Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interac...

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Autores principales:	Aldabbas Hamza, Bajahzar Abdullah, Alruily Meshrif, Qureshi Ali Adil, Amir Latif Rana M., Farhan Muhammad
Formato:	article
Lenguaje:	EN
Publicado:	De Gruyter 2020
Materias:	text mining semantic analysis machine learning natural language processing corpus google play store Science Q Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/9e3a8b38ae74488db5704ee4ad77c4c2
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spelling	oai:doaj.org-article:9e3a8b38ae74488db5704ee4ad77c4c22021-12-05T14:10:51ZGoogle Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews2191-026X10.1515/jisys-2019-0197https://doaj.org/article/9e3a8b38ae74488db5704ee4ad77c4c22020-07-01T00:00:00Zhttps://doi.org/10.1515/jisys-2019-0197https://doaj.org/toc/2191-026XTo maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral.Aldabbas HamzaBajahzar AbdullahAlruily MeshrifQureshi Ali AdilAmir Latif Rana M.Farhan MuhammadDe Gruyterarticletext miningsemantic analysismachine learningnatural language processingcorpusgoogle play storeScienceQElectronic computers. Computer scienceQA75.5-76.95ENJournal of Intelligent Systems, Vol 30, Iss 1, Pp 192-208 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	text mining semantic analysis machine learning natural language processing corpus google play store Science Q Electronic computers. Computer science QA75.5-76.95
spellingShingle	text mining semantic analysis machine learning natural language processing corpus google play store Science Q Electronic computers. Computer science QA75.5-76.95 Aldabbas Hamza Bajahzar Abdullah Alruily Meshrif Qureshi Ali Adil Amir Latif Rana M. Farhan Muhammad Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews
description	To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral.
format	article
author	Aldabbas Hamza Bajahzar Abdullah Alruily Meshrif Qureshi Ali Adil Amir Latif Rana M. Farhan Muhammad
author_facet	Aldabbas Hamza Bajahzar Abdullah Alruily Meshrif Qureshi Ali Adil Amir Latif Rana M. Farhan Muhammad
author_sort	Aldabbas Hamza
title	Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews
title_short	Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews
title_full	Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews
title_fullStr	Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews
title_full_unstemmed	Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews
title_sort	google play content scraping and knowledge engineering using natural language processing techniques with the analysis of user reviews
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/9e3a8b38ae74488db5704ee4ad77c4c2
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Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

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