Version 1
: Received: 14 March 2020 / Approved: 11 August 2020 / Online: 11 August 2020 (08:14:10 CEST)
How to cite:
Karim, A.; Azhari, A.; Belhaouri, S.B.; Qureshi, A.A. Machine Learning Algorithm’s Measurement and Analytical Visualization of User’s Reviews for Google Play Store. Preprints2020, 2020030249. https://doi.org/10.20944/preprints202003.0249.v1
Karim, A.; Azhari, A.; Belhaouri, S.B.; Qureshi, A.A. Machine Learning Algorithm’s Measurement and Analytical Visualization of User’s Reviews for Google Play Store. Preprints 2020, 2020030249. https://doi.org/10.20944/preprints202003.0249.v1
Karim, A.; Azhari, A.; Belhaouri, S.B.; Qureshi, A.A. Machine Learning Algorithm’s Measurement and Analytical Visualization of User’s Reviews for Google Play Store. Preprints2020, 2020030249. https://doi.org/10.20944/preprints202003.0249.v1
APA Style
Karim, A., Azhari, A., Belhaouri, S.B., & Qureshi, A.A. (2020). Machine Learning Algorithm’s Measurement and Analytical Visualization of User’s Reviews for Google Play Store. Preprints. https://doi.org/10.20944/preprints202003.0249.v1
Chicago/Turabian Style
Karim, A., Samir Brahim Belhaouri and Ali Adil Qureshi. 2020 "Machine Learning Algorithm’s Measurement and Analytical Visualization of User’s Reviews for Google Play Store" Preprints. https://doi.org/10.20944/preprints202003.0249.v1
Abstract
The fact is quite transparent that almost everybody around the world is using android apps. Half of the population of this planet is associated with messaging, social media, gaming, and browsers. This online marketplace provides free and paid access to users. On the Google Play store, users are encouraged to download countless of applications belonging to predefined categories. In this research paper, we have scrapped thousands of users reviews and app ratings. We have scrapped 148 apps’ reviews from 14 categories. We have collected 506259 reviews from Google play store and subsequently checked the semantics of reviews about some applications form users to determine whether reviews are positive, negative, or neutral. We have evaluated the results by using different machine learning algorithms like Naïve Bayes, Random Forest, and Logistic Regression algorithm. we have calculated Term Frequency (TF) and Inverse Document Frequency (IDF) with different parameters like accuracy, precision, recall, and F1 and compared the statistical result of these algorithms. We have visualized these statistical results in the form of a bar chart. In this paper, the analysis of each algorithm is performed one by one, and the results have been compared. Eventually, We've discovered that Logistic Regression is the best algorithm for a review-analysis of all Google play store. We have proved that Logistic Regression gets the speed of precision, accuracy, recall, and F1 in both after preprocessing and data collection of this dataset.
Keywords
machine learning; preprocessing; semantic analysis; text mining; TF/IDF; scraping; Google Play Store
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
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.
