Version 1
: Received: 25 July 2020 / Approved: 26 July 2020 / Online: 26 July 2020 (17:11:09 CEST)
How to cite:
Karim, A.; Azhari, A.; Alruily, M.; Aldabbas, H.; Brahim Belhaouri, S.; Adil Qureshi, A. Classification of Google Play Store Application Reviews Using Machine Learning. Preprints2020, 2020070646. https://doi.org/10.20944/preprints202007.0646.v1
Karim, A.; Azhari, A.; Alruily, M.; Aldabbas, H.; Brahim Belhaouri, S.; Adil Qureshi, A. Classification of Google Play Store Application Reviews Using Machine Learning. Preprints 2020, 2020070646. https://doi.org/10.20944/preprints202007.0646.v1
Karim, A.; Azhari, A.; Alruily, M.; Aldabbas, H.; Brahim Belhaouri, S.; Adil Qureshi, A. Classification of Google Play Store Application Reviews Using Machine Learning. Preprints2020, 2020070646. https://doi.org/10.20944/preprints202007.0646.v1
APA Style
Karim, A., Azhari, A., Alruily, M., Aldabbas, H., Brahim Belhaouri, S., & Adil Qureshi, A. (2020). Classification of Google Play Store Application Reviews Using Machine Learning. Preprints. https://doi.org/10.20944/preprints202007.0646.v1
Chicago/Turabian Style
Karim, A., Samir Brahim Belhaouri and Ali Adil Qureshi. 2020 "Classification of Google Play Store Application Reviews Using Machine Learning" Preprints. https://doi.org/10.20944/preprints202007.0646.v1
Abstract
Google play store allow the user to download a mobile application (app) and user get inspired by the rating and reviews of the mobile app. A recent study analyzes that user preferences, user opinion for improvement, user sentiment about particular feature and detail with descriptions of experiences are very useful for an application developer. However, many application reviews are very large and difficult to process manually. Star rating is given of the whole application and the developer cannot analyze the single feature. In this research, we have scrapped 282,231 user reviews through different data scraping techniques. We have applied the text classification on these user reviews. We have applied different algorithms and find the precision, accuracy, F1 score and recall. In evaluated results, we have to also find the best algorithm.
Keywords
Machine Learning; Natural Language Processing; Text Mining; Semantic Analysis; Scraping; Google Play Store; Rating
Subject
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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