The Porchlight Business Book Awards WINNERS have been announced!

Smart Assessment Methodology to Measure and Analyze Google Play Store

A Smart Assessment Methodology to Measure and Analyze Google Play Store

By Muhammad Farhan, Khalid Hussain, and Noor Zaman Jhanjhi

PRINT ON DEMAND— Shipping will be delayed 1-6 weeks for printing
(Depends on publisher)

There are millions of apps on a regular basis, where the creators are uploading. The millions of consumers uninstall such applications without testing duplicated data. Such applications impact users' personal information which damages the confidence of the users in Google Store and also causes users to lose.

READ FULL DESCRIPTION

Quantity Price Discount
List Price $54.50  

Quick Quote

Lorem ipsum dolor sit amet, consectetur adipisicing elit

Non-returnable discount pricing

$54.50


Book Information

Publisher: Eliva Press
Publish Date: 08/30/2020
Pages: 140
ISBN-13: 9781952751691
ISBN-10: 1952751691
Language: English

Full Description

There are millions of apps on a regular basis, where the creators are uploading. The millions of consumers uninstall such applications without testing duplicated data. Such applications impact users' personal information which damages the confidence of the users in Google Store and also causes users to lose. In addition, there is some detail on such programs, which is provided on a play page. In this analysis job, with the aid of a crawler or scraper that is used to catalog, calculate and evaluate the millions of play store applications, we have scraped a Google play store dataset. We have specific types of applications after having collected the details regarding the applications from the play shop. Such apps are charged and optional, so we have a selection of different types of home apps so gaming applications. In this work we examined the quality of the applications by addressing different testing questions dependent on a dataset 's attributes. We also tested the causal constructs from databases, correlations, correlation, repeated trends, user feedback sentiment analysis, utilizing the different machine learning approaches that can support developers and consumers alike. We evaluated consumer feedback on game apps utilizing latent sentiment research, which is a text mining technique. We consider the determinants generating either user-positivity or hostility against game applications. We evaluated the association between the average measurement of the emotions and the average client ranking. We analyzed that in feedback sentiment analysis graph much of the application displaying the top ranking in the rating graph is not right. The fundamental explorations of this analysis are the association between free and paid application with download duration, commercials on free and paid applications with respect to a number of downloads, rating connection with quality. We also visualized the interaction between specific attributes from the play store data using multiple simulation methods that are more important to see how programs interact with each other depending on the different attributes.

We have updated our privacy policy. Click here to read our full policy.