[ADVISORY] IMPORTANT

Instructions for citation:

If you use this dataset anywhere in your work, kindly cite as the below: L. Gupta, "Google Play Store Apps," Feb 2019. [Online]. Available: https://www.kaggle.com/lava18/google-play-store-apps

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!

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.

Google Play Store dataset to explore detailed information about apps, including ratings, descriptions, updates, and developer details. Popular use cases include app performance analysis, market research, and consumer behavior insights.

Use our Google Play Store dataset to explore detailed information about apps available on the platform, including app titles, developers, monetization features, user ratings, reviews, and more. This dataset also includes data on app descriptions, safety measures, download counts, recent updates, and compatibility, providing a complete overview of app performance and features.

Tailored for app developers, marketers, and researchers, this dataset offers valuable insights into user preferences, app trends, and market dynamics. Whether you're optimizing app development, conducting competitive analysis, or tracking app performance, the Google Play Store dataset is an essential resource for making data-driven decisions in the mobile app ecosystem.

Dataset Features

• url: The URL link to the app’s detail page on the Google Play Store.
• title: The name of the application.
• developer: The developer or company behind the app.
• monetization_features: Information regarding how the app generates revenue (e.g., in-app purchases, ads).
• images: Links or references to images associated with the app.
• about: Details or a summary description of the app.
• data_safety: Information regarding data safety and privacy practices.
• rating: The overall rating of the app provided by its users.
• number_of_reviews: The total count of user reviews received.
• star_reviews: A breakdown of reviews by star ratings.
• reviews: Reviews and user feedback about the app.
• what_new: Information on the latest updates or features added to the app.
• more_by_this_developer: Other apps by the same developer.
• content_rating: The content rating which guides suitability based on user age.
• downloads: The download count or range indicating the app’s popularity.
• country: The country associated with the app listing.
• app_category: The category or genre under which the app is classified.

Distribution

• Data Volume: 17 Columns and 65.54M Rows
• Format: CSV

Usage

This dataset is ideal for a variety of applications:

• App Market Analysis: Enables market researchers to extract insights on app popularity, engagement, and trends across different categories.
• Machine Learning: Can be used by data scientists to build recommendation engines or sentiment analysis models based on app review data.
• User Behavior Studies: Facilitates academic or industrial research into user preferences and behavior with respect to mobile applications.

Coverage

• Geographic Coverage: global.

License

CUSTOM Please review the respective licenses below: 1. Data Provider's License - Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: To train machine learning models for app popularity prediction, sentiment analysis, or recommendation systems.
• Researchers: For academic or scientific studies into market trends, consumer behavior, and app performance analysis.
• Businesses: For strategic analysis, developing market insights, or enhancing app development and user engagement strategies.

Suggested Dataset Name

1. Play store Insights
2. Android App Scope
3. Market Analytics
4. Play Store Metrics Vault

5. AppTrend360: Google Play Edition

Pricing

Based on Delivery frequency

~Up to $0.0025 per record. Min order $250

Approximately 10M new records are added each month. Approximately 13.8M records are updated each month. Get the complete dataset each delivery, including all records. Retrieve only the data you need with the flexibility to set Smart Updates.

• Monthly

New snapshot each month, 12 snapshots/year Paid monthly

• Quarterly

New snapshot each quarter, 4 snapshots/year Paid quarterly

• Bi-annual

New snapshot every 6 months, 2 snapshots/year Paid twice-a-year

• One-time purchase

New snapshot one-time delivery Paid once

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 dataset is a collection of user reviews for various Google Apps available on the Play Store. It provides detailed insights into user feedback, ratings, and engagement with different applications. The dataset's primary purpose is to offer a rich resource for understanding user sentiment, identifying app performance issues, and tracking user satisfaction over time. It is a valuable asset for analytics and natural language processing tasks related to app reviews.

Columns

• reviewId: A unique identifier for each individual user review.
• userName: The name of the user who submitted the review.
• userImage: The URL pointing to the user's profile image.
• content: The textual review provided by the user about the app.
• score: The numerical rating given by the user for the app, typically on a scale of 1 to 5.
• thumbsUpCount: The total number of likes or "thumbs up" received by that specific review.
• reviewCreatedVersion: The version of the app that was being reviewed at the time the review was created.
• at: The date and time when the user's review was created.
• replyContent: The textual content of the reply provided by the app developer to the user's review. A significant portion of reviews do not have a developer reply.
• repliedAt: The date and time when the developer's reply was issued. Many entries in this column are null, indicating no developer response.

Distribution

The dataset contains over 90,000 app reviews. The score column shows a distribution across ratings, with substantial counts for scores like 1.00-1.20, 2.00-2.20, 3.00-3.20, 4.00-4.20, and 4.80-5.00. For thumbsUpCount, the majority of reviews have a relatively low number of likes (0-720), but there are instances with significantly higher counts, reaching up to over 14,000 likes. The reviewCreatedVersion column shows a variety of app versions, with some being more frequently reviewed than others. Review creation dates span a period from April 2014 to February 2021, with a notable increase in review volume towards the later years, particularly between May 2020 and February 2021.

Usage

This dataset is ideal for: * Sentiment analysis of app reviews. * Natural Language Processing (NLP) tasks, such as topic modelling, text classification, and entity recognition. * App performance monitoring and identifying user pain points. * Market research on user satisfaction and trends in app usage. * Developing AI and Machine Learning models for predicting app ratings or automatically classifying feedback.

Coverage

The dataset offers global coverage for app reviews. The time range for review creation spans from 10th April 2014 to 4th February 2021. While developer replies are included, the data on repliedAt primarily indicates a single latest date (4th February 2021) with the majority being null, suggesting that developer reply timestamps are not as broadly distributed across the dataset as review creation times.

License

CC0

Who Can Use It

• App Developers: To understand user feedback, identify bugs, and improve app features.
• Data Analysts: For trends analysis, user behaviour insights, and reporting.
• Researchers: In fields like computer science, internet studies, and data analytics for academic studies on online reviews.
• Machine Learning Engineers: To train models for sentiment analysis, user support automation, or content moderation.
• Product Managers: To gather insights for product iteration and strategic planning.

Dataset Name Suggestions

• Google Play Store App Reviews
• Play Store User Feedback
• Google Apps Ratings and Reviews
• Mobile App Review Data
• Playstore Review Analytics Data

Attributes

Original Data Source: Google Apps Playstore Reviews

Get access to information about all apps in the Google Playstore to understand your competitors, market to app developers etc. This dataset includes all the fields available in the play store such as:

• Name, description, rating information etc.
• Technical information such as size, app version etc.
• Permissions.
• Developer information.
• Contact information.
• Parsed app-ads.txt information for publisher domains.
• Reviews (more than 100 million reviews available)

Context

App Permission data of 2.2 million android applications from Google Play store. Backup repo: https://github.com/gauthamp10/android-permissions-dataset

Content

I've collected the data with the help of Python and Scrapy running on a cloud virtual machine with the United States as geolocation. The data was collected on June 2021.

Also checkout:

• Google Play Store Apps dataset: https://www.kaggle.com/gauthamp10/google-playstore-apps
• Apple AppStore Apps dataset: https://www.kaggle.com/gauthamp10/apple-appstore-apps

Acknowledgements

I couldn't have build this dateset without the help of Digitalocean and github. Switched to facundoolano/google-play-scraper for sane reasons.

Inspiration

Took inspiration from: https://www.kaggle.com/gauthamp10/google-playstore-apps to build a big database for students and researchers who are interested to analyze and find insights on mobile application privacy.

Author

Gautham Prakash

My other projects: github.com/gauthamp10

Website: gauthamp10.github.io

Analysis of ‘Playstore Analysis’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/madhav000/playstore-analysis on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Google Play Store team had launched a new feature wherein, certain apps that are promising, are boosted in visibility. The boost will manifest in multiple ways including higher priority in recommendations sections (“Similar apps”, “You might also like”, “New and updated games”). These will also get a boost in search results visibility. This feature will help bring more attention to newer apps that have the potential.

Analysis to be done:

The problem is to identify the apps that are going to be good for Google to promote. App ratings, which are provided by the customers, is always a great indicator of the goodness of the app. The problem reduces to: predict which apps will have high ratings.

Problem Statement:

Google Play Store team is about to launch a new feature wherein, certain apps that are promising, are boosted in visibility. The boost will manifest in multiple ways including higher priority in recommendations sections (“Similar apps”, “You might also like”, “New and updated games”). These will also get a boost in search results visibility. This feature will help bring more attention to newer apps that have the potential.

Content:

Dataset: Google Play Store data (“googleplaystore.csv”)

Fields in the data: App: Application name Category: Category to which the app belongs Rating: Overall user rating of the app Reviews: Number of user reviews for the app Size: Size of the app Installs: Number of user downloads/installs for the app Type: Paid or Free Price: Price of the app Content Rating: Age group the app is targeted at - Children / Mature 21+ / Adult Genres: An app can belong to multiple genres (apart from its main category). For example, a musical family game will belong to Music, Game, Family genres. Last Updated: Date when the app was last updated on Play Store Current Ver: Current version of the app available on Play Store Android Ver: Minimum required Android version

--- Original source retains full ownership of the source dataset ---

This paper introduces GLARE an Arabic Apps Reviews dataset collected from Saudi Google PlayStore. It consists of 76M reviews, 69M of which are Arabic reviews of 9,980 Android Applications. We present the data collection methodology, along with a detailed Exploratory Data Analysis (EDA) and Feature Engineering on the gathered reviews. We also highlight possible use cases and benefits of the dataset.

Analysis of ‘Google Play Store Category wise Top 500 Apps’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/shakthidhar/google-play-store-category-wise-top-500-apps on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

Google Play stores top 500 app data based on their rankings on January 2022 for all the available categories. Link to scraping code: https://github.com/Shakthi-Dhar/AppPin Link to backup datafiles: github data files

Content

The dataset contains the top 500 android apps available on the google play store for the following categories: All Categories, Art & Design, Auto & Vehicles, Beauty, Books & Reference, Business, Comics, Communication, Education, Entertainment, Events, Finance, Food & Drink, Health & Fitness, House & Home, Libraries & Demo, Lifestyle, Maps & Navigation, Medical, Music & Audio, News & Magazines, Parenting, Personalization, Photography, Productivity, Shopping, Social, Sports, Tools, Travel & Local, and Video Players & Editors.

The app rankings are based on google play store app rankings for January 2022.

Abbreviations

In Review and Downloads, the alphabet T, L, Cr represents Thousands, Lakhs, Crores as per the google play store naming convention. They are similar to M, B which represent millions, billions. 1L (1 Lakh) = 100T (100 Thousand) 10L (10 Lakhs) = 1M (1 Million) 1Cr( 1 Crore) = 10M (10 Million)

Acknowledgements

This data is not provided directly by Google, so I used Appium an automation tool with python to scrape the data from the google play store app.

Inspiration

Inspired by Fortune500. Fortune500 provides data on top companies in the world, so why not have a data source for top apps in the world.

--- Original source retains full ownership of the source dataset ---

Reviews on Messengers Dataset - Review dataset

The Reviews on Messengers Dataset is a comprehensive collection of 200 the most recent customer reviews on 6 messengers obtained from the popular app store, Google Play. See the list of the apps below. This dataset encompasses reviews written in 5 different languages: English, French, German, Italian, Japanese.

  💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request… See the full description on the dataset page: https://huggingface.co/datasets/TrainingDataPro/messengers-reviews-google-play.

This dataset was constructed from an analysis of about 1.5 million apps from Google Play to identify a set of common libraries, to facilitate Android app analysis. It contains 1,113 libraries supporting common functionalities and 240 libraries for advertisement.

This repository contains the dataset for our study "A Large-Scale Empirical Study of Android Sports Apps in the Google Play Store" and this will help to replicate our study, also the replication package to direct you to help replicate it for your dataset too.

Note: The dataset given are protected with password, and the password is available in our published paper

The amount of Android apps available for download is constantly increasing, exerting a continuous pressure on developers to publish outstanding apps. Google Play (GP) is the default distribution channel for Android apps, which provides mobile app users with metrics to identify and report apps quality such as rating, amount of downloads, previous users comments, etc. In addition to those metrics, GP presents a set of top charts that highlight the outstanding apps in different categories. Both metrics and top app charts help developers to identify whether their development decisions are well valued by the community. Therefore, app presence in these top charts is a valuable information when understanding the features of top-apps. In this paper we present Hall-of-Apps, a dataset containing top charts' apps metadata extracted (weekly) from GP, for 4 different countries, during 30 weeks. The data is presented as (i) raw HTML files, (ii) a MongoDB database with all the information contained in app's HTML files (e.g., app description, category, general rating, etc.), and (iii) data visualizations built with the D3.js framework. A first characterization of the data along with the urls to retrieve it can be found in our online appendix: https://thesoftwaredesignlab.github.io/hall-of-apps-tools/

This dataset contains some stats about google play store app.

There's a story behind every dataset and here's your opportunity to share yours. Based on installs, reviews you can sort out the apps. A clear picture can be drawn of apps, you can find out apps of what category are the most expensive, most popular, have most installs. Also various comparison can be done based on the data given in the dataset.

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...

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

This dataset contains 76,535 real user reviews collected from the Google Play Store across seven popular music streaming applications: Spotify, Apple Music, SoundCloud, TIDAL, Deezer, Shazam, and Google Play Music.

Each review includes: * 🌐 The app name * 📝 The review content * ⭐ A star rating from 1 to 5 * 📱 The app version (if available) * 📅 The date the review was written

This dataset is cleaned (empty or invalid entries removed) but intentionally unaltered in tone, preserving user expressions (including slang, emojis, and punctuation). Total entries: 76,535 Language: Primarily English Date range: Varied (depending on app)

This data set contains meta data about more than a million third-party Android application that were collected from the Google Play store between 2017 and 2019. Two privacy scores were calculated for each application based on: permission requests, broadcast receivers, and user's privacy preferences. The scores also depend on other applications in the app's category. The scores were calculated based on two published formulas. The first fomulas was proposed by Mohsel et al. and published in TrustCom '18, "Countering intrusiveness using new security-centric ranking algorithm built on top of elasticsearch". The second formula was published in SPSM '16 Taylor, and Martinovic, "SecuRank: Starving Permission-Hungry Apps Using Contextual Permission Analysis"s

Dataset

This dataset was created by Ashish Patel

Contents