A Pilot Study of Mining the Differences in Patterns of Customer Review Text Between US and China AppStore

  • Lisha Li
  • Liang Ma
  • Pei-Luen Patrick Rau
  • Qin Gao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10281)

Abstract

With the fast growing of AppStore market and the developing of techniques in opinion mining, this study was aimed to investigate the sentiment and opinions of customer reviews in both China AppStore and US AppStore, and identify the difference of key term and patterns of apps reviews among different genres and between China AppStore and US AppStore. Results showed that there were small differences in using adjective words used or expressing key opinions. The result of this study could help publisher to extract useful customer feedback from customers reviews when publishing apps in foreign countries.

Keywords

Cross-cultural product and service design Cultural differences Review mining 

References

  1. 1.
    Apple: Apple - choose your country or region. https://www.apple.com/choose-your-country
  2. 2.
    Archak, N., Ghose, A., Ipeirotis, P.G.: Deriving the pricing power of product features by mining consumer reviews. Manage. Sci. 57(8), 1485–1509 (2011)CrossRefMATHGoogle Scholar
  3. 3.
    Bird, S., Klein, E., Loper, E.: Natural language processing with Python: analyzing text with the natural language toolkit. O’Reilly Media Inc., Sebastopol (2009)MATHGoogle Scholar
  4. 4.
    Chen, Y., Fay, S., Wang, Q.: Marketing implications of online consumer product reviews. Bus. Week 7150, 1–36 (2003)Google Scholar
  5. 5.
    Dellarocas, C.: The digitization of word of mouth: promise and challenges of online feedback mechanisms. Manage. Sci. 49(10), 1407–1424 (2003)CrossRefGoogle Scholar
  6. 6.
    Duan, W., Gu, B., Whinston, A.B.: Do online reviews matter?-An empirical investigation of panel data. Decis. Support Syst. 45(4), 1007–1016 (2008)CrossRefGoogle Scholar
  7. 7.
    fxsjy: Jie ba - Chinese text segmentation. https://github.com/fxsjy/jieba
  8. 8.
    Gamon, M., Aue, A., Corston-Oliver, S., Ringger, E.: Pulse: mining customer opinions from free text. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds.) IDA 2005. LNCS, vol. 3646, pp. 121–132. Springer, Heidelberg (2005). doi:10.1007/11552253_12 CrossRefGoogle Scholar
  9. 9.
    Kim, J., Park, Y., Kim, C., Lee, H.: Mobile application service networks: Apple’s app store. Serv. Bus. 8(1), 1–27 (2014)CrossRefGoogle Scholar
  10. 10.
    McFadden, D.L., Train, K.E.: Consumers’ evaluation of new products: learning from self and others. J. Polit. Econ. 104, 683–703 (1996)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Simmons, L.L., Mukhopadhyay, S., Conlon, S., Yang, J.: A computer aided content analysis of online reviews. J. Comput. Inf. Syst. 52(1), 43–55 (2011)Google Scholar
  13. 13.
    Statista: cumulative number of apps downloaded from the apple app store from July 2008 to September 2016 (in billions). http://www.statista.com/statistics/263794/number-of-downloads-from-the-apple-app-store
  14. 14.
    Zhu, M., Fang, X.: A lexical approach to study computer games and game play experience via online reviews. Int. J. Hum.-Comput. Interact. 31(6), 413–426 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Lisha Li
    • 1
  • Liang Ma
    • 1
  • Pei-Luen Patrick Rau
    • 1
  • Qin Gao
    • 1
  1. 1.Department of Industrial EngineeringTsinghua UniversityBeijingPeople’s Republic of China

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