevance of user reviews based on Naive Bayesian text classification and term frequency. Firstly, the keywords sets of App software’s user reviews are extracted. Then, the keywords sets are optimized. Finally, the relevance score of the user reviews are calculated, and whether the user reviews are relevant is judged. Through the experiment, this method is proved that can judge the relevance of App software’s user reviews effectively.


App software User reviews Relevance judgment Naive Bayesian text classification Term frequency 



This research is sponsored by the National Science Foundation of China Nos. 61462049, 60703116, and 61063006.


  1. 1.
    Lin, Y., Wang, X., Zhu, T., Zhou, A.: Survey on quality evaluation and control of online reviews. J. Softw. 25(3), 506–527 (2014)Google Scholar
  2. 2.
    Hu, Z., Zheng, X.: Product recommendation algorithm based on user’s reviews mining. J. Zhejiang Univ. (Eng. Sci.) 47(8), 1475–1485 (2013)MathSciNetGoogle Scholar
  3. 3.
    Jiang, W., Zhang, L.: Analyzing helpfulness of online reviews for user requirements elicitation. Chin. J. Comput. 36(1), 119–131 (2013)CrossRefGoogle Scholar
  4. 4.
    Li, Y., Fu, H.: Fake comments recognition based on social network graph model. J. Comput. Appl. 34(S2), 151–153, 158 (2014)Google Scholar
  5. 5.
    Pagano, D., Maalej, W.: User feedback in the appstore: an empirical study. In: 2013 21st IEEE International on Requirements Engineering Conference (RE), pp. 125–134. IEEE (2015)Google Scholar
  6. 6.
    Leopairote, W., Surarerks, A., Prompoon, N.: Software quality in use characteristic mining from customer reviews. In: 2012 Second International Conference on Digital Information and Communication Technology and its Applications (DICTAP), pp. 434–439. IEEE (2012)Google Scholar
  7. 7.
    Harman, M., Jia, Y., Zhang, Y.: App store mining and analysis: MSR for app stores. In: Proceedings of the 9th IEEE Working Conference on Mining Software Repositories, pp. 108–111. IEEE (2012)Google Scholar
  8. 8.
    AlQuwayfili, N., AlRomi, N., AlZakari, N.: Towards classifying applications in mobile phone markets: the case of religious apps. In: 2013 International Conference on Current Trends in Information Technology (CTIT), pp. 177–180. IEEE (2013)Google Scholar
  9. 9.
    Gao, C., Xu, H.: AR-tracker: track the dynamics of mobile apps via user review mining. In: 2015 IEEE Symposium on Service-Oriented System Engineering (SOSE), pp. 284–290. IEEE (2015)Google Scholar
  10. 10.
    Han, P., Wang, D., Liu, Y.: Influence of part-of-speech on Chinese and English document clustering. J. Chin. Inf. Process. 27(2), 65–73 (2013)Google Scholar
  11. 11.
    Zhang, L., Hua, K., Wang, H.: Sentiment analysis on reviews of mobile users. Procedia Comput. Sci. 34, 458–465 (2014)CrossRefGoogle Scholar
  12. 12.
    Wang, J.: Study of the application of text classification techniques on Weibo. Guangxi University (2015)Google Scholar
  13. 13.
    Di, P., Duan, L.: New Naive Bayes text classification algorithm. J. Data Acquis. Process. 29(1), 71–75 (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Qixin Xiang
    • 1
    • 2
  • Ying Jiang
    • 1
    • 2
    Email author
  • Meng Ran
    • 1
    • 2
  • Jiaman Ding
    • 1
    • 2
  1. 1.Yunnan Key Lab of Computer Technology ApplicationKunmingChina
  2. 2.Faculty of Information Engineering and AutomationKunming University of Science and TechnologyKunmingChina

Personalised recommendations