ALDA: An Aggregated LDA for Polarity Enhanced Aspect Identification Technique in Mobile App Domain

  • Binil Kuriachan
  • Nargis Pervin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10844)


With the increased popularity of the smart mobile devices, mobile applications (a.k.a apps) have become essential. While the app developers face an extensive challenge to improve user satisfaction by exploiting the valuable feedbacks, the app users are overloaded with way too many apps. Extracting the valuable features from apps and mining the associated sentiments is of utmost importance for the app developers. Similarly, from the user perspective, the key preferences should be identified. This work deals with profiling users and apps using a novel LDA based aspect identification technique. Polarity aggregation technique is used to tag the weak features of the apps the developers should concentrate on. The proposed technique has been experimented on an Android review dataset to validate the efficacy compared to state-of-the-art algorithms. Experimental findings suggest superiority and applicability of our model in practical scenarios.


Latent Dirichlet Allocation App profiling Mobile apps 


  1. 1.
    Ruiz, I.M., Nagappan, M., Adams, B., Berger, T., Dienst, S., Hassan, A.: An examination of the current rating system used in mobile app stores. IEEE Softw. 33(6), 86–92 (2017)CrossRefGoogle Scholar
  2. 2.
    Von Alan, R.H., March, S.T., Park, J., Ram, S.: Design science in information systems research. MIS Q. 28(1), 75–105 (2004)CrossRefGoogle Scholar
  3. 3.
    Gregor, S., Jones, D.: The anatomy of a design theory. J. Assoc. Inf. Syst. 8(5), 312 (2007)Google Scholar
  4. 4.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)zbMATHGoogle Scholar
  5. 5.
    Guzman, E., Maalej, W.: How do users like this feature? A fine grained sentiment analysis of app reviews. In: 2014 IEEE 22nd International Requirements Engineering Conference (RE), pp. 153–162. IEEE (2014)Google Scholar
  6. 6.
    Fu, B., Lin, J., Li, L., Faloutsos, C., Hong, J., Sadeh, N.: Why people hate your app: making sense of user feedback in a mobile app store. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1276–1284. ACM (2013)Google Scholar
  7. 7.
    Panichella, S., Di Sorbo, A., Guzman, E., Visaggio, C.A., Canfora, G., Gall, H.C.: How can I improve my app? Classifying user reviews for software maintenance and evolution. In: 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME), pp. 281–290. IEEE (2015)Google Scholar
  8. 8.
    Oh, J., Kim, D., Lee, U., Lee, J.G., Song, J.: Facilitating developer-user interactions with mobile app review digests. In: CHI 2013 Extended Abstracts on Human Factors in Computing Systems, pp. 1809–1814. ACM (2013)Google Scholar
  9. 9.
    Chen, N., Lin, J., Hoi, S.C., Xian, X., Zhang, B.: AR-miner: mining informative reviews for developers from mobile app marketplace. In: Proceesings of the 36th International Conference on Software Engineering, pp. 767–778. ACM (2014)Google Scholar
  10. 10.
    Yan, B., Chen, G.: AppJoy: personalized mobile application discovery. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, pp. 113–126. ACM (2011)Google Scholar
  11. 11.
    Ikeda, K., Hattori, G., Ono, C., Asoh, H., Higashino, T.: Twitter user profiling based on text and community mining for market analysis. Knowl.-Based Syst. 51, 35–47 (2013)CrossRefGoogle Scholar
  12. 12.
    Iacob, C., Harrison, R.: Retrieving and analyzing mobile apps feature requests from online reviews. In: Proceedings of the 10th Working Conference on Mining Software Repositories, pp. 41–44. IEEE Press (2013)Google Scholar
  13. 13.
    Galvis Carreño, L.V., Winbladh, K.: Analysis of user comments: an approach for software requirements evolution. In: Proceedings of the 2013 International Conference on Software Engineering, pp. 582–591. IEEE Press (2013)Google Scholar
  14. 14.
    Vu, P.M., Nguyen, T.T., Pham, H.V., Nguyen, T.T.: Mining user opinions in mobile app reviews: a keyword-based approach (t). In: 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp. 749–759. IEEE (2015)Google Scholar
  15. 15.
    Liu, Y., Li, Y., Guo, Y., Zhang, M.: Stratify mobile app reviews: E-LDA model based on hot “entity” discovery. In: 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 581–588. IEEE (2016)Google Scholar
  16. 16.
    Bao, Y., Datta, A.: Simultaneously discovering and quantifying risk types from textual risk disclosures. Manag. Sci. 60(6), 1371–1391 (2014)CrossRefGoogle Scholar
  17. 17.
    Srividhya, V., Anitha, R.: Evaluating preprocessing techniques in text categorization. Int. J. Comput. Sci. Appl. 47(11), 49–51 (2010)Google Scholar
  18. 18.
    Németh, L.: Hunspell. Dostupno na: Accessed 01 Oct 2013 (2010)
  19. 19.
    Bird, S., Loper, E.: NLTK: the natural language toolkit. In: Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions, p. 31. Association for Computational Linguistics (2004)Google Scholar
  20. 20.
    Feldman, R., Sanger, J.: The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data. Cambridge University Press, Cambridge (2007)Google Scholar
  21. 21.
    Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  22. 22.
    Porter, M.F.: Snowball: a language for stemming algorithms (2001)Google Scholar
  23. 23.
    Ramos, J., et al.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, vol. 242, pp. 133–142 (2003)Google Scholar
  24. 24.
    Loria, S., Keen, P., Honnibal, M., Yankovsky, R., Karesh, D., Dempsey, E., et al.: TextBlob: simplified text processing. Secondary TextBlob Simplified Text Processing (2014)Google Scholar
  25. 25.
    Chen, N., Hoi, S.C., Li, S., Xiao, X.: SimApp: a framework for detecting similar mobile applications by online kernel learning. In: Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, pp. 305–314. ACM (2015)Google Scholar
  26. 26.
    Brody, S., Elhadad, N.: An unsupervised aspect-sentiment model for online reviews. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 804–812. Association for Computational Linguistics (2010)Google Scholar
  27. 27.
    Azzopardi, L., Girolami, M., van Risjbergen, K.: Investigating the relationship between language model perplexity and IR precision-recall measures. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 369–370. ACM (2003)Google Scholar
  28. 28.
    Chang, Y.L., Chien, J.T.: Latent Dirichlet learning for document summarization. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2009, pp. 1689–1692. IEEE (2009)Google Scholar
  29. 29.
    Grimmer, J., Stewart, B.M.: Text as data: the promise and pitfalls of automatic content analysis methods for political texts. Polit. Anal. 21(3), 267–297 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Verizon Data ServicesChennaiIndia
  2. 2.Indian Institute of Technology MadrasChennaiIndia

Personalised recommendations