Listen to Your Users – Quality Improvement of Mobile Apps Through Lightweight Feedback Analyses

  • Simon André Scherr
  • Frank ElberzhagerEmail author
  • Selina Meyer
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 338)


Companies developing mobile apps face increasing requirements such as short time to market or high quality. Furthermore, users have more influence on apps, as they can easily provide feedback on the product. Consequently, feedback is a valuable source for product improvement. Ideally, this would be done in an automated way. However, because of the limitations of understanding of natural language by machines, this is not possible in a satisfactory way. We have created a quality assurance process that makes use of feedback by applying lightweight analyses in order to enable product managers to take decisions. Some aspects of our process are the inclusion of emojis to reveal emotions, the detection of trends, as well as the derivation of improvement suggestions. With examples from popular apps, we show the practical application of our process.


Quality assurance Apps Feedback Product improvement Emojis 



The research described in this paper was performed in the project Opti4Apps funded by the German Federal Ministry of Education and Research (BMBF) (grant no. 02K14A182). We thank Sonnhild Namingha for proofreading.


  1. 1.
    Elberzhager, F., Holl, K.: Towards automated capturing and processing of user feedback for optimizing mobile apps. Procedia Comput. Sci. 110, 215–221 (2017)CrossRefGoogle Scholar
  2. 2.
    Scherr, S., Elbertshager, F., Holl, K.: An automated feedback-based approach to support mobile app development. In: Proceedings - 43rd Euromicro Conference on Software Engineering and Advanced Applications, SEAA 2017, Vienna (2017)Google Scholar
  3. 3.
    Hussein, D.M.E.D.M.: A survey on sentiment analysis challenges. J. King Saud Univ. Eng. Sci. (2016)Google Scholar
  4. 4.
    Ribeiro, F., Araújo, M., Gonçalves, M., Benevenuto, F.: SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Sci. 5(1), 1–29 (2016)CrossRefGoogle Scholar
  5. 5.
    Hogenboom, A., Bal, M., Frasincar, F., Bal, D.: Towards cross-language sentiment analysis through universal star ratings. Adv. Intell. Syst. Comput. 172, 69–79 (2013)CrossRefGoogle Scholar
  6. 6.
    Scherr, S., Polst, S., Müller, L., Holl, K., Elberzhager, F.: The perception of emojis for analyzing app feedback. Int. J. Interact. Mobile Technol. [submitted]Google Scholar
  7. 7.
    Hogenboom, A., Bal, D., Frasincar, F., Bal, M., de Jong, F., Kaymak, U.: Exploiting emoticons in sentiment analysis. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing - SAC (2013)Google Scholar
  8. 8.
    Tauch, C., Kanjo, E.: The roles of emojis in mobile phone notifications. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing Adjunct - UbiComp 2016, Heidelberg (2016)Google Scholar
  9. 9.
    Provine, R., Spencer, R., Mandell, D.: Emotional expression online. J. Lang. Soc. Psychol. 26(3), 299–307 (2007)CrossRefGoogle Scholar
  10. 10.
    Unicode: Emoji Keyboard/Display Test Data for UTR #51Google Scholar
  11. 11.
    Ekman, P., Friesen, W.: Constants across cultures in the face and emotion. J. Pers. Soc. Psychol. 17(2), 124–129 (1971)CrossRefGoogle Scholar
  12. 12.
    Lazarus, R.: Emotion and Adaption (1991)Google Scholar
  13. 13.
    Plutchik, R.: A general psychoevolutionary theory of emotions. In: Theorie of Emotions, pp. 3–33 (1980)Google Scholar
  14. 14.
    Brand Resource Center: Reactions. Accessed 2016
  15. 15.
    IBM Watson: Natural Language Understanding. Natural language processing for advanced text analysis.
  16. 16.
    Hu, N., Pavlou, P., Zhang, J.: Can online reviews reveal a product’s true quality? In: Proceedings of the 7th ACM conference on Electronic commerce - EC 2006, Michigan (2006)Google Scholar
  17. 17.
    Shepherd, M.: How To Use Snapchat’s New Snap Map & Become A Social Media Master On The Go. Accessed 21 June 2017
  18. 18.
    Snapchat Support: Snapchat Support on Twitter. Accessed 27 July 2017
  19. 19.
    Waton, C.: Snapchat update: more than 800,000 angry users sign petition to change redesign. Accessed 13 Feb 2018
  20. 20.
    Carman, A.: Instagram bug makes user accounts appear to be deleted. Accessed 06 July 2018
  21. 21.
    Crook, J.: Instagram is down for some users. Accessed 26 July 2017
  22. 22.
    Wagstaff, K.: Tinder crashed and now love is dead. Accessed 02 Sept 2016
  23. 23.
    Fingas, J.: Tinder suffers sign-in problems following Facebook’s privacy changes. Accessed 04 Apr 2018
  24. 24.
    Instagram Engineering: Emojineering Part 1: Machine Learning for Emoji Trends. Accessed 2018
  25. 25.
    Ljubešić, N., Fišer, D.: A global analysis of emoji usage. In: Proceedings of the 10th Web as Corpus Workshop, Berlin (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Simon André Scherr
    • 1
  • Frank Elberzhager
    • 1
    Email author
  • Selina Meyer
    • 1
  1. 1.Fraunhofer IESEKaiserslauternGermany

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