App Categories Research Articles

Unveiling User Sentiment: Aspect-Based Analysis and Topic Modeling of Ride-Hailing and Google Play App Reviews

Background: Mobile app usage is increasing in the digital age, with Ride-Hailing app becoming the primary example of this trend. To obtain valuable understanding of how people perceive and interact with mobile app, user reviews on platforms such as Google Play are usually analyzed. This analysis can assist developers to identify areas for improvement in both Ride-hailing and Google Play App. A promising method that can be used to analyze user perception in this instance is Aspect-Based Sentiment Analysis (ABSA). Objective: This research aimed to apply ABSA to user reviews using Bidirectional Encoder Representations from Transformers (BERT) models. In this context, aspect identification and topic modeling were performed by using Latent Dirichlet Allocation (LDA). The model extracted topics from the reviews and used Generative Artificial Intelligence (GenAI) to define the aspects of the topics to further enhance the analysis. For consistency and accuracy, the method included sentiment annotation by a human annotator. Methods: A total of two datasets were used in this research, with the first collected by scraping user reviews of Ride-Hailing App while the second was obtained from Kaggle, and to identify relevant topics, modeling was performed using LDA. These topics were then categorized into aspects using GenAI, covering areas, such as customer experience, service, payment, app features, task management, and event management. Subsequently, sentiment labeling was conducted using human annotators to provide a reliable baseline. BERT model was then used to classify sentiment with aspect hints, and the evaluation included calculations of accuracy, precision, recall, and F1-score. Results: The results showed that BERT model achieved the highest accuracy of 97% in sentiment analysis across all datasets. Conclusion: This research provided valuable understanding of user experience and established a strong ABSA framework for analyzing user reviews using LDA, Aspect Annotation, GenAI, and BERT sentiment models. Future research could expand this method to other app categories and incorporate real-time ABSA for continuous monitoring and dynamic feedback. Keywords: User Reviews, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Topic Modeling, Generative Artificial Intelligence (GenAI)

Experience from Designing Augmented Reality Browsing Interfaces for Real-world Walking Scenarios

Mobile phones have enabled users to browse information in varying mobility contexts. For high-mobility settings such as walking, however, phones pose several usability challenges, particularly safety and limited screen sizes. While Augmented Reality (AR) has been proposed to address these issues, prior work has yet to investigate AR interface design in real-world walking conditions beyond text readability and notification design. This paper presents the first exploration of AR browsing interface design and extended usage while walking in the wild. We first conducted design sessions with 12 UI designers while walking in varied environments to design the window size, distance, opacity, anchor type, and placement for three categories of apps: text, video, and mixed content. Results show that traffic level significantly affects the designed window size, whereas content type significantly affects window size, distance, opacity, and vertical placement. To gain further insights from real-world usage, we conducted a multi-day observational study with 5 participants and observed that participants on average switched among window layouts every 3.3 minutes, for reasons such as safety and the level of extended visual attention.

Mapping the Online Reviews Sentiment Landscape: An Exploration of Emotion Spectrum in User Reviews of Mobile Apps

Mobile applications have emerged as powerful platforms for consumers and understanding the attitudes and reactions of users by content and emotional sides in a mobile context becomes crucial for marketing decision-making. The study with comprehensive approach aims to analyze the emotion spectrum in mobile applications user reviews context by examining specific emotions (anger, disgust, fear, joy, neutral, sadness, surprise). Sentiment analysis methodology (through "Emotion English DistilRoBERTa-base" transformers-model) is employed on the dataset of 302.647 reviews from 34 mobile application categories. Categorical examination of emotions indicates that neutrality is the dominant emotion category, followed by joy, sadness, disgust, surprise, and anger emotion categories, while the fear category is the least dominant category. According to polarity examination; negative polarity reviews are associated with neutral, sadness and disgust emotions; neutral polarity reviews are associated with neutral and sadness; positive polarity reviews are associated with neutral and joy emotion categories. Final part of analysis includes examination of emotions individually and mobile app categories which each emotion with the highest frequency of dominance are presented. The distribution rates of emotions and the individual relationships of emotions with different application categories can shed light on future academic research and marketing decision-making.

Adolescents’ Digital Nightlife: The Comparative Effects of Day- and Nighttime Smartphone Use on Sleep Quality

The smartphone occupies a substantial part of adolescents’ daily life, from the moment they wake up to, for some, well beyond their bedtime. The current study compared the impact of adolescents’ daytime, pre-bedtime, and post-bedtime smartphone use on their sleep quality. In addition, it explored the differential effects of lean-back and lean-forward smartphone apps. We collected data from 155 adolescents across 21 days using smartphone tracking (745,706 app activities) in combination with experience sampling (1,950 sleep quality assessments). We found no significant effects of daytime and pre-bedtime smartphone use on sleep quality, but a negative association of post-bedtime smartphone use with sleep quality (β = −.09). The association between smartphone use and sleep quality varied across app categories: Time spent on lean-forward apps around bedtime, such as social media apps right before (β = −.08) and game apps after bedtime (β = −.23), was associated with lower sleep quality. The use of lean-back apps (i.e., video players) was not associated with sleep quality, neither before nor after bedtime.

Data Analysis and Prediction of Of Various app

Abstract – This study focuses on the exploratory data analysis (EDA) and prediction of ratings for various apps available on the Google Play Store. As the number of mobile applications continues to grow exponentially, understanding the factors that influence app ratings can provide valuable insights for developers and stakeholders. This research involves a detailed examination of a dataset containing information about numerous apps, including their categories, sizes, user ratings, number of installs, and more. The EDA process involves summarizing the main characteristics of the data, identifying patterns, and uncovering anomalies. Key techniques such as descriptive statistics, visualization, and correlation analysis are utilized to explore relationships between app ratings and various attributes. Insights gained from EDA include the distribution of app ratings, the influence of app category and size on ratings, and trends in user feedback over time. Following EDA, a predictive modeling approach is employed to forecast app ratings based on identified influential features. Various machine learning algorithms, such as linear regression, decision trees, and random forests, are applied and compared to determine the most effective model. Performance metrics like Mean Squared Error (MSE) and R-squared are used to evaluate and validate the models. The results of this study highlight significant predictors of app ratings, offering practical recommendations for app developers to enhance user satisfaction. Additionally, the predictive models provide a framework for anticipating app success in the market, enabling more informed decision-making. Overall, this research contributes to a deeper understanding of the app ecosystem on the Google Play Store and demonstrates the value of data-driven approaches in optimizing app development and marketing strategies. IndexTerms – Car price prediction, Machine learning, Regression analysis, Automobile market, Predictive modeling, Vehicle valuation, Data analysis, Price determinants, Automotive industry, Market efficiency

Exploratory Data Analysis and Prediction of Rating Of Various app on Google Play Store

Abstract – This study focuses on the exploratory data analysis (EDA) and prediction of ratings for various apps available on the Google Play Store. As the number of mobile applications continues to grow exponentially, understanding the factors that influence app ratings can provide valuable insights for developers and stakeholders. This research involves a detailed examination of a dataset containing information about numerous apps, including their categories, sizes, user ratings, number of installs, and more. The EDA process involves summarizing the main characteristics of the data, identifying patterns, and uncovering anomalies. Key techniques such as descriptive statistics, visualization, and correlation analysis are utilized to explore relationships between app ratings and various attributes. Insights gained from EDA include the distribution of app ratings, the influence of app category and size on ratings, and trends in user feedback over time. Following EDA, a predictive modeling approach is employed to forecast app ratings based on identified influential features. Various machine learning algorithms, such as linear regression, decision trees, and random forests, are applied and compared to determine the most effective model. Performance metrics like Mean Squared Error (MSE) and R-squared are used to evaluate and validate the models. The results of this study highlight significant predictors of app ratings, offering practical recommendations for app developers to enhance user satisfaction. Additionally, the predictive models provide a framework for anticipating app success in the market, enabling more informed decision-making. Overall, this research contributes to a deeper understanding of the app ecosystem on the Google Play Store and demonstrates the value of data-driven approaches in optimizing app development and marketing strategies. IndexTerms – Car price prediction, Machine learning, Regression analysis, Automobile market, Predictive modeling, Vehicle valuation, Data analysis, Price determinants, Automotive industry, Market efficiency

Analysis of Google Play Store Apps Data Using Tableau Data Visualization Application

This research aims to enhance understanding of big data management and processing. One of the challenges faced is the complexity and large volume of data, which requires effective tools and techniques for analysis and visualization. The objective of this study is to analyze Google Play Store app data based on categories and ratings, and to visualize the results using Tableau. The research method employs a quantitative approach with a framework that includes problem formulation, data collection from the Google Play Store Apps database obtained from kaggle.com, data processing, and analysis using Tableau. The results of the study indicate that the use of data visualization in the form of management graphics, such as horizontal bars and treemaps, is highly effective in identifying the comparison of the number of applications based on categories and ratings. These visualizations facilitate understanding the distribution and trends of applications on the Google Play Store. In conclusion, this research demonstrates that data visualization with Tableau can optimize big data processing and provide valuable insights into the distribution of app categories and ratings on the Google Play Store. These findings underscore the importance of using visualization tools in big data analysis to enhance understanding and improve decision-making.

Call to Action: Investigating Interaction Delay in Smartphone Notifications.

Notifications are an essential part of the user experience on smart mobile devices. While some apps have to notify users immediately after an event occurs, others can schedule notifications strategically to notify them only on opportune moments. This tailoring allows apps to shorten the users' interaction delay. In this paper, we present the results of a comprehensive study that identified the factors that influence users' interaction delay to their smartphone notifications. We analyzed almost 10 million notifications collected in-the-wild from 922 users and computed their response times with regard to their demographics, their Big Five personality trait scores and the device's charging state. Depending on the app category, the following tendencies can be identified over the course of the day: Most notifications were logged in late morning and late afternoon. This number decreases in the evening, between 8 p.m. and 11 p.m., and at the same time exhibits the lowest average interaction delays at daytime. We also found that the user's sex and age is significantly associated with the response time. Based on the results of our study, we encourage developers to incorporate more information on the user and the executing device in their notification strategy to notify users more effectively.

A difference-in-differences analysis of the impact of government financial subsidies on mobile app usage: a natural experiment during COVID-19

ABSTRACT This study examines the impact of financial subsidies provided by the Korean government to help people during economic crises or natural disasters such as the COVID-19 pandemic. To this end, we conduct a natural experiment by selecting two adjacent South Korean metropolitan regions, wherein only one received a local government subsidy. Additionally, we utilize Nielson panel data to track residents’ usage of two mobile app categories in these regions. To uncover the causal effect of the financial subsidy on mobile app usage, we develop a difference-in-differences (DiD) model, revealing that the subsidy increased mobile app usage for food delivery and medical service apps, and this increase was relatively steady for medical service apps. Furthermore, in response to the subsidy, low-income individuals used food delivery apps more than mid- and high-income individuals, while those with high incomes utilized medical service apps more than those with low incomes. This study presents important managerial implications for policymakers to alleviate the economic damage caused by natural disasters such as future recurrent pandemics.

Is identifying boredom the answer to controlling the bombardment of notifications on mobile devices?

Mobile notifications have become ubiquitous in modern life, yet excessive volumes contribute to interruption overload. This paper investigates intelligent notification management leveraging user context. A three-stage methodology employed a focus group, survey, and in-the-wild data collection app. The focus group (n=12)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(n=12)$$\\end{document} provided preliminary insights into notification perceptions during boredom which informed survey design. The survey (n=106)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(n=106)$$\\end{document} probed usage habits across times, days, and app categories. The SeektheNotification app gathered real-world notification data from 20 Android users over 3 months.Analysis revealed social and personal apps dominate notification volumes (91% combined). Shorter response delays occurred on weekends and after 12 pm, suggesting heightened user receptivity during boredom. Random Forest classification achieved 88% accuracy, outperforming 13 other algorithms, underscoring machine learning’s potential for context-aware notification systems.Our exploratory findings indicate notifications could be optimized by considering situational factors like boredom. Further research should expand context beyond boredom and employ advanced deep learning techniques. This preliminary study demonstrates the promise of leveraging user psychology and machine intelligence to develop smarter interruption management systems to combat notification overload.

Contradiction in text review and apps rating: prediction using textual features and transfer learning.

Mobile app stores, such as Google Play, have become famous platforms for practically all types of software and services for mobile phone users. Users may browse and download apps via app stores, which also help developers monitor their apps by allowing users to rate and review them. App reviews may contain the user's experience, bug details, requests for additional features, or a textual rating of the app. These ratings can be frequently biased due to inadequate votes. However, there are significant discrepancies between the numerical ratings and the user reviews. This study uses a transfer learning approach to predict the numerical ratings of Google apps. It benefits from user-provided numeric ratings of apps as the training data and provides authentic ratings of mobile apps by analyzing users' reviews. A transfer learning-based model ELMo is proposed for this purpose which is based on the word vector feature representation technique. The performance of the proposed model is compared with three other transfer learning and five machine learning models. The dataset is scrapped from the Google Play store which extracts the data from 14 different categories of apps. First, biased and unbiased user rating is segregated using TextBlob analysis to formulate the ground truth, and then classifiers prediction accuracy is evaluated. Results demonstrate that the ELMo classifier has a high potential to predict authentic numeric ratings with user actual reviews.

Analyzing mobile app data demand and usage behavior in Sri Lanka

Owing to the rapid advancement of mobile computing, daily human practices have become more efficient and convenient. Consequently, the number of mobile app users has increased drastically over the last ten years, and a significant amount of energy is consumed by those mobile apps where the energy consumption depends on the data usage of the apps. Therefore, it is important to optimize data consumption to ensure sustainable digitalization. Mobile app personalization is one of the vital areas to optimize the data consumption of mobile apps. In this research, mobile app data usage and user experience data were collected and analyzed quantitatively and qualitatively. The data were collected from 40 Android smartphone users in Sri Lanka. Primarily, semi-structured interviews were conducted to collect qualitative data to identify mobile app usage patterns. In addition, data usage of each mobile app was collected using the in-built data usage tracker in Android smartphones for four months. Descriptive and correlation analyses were used to analyze quantitative data, whereas thematic and content analyses drew insights from the collected qualitative data. Our findings show the most data-demanding mobile app categories in terms of data consumption as social media, video players & editors, and communication. Moreover, users aged between 21 to 30 contribute to the high data consumption in mobile app usage. In addition, qualitative analysis shows that app usage behavior varied during and after the pandemic, and elderly (51-65 years) mobile app users use only a limited number of features from an app.

Harnessing the Power of Smart Devices for On-the-Go Education

This take a look at explores the dynamics of on-the-go education facilitated by means of smart devices, specializing in participant demographics, clever device utilization patterns, alternatives for academic apps, and perceptions of advantages and challenges. A numerous sample of 300 participants spanning diverse instructional ranges and genders engaged in a quantitative survey. Descriptive information discovered that clever gadgets are regularly used for training, with E-Books being the most favored app category. Participants recounted the advantages of flexibility and accessibility, but highlighted restrained peer interplay as a challenge. These findings underscore the need for innovative approaches to decorate collaboration in on-the-pass schooling. The have a look at's implications inform educators, designers, and technologists in adapting practices for the evolving needs of modern inexperienced persons.

WHERE IS THE DIGITAL DIVIDE? INTRA-GENERATIONAL DIVIDE ON APP USAGE IN POST-PANDEMIC HONG KONG

Abstract The COVID pandemic has evoked extensive digitalization of our daily lives. This paper reports two studies conducted in the June/July 2023 that evaluated smartphone usage among Hong Kong middle-aged and older adults after 13 months of smartphone-based compulsory track-and-trace COVID policy. A focus group study with 145 adults aged 55 years or above (Max = 92) found that smartphones are used primarily for instant but asynchronous communication, thus, WhatsApp and WeChat are the most popular. Divisive usage is the most salient with apps that involve monetary transactions (e.g., mobile banking, online shopping) with participants having more trust towards the security provided by the device, the network, and the financial institutions as well as higher socio-economic status (SES) being more receptive. Higher SES was also related to a broader repertoire of app use. These findings corroborate with those from a concurrent survey with 750 smartphone users aged 45 or older (Max = 78). Using latent class analysis, we found two classes of users based on their usage on 18 categories of apps. Class 1 (64.3%) were more likely to use all categories of apps than Class 2 (35.7%), with the largest Chi-square values reported on apps for small-sum transactions, online shopping, private hire cab/courier, and mobile banking – all related to monetary transactions. Class 1 were younger, with higher SES, owning more smart devices, more proficient with mobile devices, and more optimistic with technologies than Class 2. Our findings elucidate directions for narrowing the digital divide within the older adult population.

Mobile applications (apps) for tobacco cessation: Behaviour change potential and heuristic analysis using the Mobile Application Rating Scale (MARS)

Background Given the high prevalence of tobacco use, India presents a significant challenge in tobacco control. Yet, the support received for tobacco cessation is suboptimal. Hence, the aim was to identify and heuristically evaluate ‘high-quality’ and ‘engaging’ tobacco cessation mobile apps using the Mobile Application Rating Scale (MARS). Also, to categorize and analyse their features with respect to engagement, functionality, aesthetics, and information quality. Methods A systematic search of tobacco cessation apps was done within app stores of prominent smartphone platforms developed by Apple and Android. The following search terms: ‘quit smoking,’ ‘smoking cessation, ‘stop smoking,’ ‘smoking therapy,’ ‘quit tobacco,’ ‘cigarette cessation,’ ‘cold turkey,’ and ‘quit cigarette.’ Pearson’s correlations were used to analyse correlations between app scores (Total score app-quality/mean score) and downloads/ratings and number of downloads with the overall MARS score. A Chi-square test was performed to analyse any association between app focus and app release dates. Results Total MARS scores ranged from 3.1 to 4.9. Quitsure app (4.9), Craving to Quit! app (4.8) and Stop Tobacco Mobile Trainer: Quit Smoking App (4.74) were ranked the highest according to the MARS overall mean score. Older apps focussed more on mere goal setting or substance use, as compared to behaviour change; whereas, recently developed apps are now focussing more on behaviour change. Conclusions The content and functionality of behaviour change-focused apps were of higher quality than those of other app categories. These recently developed mHealth apps can effectively supersede the traditional smoking cessation methods.

Exploring User Experiences of the Mom2B mHealth Research App During the Perinatal Period: Qualitative Study.

Perinatal depression affects a significant number of women during pregnancy and after birth, and early identification is imperative for timely interventions and improved prognosis. Mobile apps offer the potential to overcome barriers to health care provision and facilitate clinical research. However, little is known about users' perceptions and acceptability of these apps, particularly digital phenotyping and ecological momentary assessment apps, a relatively novel category of apps and approach to data collection. Understanding user's concerns and the challenges they experience using the app will facilitate adoption and continued engagement. This qualitative study explores the experiences and attitudes of users of the Mom2B mobile health (mHealth) research app (Uppsala University) during the perinatal period. In particular, we aimed to determine the acceptability of the app and any concerns about providing data through a mobile app. Semistructured focus group interviews were conducted digitally in Swedish with 13 groups and a total of 41 participants. Participants had been active users of the Mom2B app for at least 6 weeks and included pregnant and postpartum women, both with and without depression symptomatology apparent in their last screening test. Interviews were recorded, transcribed verbatim, translated to English, and evaluated using inductive thematic analysis. Four themes were elicited: acceptability of sharing data, motivators and incentives, barriers to task completion, and user experience. Participants also gave suggestions for the improvement of features and user experience. The study findings suggest that app-based digital phenotyping is a feasible and acceptable method of conducting research and health care delivery among perinatal women. The Mom2B app was perceived as an efficient and practical tool that facilitates engagement in research as well as allows users to monitor their well-being and receive general and personalized information related to the perinatal period. However, this study also highlights the importance of trustworthiness, accessibility, and prompt technical issue resolution in the development of future research apps in cooperation with end users. The study contributes to the growing body of literature on the usability and acceptability of mobile apps for research and ecological momentary assessment and underscores the need for continued research in this area.

MHealth app usage amongst paediatric department doctors in South Africa.

Smartphone and mobile health (mHealth) applications (apps) have become an integral part of the day-to-day function of healthcare professionals, allowing quick, comprehensive, and up-to-date access to current clinical guidelines and other reference material. To evaluate the extent and nature of use of mHealth apps by paediatric department doctors in South Africa. E-mails requesting study participation were sent out to 285 paediatric department doctors employed at six hospitals affiliated to the University of the Witwatersrand. Willing participants were directed to complete the online study questionnaire. A total of 150 respondents completed the questionnaire. All respondents owned a mobile device and already had one or more mHealth apps, 95.3% were unaware of any regulatory body responsible for regulating the use of mHealth apps, 86.0% did not have access to free Wi-Fi at work and 87.3% used an mHealth app at least once daily. Drug dosing (81.3%), diagnostic (59.3%) and clinical decision-making (44.7%) apps were the most common app categories with Medscape® (62.0%) and EMGuidance® (41.3%) being the most frequently used apps. Peer recommendation (76.0%), app credibility (74.0%) and app functionality (66.0%) were the most common factors that were considered by respondents prior to downloading or using an mHealth app. Medical apps are frequently used among paediatric medical doctors of all ranks. Drug dosing, diagnostic and clinical decision-making apps are the most common app categories in use. Improved awareness of the regulations pertaining to the use of mHealth apps amongst doctors is required.

A Minimalistic Approach to Predict and Understand the Relation of App Usage with Students' Academic Performance

Due to usage of self-reported data which may contain biasness, the existing studies may not unveil the exact relation between academic grades and app categories such as Video. Additionally, the existing systems' requirement for data of prolonged period to predict grades may not facilitate early intervention to improve it. Thus, we presented an app that retrieves past 7 days' actual app usage data within a second (Mean=0.31s, SD=1.1s). Our analysis on 124 Bangladeshi students' real-time data demonstrates app usage sessions have a significant (p<0.05) negative association with CGPA. However, the Productivity and Books categories have a significant positive association whereas Video has a significant negative association. Moreover, the high and low CGPA holders have significantly different app usage behavior. Leveraging only the instantly accessed data, our machine learning model predicts CGPA within ±0.36 of the actual CGPA. We discuss the design implications that can be potential for students to improve grades.

Is There Any Relation Between Smartphone Usage and Loneliness During the COVID-19 Pandemic?: A Study by Exploring Two Objective App Usage Datasets

BACKGROUND: Though smartphone is popular and loneliness is higher among the youth, in low-and-middle income countries (LMICs) such as Bangladesh, the relation of loneliness with actual app usage is unexplored amid pandemic. Also, the studies conducted in developed countries are limited by exploration of some app categories.
 METHODS: We conducted two studies in Bangladesh: in 2020 (N1=100) and 2021 (N2=105). We collected participant’s ULS-8 score and 7 days’ actual app usage. We extracted app usage behavioral data from 1.69 million events and did semi-partial and partial correlation analyses.
 RESULTS: Our analysis did not present any significant relation which may indicate a negative impact on loneliness. However, we found higher usage of Social Media, Communication, Education, Books, and Shopping apps and higher entropy of Browser apps had significant (q<.05) relation with lower loneliness.
 CONCLUSION: Smartphone may not negatively impact loneliness. Instead, some app categories can play a role to mitigate loneliness.

Can App Reviews Help Developers to Improve Mobile User Interface Design?

<p>For mobile user interface (M-UI) design, it has an important impact on app user’s usage. However, M-UI design is limited by subjective factors, even professional developers can’t determine whether the M-UI design is good or bad. App reviews provide an opportunity to proactively collect user complaints and promptly improve the user experience of apps. Therefore, it is meaningful to explore whether app reviews can help developers to improve M-UI design. In this article, we randomly select six different categories of apps from Google Play Store and App Store, with over 160000 reviews, and conduct a preliminary empirical study to answer the question. Specially, we gather M-UI-related reviews, and compare the average rating of M-UI-related reviews and total reviews of each app. We observe that the M-UI is concerned by users and the average rating for M-UI-related reviews is lower than the average rating for total reviews. By extracting the topics of M-UI-related reviews, we estimate the sentiment of the M-UI-related topics. The results show that the number of M-UI-related topics are about three or four, and the sentiment of M-UI-related topics is related to the app itself. Further, by investigating the relation between the M-UI-related topics and M-UI design. We observe that users are concerned about the M-UI usability the most, and it is the various aspects of the M-UI that are causing user frustration. In particular, our findings show that M-UI-related reviews reflect the severity of M-UI-related issues and app reviews can help developers to improve M-UI design about appearance, usability, fault-tolerance, of which usability deserves the most attention.</p> <p> </p>