Abstract
Detecting users’ significant intentions (e.g., new features wanted) timely and precisely is crucial for developers to update and maintain their apps in the competitive mobile app market. Sentiment and preference mining from crowd reviews provide an opportunity to proactively collect app users’ intentions, e.g., bug fixing and feature refinement. However, users’ sentiment and preferences often change over time due to either internal factors (e.g., new bugs) or external factors (e.g., new competitors). This makes it difficult for app developers to grasp users’ sentiment and preferences in time. In this paper, we propose a novel and automated framework named DSISP for detecting users’ significant intentions effectively via sentiment-preference correlation analysis. DSISP first employs sentiment analysis and NLP (Natural Language Processing) techniques to obtain sentence-level sentiment scores and fine-grained user preference features from app reviews in different time slices. Then, the temporal correlation between user sentiment and preferences is analyzed, which can be used to monitor users’ sentiment tendency and preferences in time. Finally, DSISP identifies users’ dramatically-changing sentiment (e.g., sentiment valley) to detect users’ significant intentions. We evaluate the feasibility and performance of DSISP by using real-world app reviews and app official changelogs. The experimental results show that DSISP can detect users’ significant intentions effectively and efficiently, with a precision of 0.962 on average. It can help app developers keep track of how their users’ intentions evolve over time so that they can improve their apps correspondingly and continuously.
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References
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, pp. 281–290 (2015)
Dąbrowski, J., Letier, E., Perini, A., Susi, A.: Finding and analyzing app reviews related to specific features: a research preview. In: Knauss, E., Goedicke, M. (eds.) REFSQ 2019. LNCS, vol. 11412, pp. 183–189. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15538-4_14
Di Sorbo, A., et al.: What would users change in my app? Summarizing app reviews for recommending software changes, pp. 499–510 (2016)
Palomba, F., et al.: Crowdsourcing user reviews to support the evolution of mobile apps. J. Syst. Softw. 137, 143–162 (2018)
Liu, Y., Liu, L., Liu, H., Wang, X.: Analyzing reviews guided by app descriptions for the software development and evolution. J. Softw. Evol. Process. 30(12) (2018)
Messaoud, M.B., Jenhani, I., Jemaa, N.B., Mkaouer, M.W.: A multi-label active learning approach for mobile app user review classification. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds.) KSEM 2019. LNCS (LNAI), vol. 11775, pp. 805–816. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-29551-6_71
Villarroel, L., Bavota, G., Russo, B., Oliveto, R., Di Penta, M.: Release planning of mobile apps based on user reviews, pp. 14–24 (2016)
Palomba, F., et al.: Recommending and localizing change requests for mobile apps based on user reviews, pp. 106–117 (2017)
Zhou, Y., Su, Y., Chen, T., Huang, Z., Gall, H.C., Panichella, S.: User review-based change file localization for mobile applications. IEEE Trans. Softw. Eng., 1 (2020)
Di Sorbo, A., Panichella, S., Alexandru, C.V., Visaggio, C.A., Canfora, G.: SURF: summarizer of user reviews feedback, pp. 55–58 (2017)
Huang, Q., Xia, X., Lo, D., Murphy, G.C. : Automating intention mining. IEEE Trans. Softw. Eng., 1 (2018)
King, I.: Online app review analysis for identifying emerging issues. In: The 40th International Conference (2018)
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. J. Assoc. Inf. Sci. Technol. 61, 2544–2558 (2010)
Cohen, K.B., Dolbey, A.: Foundations of statistical natural language processing. Language 78(3), 599 (2002)
Zhao, W.X., et al.: Comparing Twitter and traditional media using topic models. In: Clough, P., et al. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 338–349. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20161-5_34
Pagano, D., Maalej, W.: User feedback in the AppStore: an empirical study, pp. 125–134 (2013)
Harman, M., Jia, Y., Zhang, Y.: App store mining and analysis: MSR for app stores, pp. 108–111 (2012)
Chen, N., Lin, J., Hoi, S.C.H., Xiao, X., Zhang, B.: AR-miner: mining informative reviews for developers from mobile app marketplace, pp. 767–778 (2014)
Guzman, E., Elhalaby, M., Bruegge, B.: Ensemble methods for app review classification: an approach for software evolution (N), pp. 771–776 (2015)
Vu, P.M., Pham, H.V., Nguyen, T.T.: Mining user opinions in mobile app reviews: a keyword-based approach (2015). arXiv: Information
Kucuktunc, O., Cambazoglu, B.B., Weber, I., Ferhatosmanoglu, H.: A large-scale sentiment analysis for yahoo! Answers, pp. 633–642 (2012)
Ma, Y., Chen, G., Wei, Q.: Finding users preferences from large-scale online reviews for personalized recommendation. Electron. Commer. Res. 17(1), 3–29 (2016). https://doi.org/10.1007/s10660-016-9240-9
Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python: Analyzing Text with The Natural Language Toolkit. O’Reilly Media, Inc., Sebastopol (2009)
Smadja, F.: Retrieving collocations from text: Xtract. Comput. Linguist. 19(1), 143–177 (1993)
Acknowledgements
This work is supported by the National Key R&D Program of China grant No.2017YFB1401201, the National Natural Science Key Foundation of China grant No.61832014 and the Shenzhen Science and Technology Foundation (JCYJ20170816093943197).
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Xiao, J., Chen, S., He, Q., Wu, H., Feng, Z., Xue, X. (2020). Detecting User Significant Intention via Sentiment-Preference Correlation Analysis for Continuous App Improvement. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds) Service-Oriented Computing. ICSOC 2020. Lecture Notes in Computer Science(), vol 12571. Springer, Cham. https://doi.org/10.1007/978-3-030-65310-1_27
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