A study of gender in user reviews on the Google Play Store

2021 ◽  
Vol 27 (2) ◽  
Author(s):  
Ehsan Noei ◽  
Kelly Lyons
Keyword(s):  
User Reviews ◽  
Google Play

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 Identification of Fraud Apps Using Sentiment Analysis Techniques

2021 ◽  
Vol 15 (23) ◽  
pp. 178-185
Author(s):  
Abeer Aljumah ◽  
Amjad Altuwijri ◽  
Thekra Alsuhaibani ◽  
Afef Selmi ◽  
Nada Alruhaily
Keyword(s):  
Sentiment Analysis ◽  
User Feedback ◽  
Sensitive Data ◽  
User Reviews ◽  
Analysis Techniques ◽  
Android App ◽  
Fake Reviews ◽  
Google Play

Considering that application’s security is an important aspect, especially nowadays with the increase in technology and the number of fraudsters. It should be noted that determining the security of an application is a difficult task, especially since most fraudsters have become skilled and professional at manipulating people and stealing their sensitive data. Therefore, we pay attention to spot insecure apps by analyzing user feedback on Google Play platform using sentiment analysis. As it is known, user reviews reflect their experiments and experiences in addition to their feelings and satisfaction with the application. But unfortunately, not all of these reviews are real, fake reviews do not reflect the sincerity of feelings, so we have been keen in our work to filter the reviews and deliver accurate and correct results. This tool is useful for both users wanting to install an android app and for developers interested in app’s optimization.

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.

How Should I Improve the UI of My App?

2021 ◽  
Vol 30 (3) ◽  
pp. 1-38
Author(s):  
Qiuyuan Chen ◽  
Chunyang Chen ◽  
Safwat Hassan ◽  
Zhengchang Xing ◽  
Xin Xia ◽  
...  
Keyword(s):  
Positive Impact ◽  
Mobile Apps ◽  
Frequent Patterns ◽  
Response Mechanism ◽  
Essential Factor ◽  
User Reviews ◽  
Information Request ◽  
Comparative Review ◽  
Google Play ◽  
Patterns Of Interaction

UI (User Interface) is an essential factor influencing users’ perception of an app. However, it is hard for even professional designers to determine if the UI is good or not for end-users. Users’ feedback (e.g., user reviews in the Google Play) provides a way for app owners to understand how the users perceive the UI. In this article, we conduct an in-depth empirical study to analyze the UI issues of mobile apps. In particular, we analyze more than 3M UI-related reviews from 22,199 top free-to-download apps and 9,380 top non-free apps in the Google Play Store. By comparing the rating of UI-related reviews and other reviews of an app, we observe that UI-related reviews have lower ratings than other reviews. By manually analyzing a random sample of 1,447 UI-related reviews with a 95% confidence level and a 5% interval, we identify 17 UI-related issues types that belong to four categories (i.e., “Appearance,” “Interaction,” “Experience,” and “Others” ). In these issue types, we find “Generic Review” is the most occurring one. “Comparative Review” and “Advertisement” are the most negative two UI issue types. Faced with these UI issues, we explore the patterns of interaction between app owners and users. We identify eight patterns of how app owners dialogue with users about UI issues by the review-response mechanism. We find “Apology or Appreciation” and “Information Request” are the most two frequent patterns. We find updating UI timely according to feedback is essential to satisfy users. Besides, app owners could also fix UI issues without updating UI, especially for issue types belonging to “Interaction” category. Our findings show that there exists a positive impact if app owners could actively interact with users to improve UI quality and boost users’ satisfactoriness about the UIs.

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.

scholarly journals A Preliminary Evaluation of Mobile Phone Apps to Curb Alcohol Consumption

2021 ◽  
Vol 19 (1) ◽  
pp. 135
Author(s):  
Omar Mubin ◽  
Billy Cai ◽  
Abdullah Al Mahmud ◽  
Isha Kharub ◽  
Michael Lwin ◽  
...  
Keyword(s):  
Alcohol Intake ◽  
Mobile Apps ◽  
Modern Society ◽  
Self Control ◽  
Preliminary Evaluation ◽  
User Reviews ◽  
Design Recommendations ◽  
Alcohol Cessation ◽  
User Ratings ◽  
Google Play

Mobile apps have become increasingly prevalent in modern society, and persuasive technology has a broader market than ever. Mobile-based alcohol cessation apps can promote positive behaviour change in users and improve the overall health of our society. This research aimed to understand the various features users respond to and make design recommendations for alcohol cessation apps. This paper reports on three sources of feedback (user ratings, user reviews, MARS App Quality score) provided on 20 alcohol cessation apps in the Google Play Store. Our findings suggest that self-control type apps received much greater positive user reviews than motivational apps. In addition, this trend was not observed through numeric user ratings. We also speculate on design recommendations for apps that are meant to inhibit alcohol intake.

scholarly journals Methodology for Analyzing the Traditional Algorithms Performance of User Reviews Using Machine Learning Techniques

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 202
Author(s):  
Abdul Karim ◽  
Azhari Azhari ◽  
Samir Brahim Belhaouri ◽  
Ali Adil Qureshi ◽  
Maqsood Ahmad
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
User Reviews ◽  
Almost Everywhere ◽  
Document Frequency ◽  
Learning Techniques ◽  
Google Play

Android-based applications are widely used by almost everyone around the globe. Due to the availability of the Internet almost everywhere at no charge, almost half of the globe is engaged with social networking, social media surfing, messaging, browsing and plugins. In the Google Play Store, which is one of the most popular Internet application stores, users are encouraged to download thousands of applications and various types of software. In this research study, we have scraped thousands of user reviews and the ratings of different applications. We scraped 148 application reviews from 14 different categories. A total of 506,259 reviews were accumulated and assessed. Based on the semantics of reviews of the applications, the results of the reviews were classified negative, positive or neutral. In this research, different machine-learning algorithms such as logistic regression, random forest and naïve Bayes were tuned and tested. We also evaluated the outcome of term frequency (TF) and inverse document frequency (IDF), measured different parameters such as accuracy, precision, recall and F1 score (F1) and present the results in the form of a bar graph. In conclusion, we compared the outcome of each algorithm and found that logistic regression is one of the best algorithms for the review-analysis of the Google Play Store from an accuracy perspective. Furthermore, we were able to prove and demonstrate that logistic regression is better in terms of speed, rate of accuracy, recall and F1 perspective. This conclusion was achieved after preprocessing a number of data values from these data sets.

scholarly journals Android Apps Security Assessment using Sentiment Analysis Techniques: Comparative Study

2021 ◽  
Vol 15 (24) ◽  
pp. 123-133
Author(s):  
Abeer Aljumah ◽  
Amjad Altuwijri ◽  
Thekra Alsuhaibani ◽  
Afef Selmi ◽  
Nada Alruhaily
Keyword(s):  
Comparative Study ◽  
Sentiment Analysis ◽  
Security Assessment ◽  
Sensitive Data ◽  
User Reviews ◽  
Application Security ◽  
Analysis Techniques ◽  
Android Apps ◽  
Fake Reviews ◽  
Google Play

Considering that application security is an important aspect, especially nowadays with the increase in technology and the number of fraudsters. It should be noted that determining the security of an application is a difficult task, especially since most fraudsters have become skilled and professional at manipulating people and stealing their sensitive data. Therefore, we pay attention to trying to spot insecurity apps, by analyzing user feedback on the Google Play platform and using sentiment analysis to determine the apps level of security. As it is known, user reviews reflect their experiments and experiences in addition to their feelings and satisfaction with the application or not. But unfortunately, not all of these reviews are real, and as is known, the fake reviews do not reflect the sincerity of feelings, so we have been keen in our work to filter the reviews to be the result is accurate and correct. This study is useful for both users wanting to install android apps and for developers interested in app optimization.

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