Google Play Store Apps: Data Analysis and Popularity Predictions Using Artificial Emotional Intelligence

Google Play Store Apps: Data Analysis and Popularity Predictions Using Artificial Emotional Intelligence

Parvathi R., Pattabiraman V.
Copyright: © 2023 |Pages: 13
DOI: 10.4018/978-1-6684-5673-6.ch012
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Abstract

The Google Play Store is one of the most well-known and widely used Android app stores. On the Play Store, there is a lot of new information not only by the developers of the programme but also by the users who provide reviews and ratings. All of this information may be used to provide valuable insight into app popularity, which can be quite beneficial to app creators. The authors used a Google Play Store raw data collection from the Kaggle website. The data set includes a variety of features that can be used to forecast app success. Many classifier models are used to predict the popularity of apps in this study and determined which one give the best results. In the classification model, user reviews are added as a numerical feature. This feature has been found to considerably improve classification accuracy. Surprisingly the social aspects have a significant impact on the popularity of an app are also considered in this study.
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Literature Survey

Sentiment Analysis is an important attribute as it gives more insight into customer behaviour and a subjective review of the customer to the App (Philip et al., 2003). Similar to this work, was the work done by Suresh and others (Chumwatana, 2015; Suresh & Urolagin, 2020; Kumari & Singh, 2016; Hanyang, 2019). They chose specific reviews giving out major details and reflecting high polarity and combined them with other attributes. These were then used to predict the popularity of the Apps.

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