A preliminary analysis of vocabulary in mobile app user reviews

2012 ◽  
Author(s):  
Leonard Hoon ◽  
Rajesh Vasa ◽  
Jean-Guy Schneider ◽  
Kon Mouzakis
Keyword(s):  
Preliminary Analysis ◽  
Mobile App ◽  
User Reviews

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.

Identifying functional aspects from user reviews for functionality-based mobile app recommendation

2017 ◽  
Vol 69 (2) ◽  
pp. 242-255 ◽  
Author(s):  
Xiaoying Xu ◽  
Kaushik Dutta ◽  
Anindya Datta ◽  
Chunmian Ge
Keyword(s):  
Mobile App ◽  
User Reviews ◽  
Functional Aspects

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.

Mining User Reviews for Mobile App Comparisons

2017 ◽  
Vol 1 (3) ◽  
pp. 1-15 ◽  
Author(s):  
Yuanchun Li ◽  
Baoxiong Jia ◽  
Yao Guo ◽  
Xiangqun Chen
Keyword(s):  
Mobile App ◽  
User Reviews

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.

scholarly journals Studying the consistency of star ratings and the complaints in 1 & 2-star user reviews for top free cross-platform Android and iOS apps

2016 ◽  
Author(s):  
Hanyang Hu ◽  
Cor-Paul Bezemer ◽  
Ahmed E Hassan
Keyword(s):  
Quality Assurance ◽  
Empirical Study ◽  
Software Quality ◽  
Negative Impact ◽  
Mobile App ◽  
User Reviews ◽  
Cross Platform ◽  
Prior Experiences

How users rate a mobile app via star ratings and user reviews is of utmost importance for the success of an app. Recent studies and surveys show that users rely heavily on star ratings and user reviews that are provided by other users, for deciding which app to download. However, understanding star ratings and user reviews is a complicated matter, since they are influenced by many factors such as the actual quality of the app and how the user perceives such quality relative to their expectations, which are in turn influenced by their prior experiences and expectations relative to other apps on the platform (e.g., iOS versus Android). Nevertheless, star ratings and user reviews provide developers with valuable information for improving the software quality of their app. In an effort to expand their revenue and reach more users, app developers commonly build cross-platform apps, i.e., apps that are available on multiple platforms. As star ratings and user reviews are of such importance in the mobile app industry, it is essential for developers of cross-platform apps to maintain a consistent level of star ratings and user reviews for their apps across the various platforms on which they are available. In this paper, we investigate whether cross-platform apps achieve a consistent level of star ratings and user reviews. We manually identify 19 cross-platform apps and conduct an empirical study on their star ratings and user reviews. By manually tagging 9,902 1 & 2-star reviews of the studied cross-platform apps, we discover that the distribution of the frequency of complaint types varies across platforms. Finally, we study the negative impact ratio of complaint types and find that for some apps, users have higher expectations on one platform. All our proposed techniques and our methodologies are generic and can be used for any app. Our findings show that at least 68% of the studied cross-platform apps do not have consistent star ratings, which suggests that different quality assurance efforts need to be considered by developers for the different platforms that they wish to support.

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