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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.

This dataset encompasses a wide-ranging collection of Google Play applications, providing a holistic view of the diverse ecosystem within the platform. It includes information on various attributes such as the title, developer, monetization features, images, app descriptions, data safety measures, user ratings, number of reviews, star rating distributions, user feedback, recent updates, related applications by the same developer, content ratings, estimated downloads, and timestamps. By aggregating this data, the dataset offers researchers, developers, and analysts an extensive resource to explore and analyze trends, patterns, and dynamics within the Google Play Store. Researchers can utilize this dataset to conduct comprehensive studies on user behavior, market trends, and the impact of various factors on app success. Developers can leverage the insights derived from this dataset to inform their app development strategies, improve user engagement, and optimize monetization techniques. Analysts can employ the dataset to identify emerging trends, assess the performance of different categories of applications, and gain valuable insights into consumer preferences. Overall, this dataset serves as a valuable tool for understanding the broader landscape of the Google Play Store and unlocking actionable insights for various stakeholders in the mobile app industry.

Dataset Card for Macappstore Applications Metadata

Mac App Store Applications Metadata sourced by the public API.

Curated by: MacPaw Way Ltd.

Language(s) (NLP): Mostly EN, DE License: MIT

  Dataset Details

This data aims to cover our internal company research needs and start collecting and sharing the macOS app dataset since we have yet to find a suitable existing one. Full application metadata was sourced by the public iTunes search API for the US, Germany, and Ukraine… See the full description on the dataset page: https://huggingface.co/datasets/MacPaw/mac-app-store-apps-metadata.

Use the OpenWeb Ninja Google Play App Store Data API to access comprehensive data on Google Play Store, including Android Apps / Games, reviews, top charts, search, and more. Our extensive dataset provides over 40 app store data points, enabling you to gain deep insights into the market.

The App Store Data dataset includes all key app details:

App Name, Description, Rating, Photos, Downloads, Version Information, App Size, Permissions, Developer and Contact Information, Consumer Review Data.

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.

Dataset

This dataset was created by Aleksandr Krainov

Contents

Observed adoption of the App Store Data Renderer plugin across real WordPress sites in the WatsonWP index.

Dataset

This dataset was created by Feroz Shinwari

Released under CC0: Public Domain

Contents

masoodali/apple-app-store-labels-policies dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Card for Macappstore Applications Descriptions

Mac App Store Applications descriptions extracted from the metadata from the public API.

Curated by: MacPaw Way Ltd.

Language(s) (NLP): Mostly EN, DE License: MIT

  Dataset Details

This dataset is a combined and refined Mac App Store Applications Metadata dataset subset. The main idea behind its creation is to separate the description texts of the macOS apps for the convenience of further analysis.… See the full description on the dataset page: https://huggingface.co/datasets/MacPaw/mac-app-store-apps-descriptions.

This is the dataset used for paper: "A Recommender System of Buggy App Checkers for App Store Moderators", published on the International Conference on Mobile Software Engineering and Systems (MOBILESoft) in 2015.

Dataset Collection We built a dataset that consists of a random sample of Android app metadata and user reviews available on the Google Play Store on January and March 2014. Since the Google Play Store is continuously evolving (adding, removing and/or updating apps), we updated the dataset twice. The dataset D1 contains available apps in the Google Play Store in January 2014. Then, we created a new snapshot (D2) of the Google Play Store in March 2014.

The apps belong to the 27 different categories defined by Google (at the time of writing the paper), and the 4 predefined subcategories (free, paid, new_free, and new_paid). For each category-subcategory pair (e.g. tools-free, tools-paid, sports-new_free, etc.), we collected a maximum of 500 samples, resulting in a median number of 1.978 apps per category.

For each app, we retrieved the following metadata: name, package, creator, version code, version name, number of downloads, size, upload date, star rating, star counting, and the set of permission requests.

In addition, for each app, we collected up to a maximum of the latest 500 reviews posted by users in the Google Play Store. For each review, we retrieved its metadata: title, description, device, and version of the app. None of these fields were mandatory, thus several reviews lack some of these details. From all the reviews attached to an app, we only considered the reviews associated with the latest version of the app —i.e., we discarded unversioned and old-versioned reviews. Thus, resulting in a corpus of 1,402,717 reviews (2014 Jan.).

Dataset Stats Some stats about the datasets:

• D1 (Jan. 2014) contains 38,781 apps requesting 7,826 different permissions, and 1,402,717 user reviews.

• D2 (Mar. 2014) contains 46,644 apps and 9,319 different permission requests, and 1,361,319 user reviews.

Additional stats about the datasets are available here.

Dataset Description To store the dataset, we created a graph database with Neo4j. This dataset therefore consists of a graph describing the apps as nodes and edges. We chose a graph database because the graph visualization helps to identify connections among data (e.g., clusters of apps sharing similar sets of permission requests).

In particular, our dataset graph contains six types of nodes: - APP nodes containing metadata of each app, - PERMISSION nodes describing permission types, - CATEGORY nodes describing app categories, - SUBCATEGORY nodes describing app subcategories, - USER_REVIEW nodes storing user reviews. - TOPIC topics mined from user reviews (using LDA).

Furthermore, there are five types of relationships between APP nodes and each of the remaining nodes:

• USES_PERMISSION relationships between APP and PERMISSION nodes
• HAS_REVIEW between APP and USER_REVIEW nodes
• HAS_TOPIC between USER_REVIEW and TOPIC nodes
• BELONGS_TO_CATEGORY between APP and CATEGORY nodes
• BELONGS_TO_SUBCATEGORY between APP and SUBCATEGORY nodes

Dataset Files Info

Neo4j 2.0 Databases

googlePlayDB1-Jan2014_neo4j_2_0.rar

googlePlayDB2-Mar2014_neo4j_2_0.rar We provide two Neo4j databases containing the 2 snapshots of the Google Play Store (January and March 2014). These are the original databases created for the paper. The databases were created with Neo4j 2.0. In particular with the tool version 'Neo4j 2.0.0-M06 Community Edition' (latest version available at the time of implementing the paper in 2014).

Neo4j 3.5 Databases

googlePlayDB1-Jan2014_neo4j_3_5_28.rar

googlePlayDB2-Mar2014_neo4j_3_5_28.rar Currently, the version Neo4j 2.0 is deprecated and it is not available for download in the official Neo4j Download Center. We have migrated the original databases (Neo4j 2.0) to Neo4j 3.5.28. The databases can be opened with the tool version: 'Neo4j Community Edition 3.5.28'. The tool can be downloaded from the official Neo4j Donwload page.

  In order to open the databases with more recent versions of Neo4j, the databases must be first migrated to the corresponding version. Instructions about the migration process can be found in the Neo4j Migration Guide.

  First time the Neo4j database is connected, it could request credentials. The username and pasword are: neo4j/neo4j

Context

While many public datasets (on Kaggle and the like) provide Apple App Store data, there are not many counterpart datasets available for Google Play Store apps anywhere on the web. On digging deeper, I found out that iTunes App Store page deploys a nicely indexed appendix-like structure to allow for simple and easy web scraping. On the other hand, Google Play Store uses sophisticated modern-day techniques (like dynamic page load) using JQuery making scraping more challenging.

Content

Each app (row) has values for catergory, rating, size, and more.

Acknowledgements

This information is scraped from the Google Play Store. This app information would not be available without it.

Inspiration

The Play Store apps data has enormous potential to drive app-making businesses to success. Actionable insights can be drawn for developers to work on and capture the Android market!

Explore the Google Play Store Reviews Database, a comprehensive collection of user reviews for various apps available on the Google Play Store.

This dataset includes millions of reviews across a wide range of categories such as games, productivity, social media, finance, health, and more. Each review entry provides essential details, including app names, user ratings, review texts, review dates, and user feedback, offering valuable insights for developers, data analysts, and market researchers.

Key Features:

• Extensive Review Coverage: Contains millions of user reviews from the Google Play Store, covering various app categories like games, productivity, social media, finance, and health.
• Detailed Review Information: Each review includes key details such as app name, user rating, review text, review date, and user feedback, allowing for in-depth analysis of user sentiment and app performance.
• Ideal for Market Analysis: Perfect for developers, data scientists, and market researchers interested in analyzing user feedback, studying trends in app usage, or optimizing app development strategies based on user reviews.
• Rich Source of User Insights: Provides a comprehensive overview of user experiences and preferences, helping professionals stay updated on the latest trends, popular apps, and user satisfaction levels.

Whether you're analyzing user feedback, researching market trends, or developing new app strategies, the Google Play Store Reviews Database is an invaluable resource that provides detailed insights and extensive coverage of app reviews on the Google Play Store.

We built a crawler to collect data from the Google Play store including the application's metadata and APK files. The manifest files were extracted from the APK files and then processed to extract the features. The data set is composed of 870,515 records/apps, and for each app we produced 48 features. The data set was used to built and test two bootstrap aggregating of multiple XGBoost machine learning classifiers. The dataset were collected between April 2017 and November 2018. We then checked the status of these applications on three different occasions; December 2018, February 2019, and May-June 2019.

Context

We a group of students from a Data Science and Machine Learning Bootcamp in which we have decided to create an AI logo generator for our capstone project. We need huge amount of logos for training our deep learning model - DCGAN so we have scraped over 365k data from the Apple App Store to download the logos of the apps for training purposes.

GitHub Link of our Project - LOGO⅃ : https://github.com/jackychansky/Logo-Generator-by-DCGAN/blob/main/README.md

Content

We have used Rapid API to acquire the data we need and we have scraped over 10,000,000 apps infomation (link to the dataset: https://www.kaggle.com/fentyforte/365k-ios-apps-dataset) thus downloading all the logos from the logolink scraped from the dataset to create the current large logo dataset.

Don't forget to hit the upvote🙏🙏

🔖 Overview

The Google Play App Reviews dataset contains valuable feedback from users who have reviewed apps on the Google Play Store. This dataset includes both user ratings and detailed comments, making it ideal for sentiment analysis, user experience evaluation, and app performance research.

📚 Columns Description

📊 Key Features

• ✅ Rich Feedback: Includes both ratings and textual feedback from users.
• 🌍 Global Reach: Reviews are collected from users worldwide, providing diverse insights.
• 🔒 Anonymized Data: No personally identifiable information is included.
• ⚙️ Ready for Analysis: Cleaned and pre-processed for immediate use in sentiment analysis and app performance evaluation.

🎯 Potential Use Cases

• Sentiment Analysis: Analyze user sentiment based on reviews and ratings.
• Customer Feedback: Measure user satisfaction and discover areas for improvement.
• App Version Comparison: Evaluate how different versions of the app perform based on user feedback.
• Geographic Insights: Analyze regional differences in app usage and reviews.
• Developer Interaction: Assess the effectiveness of developer responses to user reviews.

🚀 Get Started!

Ready to dive into the world of app feedback and sentiment analysis? Explore the dataset, build models to understand user sentiments, and enhance app experiences based on real feedback.

Happy coding! ✨

This Dataset is the cleaned up version of the Google Play Store Data dataset , available on Kaggle. The EDA and data cleaning was performed using Python .

Context

To date (April 2020), Android is still the most popular mobile operating system in the world. Taking into account billion of Android users worldwide, mining this data has the potential to reveal user behaviors and trends in the whole global scope.

Content

There are 2 CSV files: - app.csv with 53,732 rows and 18 columns. - comment.csv with 1,468,173 rows and 4 columns.

The scraping was done in April 2020.

Acknowledgements

This dataset is obtained from scraping Google Play Store. Without Google and Android, this dataset wouldn’t have existed.

The dataset is first published in this blog.

Inspiration

Business trends on mobile can be explored by examining this dataset.

We crawled 90,000 app reviews from both Google Play Store and Apple App Store, including reviews from both free and paid apps. These reviews were filtered for explainability needs, and after this process, 4,495 reviews remained. Among them, 2,185 reviews indicated an explanation need, while 2,310 did not. This resulting gold standard dataset was used to train and evaluate several machine learning models and rule-based approaches for detecting explanation needs in app reviews.

The dataset includes both balanced and unbalanced evaluation sets, as well as the original crawled data from October 2023. In addition to machine learning approaches, rule-based methods optimized for F1 score, precision, and recall are also included.

We provide several pre-trained machine learning models (including BERT, SetFit, AdaBoost, K-Nearest Neighbor, Logistic Regression, Naive Bayes, Random Forest, and SVM) along with training scripts and evaluation notebooks. These models can be applied directly or retrained using the included datasets.

For further details on the structure and usage of the dataset, please refer to the README.md file within the provided ZIP archive.