Abstract
In this paper, authors used the Exploratory Data Analysis (EDA) that embodies different patterns and find useful tidings from Google play store application (app) data. The intrinsic objective behind this is to analyze the features of the dataset in order to help the developers to understand the trends within the market and the end user needs towards the application, as well as the mechanism of App Store Optimization (ASO) that leads to enhancement of the popularity of the developer app.
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References
Android and Google Play Statistics (2019) Development resources and intelligence | AppBrain. Appbrain.Com. https://www.appbrain.com/stats. Accessed 6 Jan 2019
World Population Clock: 7.7 Billion People (2019) - Worldometers (2019) Worldometers.Info. http://www.worldometers.info/world-population/. Accessed 6 Jan 2019
2018, Market and Statistics - Elearning Learning (2019). Elearninglearning.Com. https://www.elearninglearning.com/2018/market/statistics/. Accessed 6 Jan 2019
2019. https://www.quora.com/What-are-best-practices-for-app-store-optimization. Accessed 6 Jan 2019
Joorabchi ME, Mesbah A, Kruchten P (2013) Real challenges in mobile app development. In: Empirical software engineering and measurement, 2013 ACM/IEEE international symposium on. IEEE
Chang G, Huo H (2018) A method of fine grained short text sentiment analysis based on machine learning. Neural Netw World 28(4):345–360
Hassan S, Bezemer C, Hassan A (2018) Studying bad updates of top free-to download apps in the Google play store. IEEE Trans Softw Eng pp 1–1
McIlroy S et al (2015) Fresh apps: an empirical study of frequently-updated mobile apps in the Google play store. Empir Software Eng 21(3):13461370. https://doi.org/10.1007/s10664-015-9388-2
Mojica Ruiz I et al (2017) An examination of the current rating system used in mobile app stores. IEEE Software, 1–1. Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ms.2017.265094809
Varshney K (2018) Sentiment analysis of application reviews on play store. Int J Res Appl Sci Eng Technol (IJRASET) 6(3):2327–2329. https://doi.org/10.22214/ijraset.2018.3537
Hu H et al (2018) Studying the consistency of star ratings and reviews of popular free hybrid android and ios apps. Empir Software Eng. https://doi.org/10.1007/s10664-018-9617-6
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Rajesh, N., Prasad, K.D., Akhila, N., Dayal, A. (2020). Exploratory Data Analysis to Build Applications for Android Developer. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-24322-7_30
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DOI: https://doi.org/10.1007/978-3-030-24322-7_30
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