Discrepancy Detection between Actual User Reviews and Numeric Ratings of Google App Store using Deep Learning

2021 ◽  
pp. 115111
Author(s):  
Saima Sadiq ◽  
Muhammad Umer ◽  
Saleem Ullah ◽  
Seyedali Mirjalili ◽  
Vaibhav Rupapara ◽  
...  
Keyword(s):  
Deep Learning ◽  
User Reviews ◽  
App Store ◽  
Discrepancy Detection

Detecting Usability and User Experience Issues in Stroke Caregiving Apps: An Analysis of User Reviews (Preprint)

2021 ◽  
Author(s):  
Elton Lobo ◽  
Mohamed Abdelrazek ◽  
Anne Frølich ◽  
Lene Juel Rasmussen ◽  
Patricia M. Livingston ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
User Experience ◽  
Participatory Design ◽  
User Participation ◽  
User Reviews ◽  
App Store ◽  
Healthcare Needs ◽  
Recovery Trajectory ◽  
Mhealth Apps

BACKGROUND Stroke caregivers often experience negative impacts when caring for a person living with a stroke. Technologically based interventions such as mHealth apps have demonstrated potential in supporting the caregivers during the recovery trajectory. Hence, there is an increase in apps in popular app stores, with a few apps addressing the healthcare needs of stroke caregivers. Since most of these apps were published without explanation of their design and evaluation processes, it is necessary to identify the usability and user experience issues to help app developers and researchers to understand the factors that affect long-term adherence and usage in stroke caregiving technology. OBJECTIVE The purpose of this study was to determine the usability and user experience issues in commercially available mHealth apps from the user reviews published within the app store to help researchers and developers understand the factors that may affect long-term adherence and usage. METHODS User reviews were extracted from the previously identified 47 apps that support stroke caregiving needs using a python-scraper for both app stores (i.e. Google Play Store and Apple App Store). The reviews were pre-processed to (i) clean the dataset and ensure unicode normalization, (ii) remove stop words and (iii) group words together with similar meanings. The pre-processed reviews were filtered using sentiment analysis to exclude positive and non-English reviews. The final corpus was classified based on usability and user experience dimensions to highlight issues within the app. RESULTS Of 1,385,337 user reviews, only 162,095 were extracted due to the limitations in the app store. After filtration based on the sentiment analysis, 15,818 reviews were included in the study and were filtered based on the usability and user experience dimensions. Findings from the usability and user experience dimensions highlight critical errors/effectiveness, efficiency and support that contribute to decreased satisfaction, affect and emotion and frustration in using the app. CONCLUSIONS Commercially available mHealth apps consist of several usability and user experience issues due to their inability to understand the methods to address the healthcare needs of the caregivers. App developers need to consider participatory design approaches to promote user participation in design. This might ensure better understanding of the user needs and methods to support these needs; therefore, limiting any issues and ensuring continued use.

Impact of online customer reviews and deep learning on product innovation empirical study on mobile applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huiliang Zhao ◽  
Qin Yang ◽  
Zhenghong Liu
Keyword(s):  
Deep Learning ◽  
Mobile Application ◽  
Product Innovation ◽  
Research Study ◽  
Market Competition ◽  
Online Reviews ◽  
Business Environment ◽  
Content Type ◽  
App Store ◽  
Networking Technology

PurposeThe customer enables online reviews, discusses product features and enhances the user's experiences in online activities. Users generated product innovation and product reviews effect as market competition. This research study explains deep learning, online reviews and product innovation empirical evidence used by mobile apps.Design/methodology/approachOnline reviews and product innovation are very important for every organization and firms to achieve a competitive advantage in a large business environment. When the authors see past traditional history, customers are not involved in product creating and innovating processes. Due to new technology changes, online systems and web 2.0 increase this ability.FindingsFor this research purpose, the authors use different analytical software to measure the impact among variables. This study is established on primary data; this study collected data from online customers and its users. For data collection, the authors use some questionnaires, and these questions are filled from 200 respondents.Research limitations/implicationsThis research study used data from the Google app store – Google product selling application – and gathered customers' online reviews. Research found that customers' online reviews and deep learning positively and significantly influence product innovation through networking technology. This research-based online mobile application and its research reviews found that organizations convert their own business online and effectively and efficiently enhance creditability.Originality/valueThis research study used data from the Google app store Google product selling application and gathered customers' online reviews. Research founded that customers' online reviews and deep learning are positively and significantly influence product innovation through networking technology. This research-based online mobile application and its research reviews found that organizations convert their own business online and effectively and efficiently enhance creditability.

scholarly journals User Perspectives of Mood-Monitoring Apps Available to Young People: Qualitative Content Analysis

10.2196/18140 ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. e18140 ◽  
Author(s):  
Emily Widnall ◽  
Claire Ellen Grant ◽  
Tao Wang ◽  
Lauren Cross ◽  
Sumithra Velupillai ◽  
...  
Keyword(s):  
Content Analysis ◽  
Young People ◽  
Qualitative Content Analysis ◽  
Application Programming Interface ◽  
User Engagement ◽  
User Reviews ◽  
Social Community ◽  
App Store ◽  
Mobile Health Apps ◽  
Technical Features

Background Mobile health apps are increasingly available and used in a clinical context to monitor young people’s mood and mental health. Despite the benefits of accessibility and cost-effectiveness, consumer engagement remains a hurdle for uptake and continued use. Hundreds of mood-monitoring apps are publicly available to young people on app stores; however, few studies have examined consumer perspectives. App store reviews held on Google and Apple platforms provide a large, rich source of naturally generated, publicly available user reviews. Although commercial developers use these data to modify and improve their apps, to date, there has been very little in-depth evaluation of app store user reviews within scientific research, and our current understanding of what makes apps engaging and valuable to young people is limited. Objective This study aims to gain a better understanding of what app users consider useful to encourage frequent and prolonged use of mood-monitoring apps appropriate for young people. Methods A systematic approach was applied to the selection of apps and reviews. We identified mood-monitoring apps (n=53) by a combination of automated application programming interface (API) methods. We only included apps appropriate for young people based on app store age categories (apps available to those younger than 18 years). We subsequently downloaded all available user reviews via API data scraping methods and selected a representative subsample of reviews (n=1803) for manual qualitative content analysis. Results The qualitative content analysis revealed 8 main themes: accessibility (34%), flexibility (21%), recording and representation of mood (18%), user requests (17%), reflecting on mood (16%), technical features (16%), design (13%), and health promotion (11%). A total of 6 minor themes were also identified: notification and reminders; recommendation; privacy, security, and transparency; developer; adverts; and social/community. Conclusions Users value mood-monitoring apps that can be personalized to their needs, have a simple and intuitive design, and allow accurate representation and review of complex and fluctuating moods. App store reviews are a valuable repository of user engagement feedback and provide a wealth of information about what users value in an app and what user needs are not being met. Users perceive mood-monitoring apps positively, but over 20% of reviews identified the need for improvement.

App store mining for iterative domain analysis: Combine app descriptions with user reviews

2019 ◽  
Vol 49 (6) ◽  
pp. 1013-1040 ◽  
Author(s):  
Yuzhou Liu ◽  
Lei Liu ◽  
Huaxiao Liu ◽  
Xinglong Yin
Keyword(s):  
Domain Analysis ◽  
User Reviews ◽  
App Store

Handwriting 99 Multiplication on App Store

2020 ◽  
pp. 110-127
Keyword(s):  
Deep Learning ◽  
Test Accuracy ◽  
App Store ◽  
Handwritten Digit ◽  
Traditional Approaches ◽  
Testing Patterns ◽  
Model Subject

The Modified NIST (MNIST) database, consisting of 70,000 handwritten digit images, in partition to 60,000 training patterns and 10,000 testing patterns, serves as a typical benchmark of evaluating performance of handwritten digit classification. After the LeNet CNNs model proposed by LeCun, researchers regarded this example as “Hello, World” in the field of deep learning. This chapter compares traditional approaches with the CNN model. The dataset of training and testing CNN models here is expanded to the Extension-MNIST (EMNIST) database. It will be employed to pre-train a CNN model for recognizing the handwritten digit image and installation on the iOS device. The user of the presented App can directly write digits on the touchscreen, and the smartphone instantly recognizes what were written. The pre-trained model subject to EMNIST database with a test accuracy of 99.4% has been integrated to an iOS App, termed as handwriting 99 multiplication, which has been successfully published on Apple's App Store.

scholarly journals User Perspectives of Mood-Monitoring Apps Available to Young People: Qualitative Content Analysis (Preprint)

2020 ◽  
Author(s):  
Emily Widnall ◽  
Claire Ellen Grant ◽  
Tao Wang ◽  
Lauren Cross ◽  
Sumithra Velupillai ◽  
...  
Keyword(s):  
Content Analysis ◽  
Young People ◽  
Qualitative Content Analysis ◽  
Application Programming Interface ◽  
User Engagement ◽  
User Reviews ◽  
Social Community ◽  
App Store ◽  
Mobile Health Apps ◽  
Technical Features

BACKGROUND Mobile health apps are increasingly available and used in a clinical context to monitor young people’s mood and mental health. Despite the benefits of accessibility and cost-effectiveness, consumer engagement remains a hurdle for uptake and continued use. Hundreds of mood-monitoring apps are publicly available to young people on app stores; however, few studies have examined consumer perspectives. App store reviews held on Google and Apple platforms provide a large, rich source of naturally generated, publicly available user reviews. Although commercial developers use these data to modify and improve their apps, to date, there has been very little in-depth evaluation of app store user reviews within scientific research, and our current understanding of what makes apps engaging and valuable to young people is limited. OBJECTIVE This study aims to gain a better understanding of what app users consider useful to encourage frequent and prolonged use of mood-monitoring apps appropriate for young people. METHODS A systematic approach was applied to the selection of apps and reviews. We identified mood-monitoring apps (n=53) by a combination of automated application programming interface (API) methods. We only included apps appropriate for young people based on app store age categories (apps available to those younger than 18 years). We subsequently downloaded all available user reviews via API data scraping methods and selected a representative subsample of reviews (n=1803) for manual qualitative content analysis. RESULTS The qualitative content analysis revealed 8 main themes: accessibility (34%), flexibility (21%), recording and representation of mood (18%), user requests (17%), reflecting on mood (16%), technical features (16%), design (13%), and health promotion (11%). A total of 6 minor themes were also identified: notification and reminders; recommendation; privacy, security, and transparency; developer; adverts; and social/community. CONCLUSIONS Users value mood-monitoring apps that can be personalized to their needs, have a simple and intuitive design, and allow accurate representation and review of complex and fluctuating moods. App store reviews are a valuable repository of user engagement feedback and provide a wealth of information about what users value in an app and what user needs are not being met. Users perceive mood-monitoring apps positively, but over 20% of reviews identified the need for improvement.

scholarly journals Health App Recommendation System using Ensemble Multimodel Deep Learning

2020 ◽  
Vol 13 (5) ◽  
pp. 7-19
Author(s):  
Deepak Chowdary Edara ◽  
◽  
Venkatramaphanikumar Sistla ◽  
Venkata Krishna Kishore Kolli
Keyword(s):  
Deep Learning ◽  
Recommendation System ◽  
Opinion Mining ◽  
Semantic Analysis ◽  
Short Term Memory ◽  
Mobile Apps ◽  
Mobile App ◽  
User Reviews ◽  
Series Of Experiments ◽  
Long Short Term Memory

Nowadays, mobile devices and apps are meant to fulfill the needs of various people in society. But, mobile app Stores are facing major challenges in recommending proper apps for users. Recommending mobile apps for users according to personal preference and various mobile device limitations is therefore important. In this scenario, there is a huge need for developing recommender systems (RS) for the user’s community in enabling critical mobile apps such as Health based Apps. Recommendation Systems perform an extensive survey on the collection of user reviews, preferences and opinions to discover recommendations of suitable applications to the users' community. In this paper, we have designed an aspect-based recommendation framework by performing three tasks: such as identifying the mentions associated with item aspects in user reviews, extracting the sentiment related opinions using Latent Semantic Analysis of such aspects in the reviews, and perform the opinion mining from all of the aspects to generate enhanced recommendations with Ensemble Multimodel Deep Learning (EMDL). EMDL comprises of two state-of- the-art classifiers such as Deep Neural Networks (DNN) and Long Short Term Memory (LSTM). In contrast to the prior work, we conducted a series of experiments with several state-of-art deep learning models to extract useful recommendations. The achieved results show that classification with outperforms in all the aspects based on various evaluation metrics when compared to the rest of the models.

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