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US App Store Public Perception Dataset
Reviews & Ratings
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset provides a detailed collection of Apple App Store reviews for a selection of popular mobile applications, including Uber, Waze, Facebook, Spotify, Netflix, Pinterest, X (formerly Twitter), TikTok, Tinder, and Instagram. It offers insights into public perception and user feedback over several years. The data can be used to extract sentiments and trends, identify app versions that received the most positive or negative feedback, and apply topic modelling to pinpoint common user pain points within these applications.
Columns
- index: A unique identifier for each review record.
- date: The specific date when the review was submitted.
- pseudoUserId: A pseudonymous identifier for the user who submitted the review.
- userName: The username of the reviewer.
- title: The title provided by the user for their review.
- review: The full text content of the user's review.
- rating: The numerical rating given by the user, typically on a scale of 1 to 5.
- isEdited: A boolean indicator showing whether the review has been edited (true) or not (false).
- country: The country from which the review originated.
- applicationName: The name of the application being reviewed.
Distribution
The dataset is typically provided in a CSV format. It contains approximately 174,508 records (rows) covering reviews for various applications. The distribution of ratings is as follows: 47,192 reviews are rated between 1.00 and 1.08, 15,217 between 1.96 and 2.04, 17,774 between 3.00 and 3.08, 18,183 between 3.96 and 4.04, and 76,143 between 4.92 and 5.00. The majority of reviews (173,886) are unedited, while 623 reviews have been marked as edited. The number of unique user IDs is 121,028, and there are 173,654 unique usernames.
Usage
This dataset is ideal for:
- Sentiment analysis to gauge public opinion on specific app features or overall user satisfaction.
- Trend analysis to observe changes in user feedback over time for different app versions.
- Identifying pain points through topic modelling of review content, helping app developers prioritise improvements.
- Market research to understand user needs and competitive landscapes within the mobile app ecosystem.
Coverage
The dataset primarily covers Apple App Store reviews from the US region. The reviews span a time period from September 2017 to November 2023.
License
CC0
Who Can Use It
- Data Scientists and Analysts: For building predictive models, performing natural language processing (NLP) tasks, and extracting actionable insights from unstructured text data.
- App Developers and Product Managers: To understand user feedback, identify bugs, and inform future product development and updates.
- Market Researchers: For competitive analysis, understanding user sentiment towards specific apps, and identifying market opportunities.
- Academics and Students: For research projects related to user experience, mobile app trends, and sentiment analysis.
Dataset Name Suggestions
- Apple App Store User Reviews (US)
- Mobile Application Feedback Dataset
- App Store Review Data (2017-2023)
- US App Store Public Perception Data
Attributes
Original Data Source: 🏆Uber, FB, Waze, etc US Apple App Store Reviews
QUALITY