Abstract
Users search for applications on the online application store by inputting functional terms, such as “automated assignment solver”, “English translator” and “free VPN”. In response, the application store recommends a list of applications whose titles and descriptions closely match the user’s search terms. Acknowledging this, application developers incorporate trending and frequently searched functional terms into their application titles and descriptions to make them compelling and to enhance the visibility of their products in user searches, thereby increasing the likelihood of application success. However, traditional literature analyzing mobile application titles and descriptions to determine their impact on application success is scarce and may also lack data-analytical approaches. Moreover, the definition of application success provided by existing literature may be flawed, as it solely relies on higher downloads or positive numeric ratings, neglecting the crucial factor of time. This research proposes a Machine Learning-inspired framework to extract functional (aspects) themes from titles and descriptions of Google Play Education applications, influencing their success. It also formulates an enhanced definition of application success that considers downloads and ratings over a specific time period, and also integrates the user sentiment when defining application success. According to the findings of this research, themes of Math and Homework Support, Learning and Practice, Live Assistance and Tutoring, and Instant Solutions and Tools are highly correlated with success within the Education category of the Google Play store. Developers can enhance the visibility and appeal of their applications in user search results by incorporating these themes into their application titles and descriptions, ultimately leading to higher likelihood of success.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles and news from researchers in related subjects, suggested using machine learning.Data availability
The dataset of Google-play application investigated in this research work is not publicly available. The dataset is scraped from the Google-play store and stored in personal repository of authors. However, it is available from the first or corresponding authors on request through email. The scraped content of the dataset is solely derived from publicly available information on the Google Play Store. It is not utilized or intended for commercial purposes.
Notes
References
Ali, M., Joorabchi, M.E., Mesbah, A.: Same app, different app stores: a comparative study. In: 2017 IEEE/ACM 4th International Conference on Mobile Software Engineering and Systems (MOBILESoft), pp. 79–90. IEEE. https://doi.org/10.1109/MOBILESoft.2017.3 (2017)
Aljrees, T., Umer, M., Saidani, O., Almuqren, L., Ishaq, A., Alsubai, S., Ashraf, I., et al.: Contradiction in text review and apps rating: prediction using textual features and transfer learning. PeerJ Comput. Sci. 10, 1722 (2024). https://doi.org/10.7717/peerj-cs.1722
AmanUllah, H., Fatima, M., Muneer, U., Ilyas, S., Rehman, R.A., Afzal, I.: Causal impact analysis on android market. Int. J. Adv. Comput. Sci. Appl. (2019). https://doi.org/10.14569/IJACSA.2019.0100644
Baihaqi, K.A., Sediyono, E., Dewi, C., Widiasari, I.R., Fauzi, A.: Classification of mobile application user ratings based on data from google play store. In: E3S Web of Conferences, vol. 500, pp. 01017. EDP Sciences. https://doi.org/10.1051/e3sconf/202450001017 (2024)
Bilal, A., Mirza, H.T., Hussain, I.: Identifying significant textual features in titles of google play store applications and their influence on user review rating. Knowl. Inf. Syst. 65(3), 1159–1178 (2023). https://doi.org/10.1007/s10115-022-01799-x
Bilal, A., Mirza, H.T., Hussain, I., Ahmad, A.: Investigating influence of google-play application titles on success. Big Data Res. (2024). https://doi.org/10.1016/j.bdr.2024.100443
Biviji, R., Vest, J.R., Dixon, B.E., Cullen, T., Harle, C.A., et al.: Factors related to user ratings and user downloads of mobile apps for maternal and infant health: cross-sectional study. JMIR Mhealth Uhealth 8(1), 15663 (2020). https://doi.org/10.2196/15663
Cao, X., Watanabe, M., Ono, K.: How character-centric game icon design affects the perception of gameplay. Appl. Sci. 11(21), 9911 (2021)
Chembakottu, B., Li, H., Khomh, F.: A large-scale exploratory study of android sports apps in the google play store. Inf. Softw. Technol. 164, 107321 (2023). https://doi.org/10.1016/j.infsof.2023.107321
Finkelstein, A., Harman, M., Jia, Y., Martin, W., Sarro, F., Zhang, Y.: Investigating the relationship between price, rating, and popularity in the blackberry world app store. Inf. Softw. Technol. 87, 119–139 (2017). https://doi.org/10.1016/j.infsof.2017.03.002
Hou, K.-C., Ho, C.-H., et al.: A preliminary study on aesthetic of apps icon design. In: IASDR 2013 5th International Congress of International Association of Societies of Design Research, pp. 1–12. https://api.semanticscholar.org/CorpusID:61676296 (2013)
Hu, H., Wang, S., Bezemer, C.-P., Hassan, A.E.: Studying the consistency of star ratings and reviews of popular free hybrid android and IoS apps. Empir. Softw. Eng. 24, 7–32 (2019). https://doi.org/10.1007/s10664-018-9617-6
Janse Van Rensburg, W., Thomson, K.-L., Futcher, L.: Factors influencing smartphone application downloads. In: IFIP World Conference on Information Security Education, pp. 81–92. Springer. https://doi.org/10.1007/978-3-319-99734-6_7 (2018)
Karagkiozidou, M., Ziakis, C., Vlachopoulou, M., Kyrkoudis, T.: App store optimization factors for effective mobile app ranking. In: Strategic Innovative Marketing and Tourism: 7th ICSIMAT, Athenian Riviera, Greece, 2018, pp. 479–486. Springer. https://doi.org/10.1007/978-3-030-12453-3 (2019)
Lee, G., Raghu, T.S.: Determinants of mobile apps’ success: evidence from the app store market. J. Manag. Inf. Syst. 31(2), 133–170 (2014). https://doi.org/10.2753/MIS0742-1222310206
Lin, C.-H., Chen, M.: The icon matters: how design instability affects download intention of mobile apps under prevention and promotion motivations. Electron. Commer. Res. 19(1), 211–229 (2019). https://doi.org/10.1007/s10660-018-9297-8
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), 2112 (2018). https://doi.org/10.1002/smr.2112
Liu, W., Cao, Y., Proctor, R.W.: How do app icon color and border shape influence visual search efficiency and user experience? Evidence from an eye-tracking study. Int. J. Ind. Ergon. 84, 103160 (2021). https://doi.org/10.1016/j.ergon.2021.103160
Luiz, W., Viegas, F., Alencar, R., Mourão, F., Salles, T., Carvalho, D., Gonçalves, M.A., Rocha, L.: A feature-oriented sentiment rating for mobile app reviews. In: Proceedings of the 2018 World Wide Web Conference, pp. 1909–1918. https://doi.org/10.1145/3178876.3186168 (2018)
Mahmood, A.: Identifying the influence of various factor of apps on google play apps ratings. J. Data Inf. Manag. 2, 15–23 (2020). https://doi.org/10.1007/s42488-019-00015-w
Mou, J., Zhu, W., Benyoucef, M.: Impact of product description and involvement on purchase intention in cross-border e-commerce. Ind. Manag. Data Syst. 120(3), 567–586 (2020). https://doi.org/10.1108/IMDS-05-2019-0280
Oyebode, O., Alqahtani, F., Orji, R.: Using machine learning and thematic analysis methods to evaluate mental health apps based on user reviews. IEEE Access 8, 111141–111158 (2020). https://doi.org/10.1109/ACCESS.2020.3002176
Pal Kapoor, A., Vij, M.: How to boost your app store rating? An empirical assessment of ratings for mobile banking apps. J. Theor. Appl. Electron. Commer. Res. 15(1), 99–115 (2020). https://doi.org/10.4067/S0718-18762020000100108
Picoto, W.N., Duarte, R., Pinto, I.: Uncovering top-ranking factors for mobile apps through a multimethod approach. J. Bus. Res. 101, 668–674 (2019). https://doi.org/10.1016/j.jbusres.2019.01.038
Ruiz, I.J.M., Nagappan, M., Adams, B., Berger, T., Dienst, S., Hassan, A.E.: Examining the rating system used in mobile-app stores. IEEE Softw. 33(6), 86–92 (2015). https://doi.org/10.1109/MS.2015.56
Sadiq, S., Umer, M., Ullah, S., Mirjalili, S., Rupapara, V., Nappi, M.: Discrepancy detection between actual user reviews and numeric ratings of google app store using deep learning. Expert Syst. Appl. 181, 115111 (2021). https://doi.org/10.1016/j.eswa.2021.115111
Sarro, F., Harman, M., Jia, Y., Zhang, Y.: Customer rating reactions can be predicted purely using app features. In: 2018 IEEE 26th International Requirements Engineering Conference (RE), pp. 76–87. IEEE. https://doi.org/10.1109/RE.2018.00018 (2018)
Strzelecki, A.: Application of Developers’ and Users’ Dependent Factors in App Store Optimization. University of Economics in Katowice, Katowice (2020). https://doi.org/10.3991/ijim.v14i13.14143
Taye, M.M.: Understanding of machine learning with deep learning: architectures, workflow, applications and future directions. Computers 12(5), 91 (2023). https://doi.org/10.3390/computers12050091
Tian, Y., Nagappan, M., Lo, D., Hassan, A.E.: What are the characteristics of high-rated apps? A case study on free android applications. In: 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME), pp. 301–310. IEEE. https://doi.org/10.1109/ICSM.2015.7332476 (2015)
Umer, M., Ashraf, I., Mehmood, A., Ullah, S., Choi, G.S.: Predicting numeric ratings for google apps using text features and ensemble learning. ETRI J. 43(1), 95–108 (2021). https://doi.org/10.4218/etrij.2019-0443
Yang, C., Wu, L., Yu, C., Zhou, Y.: A phrase-level user requests mining approach in mobile application reviews: concept, framework, and operation. Information 12(5), 177 (2021). https://doi.org/10.3390/info12050177
Zhang, Y., Guo, B., Liu, J., Guo, T., Ouyang, Y., Yu, Z.: Which app is going to die? A framework for app survival prediction with multitask learning. IEEE Trans. Mob. Comput. 21(2), 728–739 (2020). https://doi.org/10.1109/TMC.2020.3012767
Zhi, Y., Li, T., Yang, Z.: Extracting features from app descriptions based on POS and dependency. In: Proceedings of the 36th Annual ACM Symposium on Applied Computing, pp. 1354–1358. https://doi.org/10.1145/3412841.3442120 (2021)
Funding
This research received no external funding.
Author information
Authors and Affiliations
Contributions
Ahmad Bilal and Hamid Turab Mirza conceptualized the main idea, conducted feature extraction, acquired datasets, and contributed to the paper write-up. Adnan Ahmad, Ibrar Hussain and Ahmad Salman Khan contributed to the experiment design.
Corresponding author
Ethics declarations
Competing interests
Authors have no conflict of interest as defined by this specific Journal/Publisher, or other interests that might be perceived to influence the results and/or discussion reported in this paper.
Ethical approval
This research does not involve any human or animal subjects and it is not directly or indirectly related to the participation of humans and animals in performing experiments. Therefore, no ethical approval is required for the conduct of this research.
Research data policy statement
This research paper promotes the availability and usability of research data. This policy ensures that the data underpinning 31 the findings of this study are appropriately managed and made accessible to the scientific community.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Bilal, A., Mirza, H.T., Ahmad, A. et al. Unveiling functional aspects in google play education app titles and descriptions influencing app success. Autom Softw Eng 32, 23 (2025). https://doi.org/10.1007/s10515-025-00497-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10515-025-00497-6