This dataset offers a focused and invaluable window into user perceptions and experiences with applications listed on the Apple App Store. It is a vital resource for app developers, product managers, market analysts, and anyone seeking to understand the direct voice of the customer in the dynamic mobile app ecosystem.

Dataset Specifications:

• Investment: $45.0
• Status: Published and immediately available.
• Category: Ratings and Reviews Data
• Format: Compressed ZIP archive containing JSON files, ensuring easy integration into your analytical tools and platforms.
• Volume: Comprises 10,000 unique app reviews, providing a robust sample for qualitative and quantitative analysis of user feedback.
• Timeliness: Last crawled: (This field is blank in your provided info, which means its recency is currently unknown. If this were a real product, specifying this would be critical for its value proposition.)

Richness of Detail (11 Comprehensive Fields):

Each record in this dataset provides a detailed breakdown of a single App Store review, enabling multi-dimensional analysis:

1. Review Content:

   • review: The full text of the user's written feedback, crucial for Natural Language Processing (NLP) to extract themes, sentiment, and common keywords.
   • title: The title given to the review by the user, often summarizing their main point.
   • isEdited: A boolean flag indicating whether the review has been edited by the user since its initial submission. This can be important for tracking evolving sentiment or understanding user behavior.

2. Reviewer & Rating Information:

   • username: The public username of the reviewer, allowing for analysis of engagement patterns from specific users (though not personally identifiable).
   • rating: The star rating (typically 1-5) given by the user, providing a quantifiable measure of satisfaction.

3. App & Origin Context:

   • app_name: The name of the application being reviewed.
   • app_id: A unique identifier for the application within the App Store, enabling direct linking to app details or other datasets.
   • country: The country of the App Store storefront where the review was left, allowing for geographic segmentation of feedback.

4. Metadata & Timestamps:

   • _id: A unique identifier for the specific review record in the dataset.
   • crawled_at: The timestamp indicating when this particular review record was collected by the data provider (Crawl Feeds).
   • date: The original date the review was posted by the user on the App Store.

Expanded Use Cases & Analytical Applications:

This dataset is a goldmine for understanding what users truly think and feel about mobile applications. Here's how it can be leveraged:

• Product Development & Improvement:

  • Bug Detection & Prioritization: Analyze negative review text to identify recurring technical issues, crashes, or bugs, allowing developers to prioritize fixes based on user impact.
  • Feature Requests & Roadmap Prioritization: Extract feature suggestions from positive and neutral review text to inform future product roadmap decisions and develop features users actively desire.
  • User Experience (UX) Enhancement: Understand pain points related to app design, navigation, and overall usability by analyzing common complaints in the review field.
  • Version Impact Analysis: If integrated with app version data, track changes in rating and sentiment after new app updates to assess the effectiveness of bug fixes or new features.

• Market Research & Competitive Intelligence:

  • Competitor Benchmarking: Analyze reviews of competitor apps (if included or combined with similar datasets) to identify their strengths, weaknesses, and user expectations within a specific app category.
  • Market Gap Identification: Discover unmet user needs or features that users desire but are not adequately provided by existing apps.
  • Niche Opportunities: Identify specific use cases or user segments that are underserved based on recurring feedback.

• Marketing & App Store Optimization (ASO):

  • Sentiment Analysis: Perform sentiment analysis on the review and title fields to gauge overall user satisfaction, pinpoint specific positive and negative aspects, and track sentiment shifts over time.
  • Keyword Optimization: Identify frequently used keywords and phrases in reviews to optimize app store listings, improving discoverability and search ranking.
  • Messaging Refinement: Understand how users describe and use the app in their own words, which can inform marketing copy and advertising campaigns.
  • Reputation Management: Monitor rating trends and identify critical reviews quickly to facilitate timely responses and proactive customer engagement.

• Academic & Data Science Research:

  • Natural Language Processing (NLP): The review and title fields are excellent for training and testing NLP models for sentiment analysis, topic modeling, named entity recognition, and text summarization.
  • User Behavior Analysis: Study patterns in rating distribution, isEdited status, and date to understand user engagement and feedback cycles.
  • Cross-Country Comparisons: Analyze country-specific reviews to understand regional differences in app perception, feature preferences, or cultural nuances in feedback.

This App Store Reviews dataset provides a direct, unfiltered conduit to understanding user needs and ultimately driving better app performance and greater user satisfaction. Its structured format and granular detail make it an indispensable asset for data-driven decision-making in the mobile app industry.

Do you know?

Do you know how much time you spend on an app? Do you know the total use time of a day or average use time of an app?

What it consists of?

This data set consists of - how many times a person unlocks his phone. - how much time he spends on every app on every day. - how much time he spends on his phone.

It lists the usage time of apps for each day.

What we can do?

Use the test data to find the Total Minutes that we can use the given app in a day. we can get a clear stats of apps usage. This data set will show you about the persons sleeping behavior as well as what app he spends most of his time. with this we can improve the productivity of the person.

The dataset was collected from the app usage app.

Context

This data set contains some basic statistics about user count and user growth as well as crash count for a real mobile app. The dataset contains a basic timeseries of 1 hour resolution for a period of one week.

Content

The data set contains columns for total concurrent user count, new users acquired in that period of time, number of sessions and crash count.

Acknowledgements

This data set would not be available without the Real User Monitoring capabilities of Dynatrace and its flexibility to export and expose this data for scientific experiments.

Inspiration

The data set was intended to play around with seasonality, trend and prediction of timeseries.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.

This dataset encompasses mobile smartphone application (app) usage, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Use it for measurement, attribution or surveying to understand the why. iOS and Android operating system coverage.

Tie app usage to web and location events using anonymized PanelistID for omnichannel consumer journey understanding.

App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

This dataset comprises 10,000 user reviews of the BCA Mobile app collected from the Google Play Store between December 24, 2023, and June 12, 2024. Each review includes the user's name, the rating they provided (ranging from 1 to 5 stars), the timestamp of when the review was created, and the text content of the review. The dataset is in Indonesian and focuses on feedback from users in Indonesia. This data can be used to perform sentiment analysis, understand user experiences, identify common issues, and assess the overall performance of the BCA Mobile app during the specified timeframe. The reviews are sorted based on the newest first, providing the latest feedback at the top.

Unlock the power of user feedback with our iOS App Store Reviews Dataset, a comprehensive collection of reviews from thousands of apps across various categories. This robust App Store dataset includes essential details such as app names, ratings, user comments, timestamps, and more, offering valuable insights into user experiences and preferences.

Perfect for app developers, marketers, and data analysts, this dataset allows you to conduct sentiment analysis, monitor app performance, and identify trends in user behavior. By leveraging the iOS App Store Reviews Dataset, you can refine app features, optimize marketing strategies, and elevate user satisfaction.

Whether you’re tracking mobile app trends, analyzing specific app categories, or developing data-driven strategies, this App Store dataset is an indispensable tool. Download the iOS App Store Reviews Dataset today or contact us for custom datasets tailored to your unique project requirements.

Ready to take your app insights to the next level? Get the iOS App Store Reviews Dataset now or explore our custom data solutions to meet your needs.

At MFour, our Behavioral Data stands out for its uniqueness and depth of insights. What makes our data genuinely exceptional is the combination of several key factors:

• First-Party Opt-In Data: Our data is sourced directly from our opt-in panel of consumers who willingly participate in research and provide observed behaviors. This ensures the highest data quality and eliminates privacy concerns. CCPA compliant.

• Unparalleled Data Coverage: With access to 3B+ billion events, we have an extensive pool of participants who allow us to observe their brick + mortar location visitation, app + web smartphone usage, or both. This large-scale coverage provides robust and reliable insights.

• Our data is generally sourced through our Surveys On The Go (SOTG) mobile research app, where consumers are incentivized with cash rewards to participate in surveys and share their observed behaviors. This incentivized approach ensures a willing and engaged panel, leading to the highest-quality data.

The primary use cases and verticals of our Behavioral Data Product are diverse and varied. Some key applications include:

• Data Acquisition and Modeling: Our data helps businesses acquire valuable insights into consumer behavior and enables modeling for various research objectives.

• Shopper Data Analysis: By understanding purchase behavior and patterns, businesses can optimize their strategies, improve targeting, and enhance customer experiences.

• Media Consumption Insights: Our data provides a deep understanding of viewer behavior and patterns across popular platforms like YouTube, Amazon Prime, Netflix, and Disney+, enabling effective media planning and content optimization.

• App Performance Optimization: Analyzing app behavior allows businesses to monitor usage patterns, track key performance indicators (KPIs), and optimize app experiences to drive user engagement and retention.

• Location-Based Targeting: With our detailed location data, businesses can map out consumer visits to physical venues and combine them with web and app behavior to create predictive ad targeting strategies.

• Audience Creation for Ad Placement: Our data enables the creation of highly targeted audiences for ad campaigns, ensuring better reach and engagement with relevant consumer segments.

The Behavioral Data Product complements our comprehensive suite of data solutions in the broader context of our data offering. It provides granular and event-level insights into consumer behaviors, which can be combined with other data sets such as survey responses, demographics, or custom profiling questions to offer a holistic understanding of consumer preferences, motivations, and actions.

MFour's Behavioral Data empowers businesses with unparalleled consumer insights, allowing them to make data-driven decisions, uncover new opportunities, and stay ahead in today's dynamic market landscape.

The number of smartphone users in Saudi Arabia was forecast to continuously increase between 2024 and 2029 by in total five million users (+22.17 percent). After the nineteenth consecutive increasing year, the smartphone user base is estimated to reach 27.51 million users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Kuwait and Israel.

This dataset includes network traffic data from more than 50 Android applications across 5 different scenarios. The applications are consistent in all scenarios, but other factors like location, device, and user vary (see Table 2 in the paper). The current repository pertains to Scenario D. Within the repository, for each application, there is a compressed file containing the relevant PCAP files. The PCAP files follow the naming convention: {Application Name}{Scenario ID}{#Trace}_Final.pcap.

Context

Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.3k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 66k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs.

Content

Rico was built by mining Android apps at runtime via human-powered and programmatic exploration. Like its predecessor ERICA, Rico’s app mining infrastructure requires no access to — or modification of — an app’s source code. Apps are downloaded from the Google Play Store and served to crowd workers through a web interface. When crowd workers use an app, the system records a user interaction trace that captures the UIs visited and the interactions performed on them. Then, an automated agent replays the trace to warm up a new copy of the app and continues the exploration programmatically, leveraging a content-agnostic similarity heuristic to efficiently discover new UI states. By combining crowdsourcing and automation, Rico can achieve higher coverage over an app’s UI states than either crawling strategy alone. In total, 13 workers recruited on UpWork spent 2,450 hours using apps on the platform over five months, producing 10,811 user interaction traces. After collecting a user trace for an app, we ran the automated crawler on the app for one hour.

Acknowledgements

UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN https://interactionmining.org/rico

Inspiration

The Rico dataset is large enough to support deep learning applications. We trained an autoencoder to learn an embedding for UI layouts, and used it to annotate each UI with a 64-dimensional vector representation encoding visual layout. This vector representation can be used to compute structurally — and often semantically — similar UIs, supporting example-based search over the dataset. To create training inputs for the autoencoder that embed layout information, we constructed a new image for each UI capturing the bounding box regions of all leaf elements in its view hierarchy, differentiating between text and non-text elements. Rico’s view hierarchies obviate the need for noisy image processing or OCR techniques to create these inputs.

Nowadays, mobile applications (a.k.a., apps) are used by over two billion users for every type of need, including social and emergency connectivity. Their pervasiveness in today world has inspired the software testing research community in devising approaches to allow developers to better test their apps and improve the quality of the tests being developed. In spite of this research effort, we still notice a lack of empirical analyses aiming at assessing the actual quality of test cases manually developed by mobile developers: this perspective could provide evidence-based findings on the future research directions in the field as well as on the current status of testing in the wild. As such, we performed a large-scale empirical study targeting 1,780 open-source Android apps and aiming at assessing (1) the extent to which these apps are actually tested, (2) how well-designed are the available tests, and (3) what is their effectiveness. The key results of our study show that mobile developers still tend not to properly test their apps, possibly because of time to market requirements. Furthermore, we discovered that the test cases of the considered apps have a low (i) design quality, both in terms of test code metrics and test smells, and (ii) effectiveness when considering code coverage as well as assertion density.

The number of social media users in Saudi Arabia was forecast to continuously increase between 2024 and 2029 by in total six million users (+28.05 percent). After the ninth consecutive increasing year, the social media user base is estimated to reach 27.42 million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of social media users in countries like Israel and Kuwait.

The energy consumption of Android devices, measured via data collection from features, is a recurring theme in the literature. To evaluate the performance of such devices, databases are generated by collecting data from features while using the Android operating system. This is a database generated using Tucandeira Data Collector from the daily use of smartphones and tablets while performing everyday tasks. The dataset contains 98 features and 10,331,114 records related to dynamic, background, list of applications, and static data. Device records were collected daily from ten distinct devices and stored in CSV files that were later organized to generate a database by cleaning and preprocessing the data that are publically available in the Mendeley Data Repository. The dataset formed an integral component of the SWPERFI RD&I Project, a research, development, and innovation initiative aimed at improving the performance and energy optimization of mobile devices. This project was undertaken at the Federal University of Amazonas.

This comprehensive iOS application reviews dataset contains thousands of authentic user reviews from the Apple App Store in English. The dataset provides valuable insights for app developers, marketers, and researchers studying mobile application performance and user sentiment.

Key Features:

• Real user reviews from popular iOS apps
• Star ratings from 1 to 5 stars
• Review dates and timestamps
• App store URLs and metadata
• User demographics and location data
• App version information
• Review titles and detailed feedback

Applications: Perfect for sentiment analysis, app store optimization, mobile app development research, user experience studies, and competitive analysis. This dataset enables businesses to understand user preferences, identify app improvement opportunities, and develop better mobile applications.

Data Quality: All reviews are genuine user feedback collected from the official Apple App Store, ensuring authenticity and reliability for research and business intelligence purposes. The dataset covers various app categories including fitness, shopping, education, entertainment, and productivity applications.

Apple App Store Key StatisticsApps & Games in the Apple App StoreApps in the Apple App StoreGames in the Apple App StoreMost Popular Apple App Store CategoriesPaid vs Free Apps in Apple App...

Intending to cover the existing gap regarding behavioral datasets modelling interactions of users with individual a multiple devices in Smart Office to later authenticate them continuously, we publish the following collection of datasets, which has been generated after having five users interacting for 60 days with their personal computer and mobile devices. Below you can find a brief description of each dataset.Dataset 1 (2.3 GB). This dataset contains 92975 vectors of features (8096 per vector) that model the interactions of the five users with their personal computers. Each vector contains aggregated data about keyboard and mouse activity, as well as application usage statistics. More info about features meaning can be found in the readme file. Originally, the number of features of this dataset was 24 065 but after filtering the constant features, this number was reduced to 8096. There was a high number of constant features to 0 since each possible digraph (two keys combination) was considered when collecting the data. However, there are many unusual digraphs that the users never introduced in their computers, so these features were deleted in the uploaded dataset.Dataset 2 (8.9 MB). This dataset contains 61918 vectors of features (15 per vector)that model the interactions of the five users with their mobile devices. Each vector contains aggregated data about application usage statistics. More info about features meaning can be found in the readme file.Dataset 3 (28.9 MB). This dataset contains 133590vectors of features (42 per vector)that model the interactions of the five users with their mobile devices. Each vector contains aggregated data about the gyroscope and Accelerometer sensors.More info about features meaning can be found in the readme file.Dataset 4 (162.4 MB). This dataset contains 145465vectors of features (241 per vector)that model the interactions of the five users with both personal computers and mobile devices. Each vector contains the aggregation of the most relevant features of both devices. More info about features meaning can be found in the readme file.Dataset 5 (878.7 KB). This dataset is composed of 7 datasets. Each one of them contains an aggregation of feature vectors generated from the active/inactive intervals of personal computers and mobile devices by considering different time windows ranging from 1h to 24h.1h: 4074 vectors2h: 2149 vectors3h: 1470 vectors4h: 1133 vectors6h: 770 vectors12h: 440 vectors24h: 229 vectors

The dataset contains year, month and payment application-wise UPI Apps Transaction Statistics like Customer Initiated Transactions, B2C Transactions, B2B Transactions and On-us Transactions Note: 1) Unified Payments Interface(UPI) is an instant real-time payment system developed by National Payments Corporation of India. The interface facilitates inter-bank peer-to-peer and person-to-merchant transactions 2) From January 2021 onwards, ‚On-us Transactions‚ in UPI that are not processed and settled through the UPI Central System is shown under ‚ On-us Transactions column 3) Apps which has volume less than 10,000 is included under‚ Other Apps. 4) App volume in table is basis the Payer App logic, i.e the financial transaction is attributed to the PSP in UPI on the Payer's side. 5) BHIM Volume is inclusive of *99# volume. 6) For WhatsApp, Maximum registered user base of hundred (100) million in UPI