scholarly journals Classification of Google Play Store Application Reviews Using Machine Learning

2020 ◽  
Author(s):  
Abdul Karim ◽  
Azhari Azhari ◽  
Meshrif Alruily ◽  
Hamza Aldabbas ◽  
Samir Brahim Belhaouri ◽  
...  
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Mobile Application ◽  
Mobile App ◽  
User Preferences ◽  
User Reviews ◽  
Star Rating ◽  
Single Feature ◽  
Google Play

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.

Classification of Google Play Store Application Reviews Using Machine Learning

2020 ◽  
Author(s):  
Abdul Karim ◽  
SAMIR BRAHIM BELHAOUARI ◽  
Azhari SN ◽  
Ali Adil Qureshi
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Mobile Application ◽  
Mobile App ◽  
User Preferences ◽  
User Reviews ◽  
Star Rating ◽  
Single Feature ◽  
Google Play

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 find the best algorithm.

A Brief Survey on Text Classification Using Various Machine Learning Techniques

2018 ◽  
Vol 8 (1) ◽  
pp. 14
Author(s):  
Padmavathi .S ◽  
M. Chidambaram
Keyword(s):  
Machine Learning ◽  
Text Classification ◽  
Fixed Number ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Rule Based ◽  
Learning Techniques ◽  
Machine Learning Approach ◽  
Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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

2020 ◽  
Vol 30 (1) ◽  
pp. 192-208 ◽  
Author(s):  
Hamza Aldabbas ◽  
Abdullah Bajahzar ◽  
Meshrif Alruily ◽  
Ali Adil Qureshi ◽  
Rana M. Amir Latif ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Language Processing ◽  
Mobile Application ◽  
Knowledge Engineering ◽  
Machine Learning Algorithms ◽  
Application Development ◽  
User Reviews ◽  
N Gram ◽  
Logistic Regression Algorithm ◽  
Google Play

Abstract 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.

scholarly journals A Sentiment-Statistical Approach for Identifying Problematic Mobile App Updates Based on User Reviews

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 152 ◽  
Author(s):  
Xiaozhou Li ◽  
Boyang Zhang ◽  
Zheying Zhang ◽  
Kostas Stefanidis
Keyword(s):  
Mobile Applications ◽  
Statistical Approach ◽  
Maintenance Phase ◽  
Mobile App ◽  
User Reviews ◽  
Google Play

Mobile applications (apps) on IOS and Android devices are mostly maintained and updated via Apple Appstore and Google Play, respectively, where the users are allowed to provide reviews regarding their satisfaction towards particular apps. Despite the importance of user reviews towards mobile app maintenance and evolution, it is time-consuming and ineffective to dissect each individual negative review. In addition, due to the different app update strategies, it is uncertain that each update can be accepted well by the users. This study aims to provide an approach to detect the particular days during the mobile app maintenance phase when the negative reviews require developers’ attention. Furthermore, the method shall facilitate the mapping of the identified abnormal days towards the updates that result in such negativity in reviews. The method’s purpose is to enable app developers to respond swiftly to significant flaws reflected by user reviews in order to prevent user churns.

scholarly journals Determining Areas of Improvement in Quranic Arabic Vocabulary Learning Mobile Applications through Analysis of App User Reviews

2019 ◽  
Vol 8 (5C) ◽  
pp. 1188-1191
Keyword(s):  
Mobile Applications ◽  
Vocabulary Learning ◽  
Mobile App ◽  
Pricing Strategy ◽  
World Population ◽  
User Reviews ◽  
Arabic Speakers ◽  
The World ◽  
Technical Capability ◽  
Google Play

Muslims constitute roughly around one fifth of the world population, the majority of which are not Arabic speakers. This poses a problem for them in their devotional activities such as performing the five obligatory daily prayers and reading the Holy Qur’an because they could not understand what they are reciting or reading. Added to this, Muslim adults who are busy working usually find it hard to find the time to attend Quranic Arabic classes. In order to rectify this problem, some mobile app developers have created apps with the objective of teaching Muslims Quranic Arabic vocabulary items so that they could begin to learn and understand Quranic Arabic. In March 2019, there are about eleven Quranic Arabic vocabulary teaching mobile applications which could be downloaded from Google Play Store. These apps come with differing features and are of varying quality. This exploratory qualitative study aims to analyze the user reviews of these apps in order to determine areas where they can be further improved by the developers. The findings of this research found that generally developers should concentrate on three areas of improvement; their applications’ content, technical capability, and pricing strategy. It is hoped that the findings from this study can be used by Quranic Arabic vocabulary mobile app developers to further improve their apps so that the Muslim public can benefit more from them.

Risk of Readmission for Diabetes Patients: A Machine Learning Approach

2019 ◽  
Author(s):  
K Sowjanya ◽  
Mou Dasgupta
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Mobile Application ◽  
Health Care Quality ◽  
Care Quality ◽  
Mobile App ◽  
Machine Learning Techniques ◽  
Diabetic Patients ◽  
Readmission Rates ◽  
The People

Diabetes mellitus generally referred to as diabetes is reaching epidemic proportions in India and all around the world. The degree of disease and destruction due to diabetes and complications connected with diabetes is enormous, and originated a significant health care burden on both households and society. Deficiency of knowledge about diabetes causes untimely death among the population at large. Thus, developing a technique that should spread awareness about diabetes may affect the people. In this book, a mobile/android application based solution to overcome the lack of awareness about diabetes has been presented. The application uses machine learning techniques to predict risk of readmission to the hospital in diabetics. At the same time, the system also provides knowledge about diabetes and some suggestions on the disease. A comparative analysis of four machine learning (ML) algorithms were performed. The Decision Tree (DT) classifier outperforms amongst the 4 ML algorithms. Hence, DT classifier is used to design the machinery for the mobile application for diabetes risk of readmission prediction using UCI dataset. Due to the lack of knowledge many people even don’t know that they have diabetes, this will lead to a serious problem, as duration of unknown disease increases the risks associated with it also increases. Hospital readmission is a high-priority health care quality measure and target for cost reduction. Reducing readmission rates of diabetic patients have the potential to greatly reduce health care costs while simultaneously improving care. In this book, a novel mobile application based solution for this problem is provided. This mobile app, MobDBRCal (Risk of Readmission in Diabetes) will act as an important tool that can help in predicting the chances of risk of readmission in diabetes and also provides knowledge about this chronic disease.

scholarly journals Classification of Encrypted Mobile App Traffic Using the Machine Learning Method

2018 ◽  
pp. 21-28 ◽  
Author(s):  
Oleg Sheluhin ◽  
◽  
Vyacheslav Barkov ◽  
Mikhail Polkovnikov ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Mobile App ◽  
Machine Learning Method ◽  
Learning Method
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