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App Store Ratings & Feedback
Reviews & Ratings
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset provides a collection of over 12,000 user reviews for various applications from an app store. It includes user-assigned ratings, which can be used to classify reviews as either positive or negative. The dataset is a valuable resource for conducting sentiment analysis tasks and can assist beginners in working with annotated, real-world data to understand user feedback on mobile applications. It serves as a foundation for exploring consumer sentiment and application performance insights.
Columns
- reviewId: A unique identifier assigned to each individual review.
- userName: The username of the person who submitted the review.
- userImage: The location of the image associated with the user who posted the review.
- content: The full text of the user's review.
- score: The rating given to the application by the user, ranging from 1 to 5, where a score of 5 indicates the most positive sentiment and 1 signifies the most negative.
- thumbsUpCount: The total number of users who have upvoted a particular review.
- reviewCreatedVersion: The specific version of the application that the review pertains to.
- at: The precise date and time when the review was originally posted.
- replyContent: Any reply provided to the original user review by the app developer or another party.
- repliedAt: The date and time when a reply to the review was posted.
Distribution
The dataset contains over 12,000 distinct reviews, with 12,495 unique review identifiers recorded. Ratings are distributed across the 1 to 5 scale, with significant counts for scores like 1.00-1.20 (2,506 reviews), 2.00-2.20 (2,344 reviews), 3.00-3.20 (1,991 reviews), 4.00-4.20 (2,775 reviews), and 4.80-5.00 (2,879 reviews). The number of upvotes (thumbsUpCount) for reviews spans a wide range, from 0 to 397. Many reviews (17%) do not specify a version, while '1.5.11' accounts for 4% of review versions. A substantial portion of reviews (53%) do not have a corresponding reply content. The data is typically provided in a CSV file format.
Usage
This dataset is ideally suited for a variety of analytical and machine learning applications. It is particularly useful for:
- Performing sentiment analysis to gauge public opinion on mobile applications.
- Developing and training natural language processing (NLP) models, such as BERT-based sentiment classifiers.
- Extracting key insights and trends from user feedback to inform app development and marketing strategies.
- Educating beginners in the field of sentiment analysis and text mining using annotated, real-world data.
- Analysing user engagement and the impact of replies on review visibility.
Coverage
The dataset offers a global scope, encompassing reviews from users worldwide. The time range for user-posted reviews extends from 8th February 2015 to 28th October 2020. Replies to reviews cover a slightly broader period, from 14th January 2013 to 28th October 2020. The data reflects feedback from real users of various app store applications, providing a diverse demographic perspective on mobile app usage and satisfaction.
License
CCO
Who Can Use It
This dataset is beneficial for a wide range of users, including:
- Data Scientists and Machine Learning Engineers: For building and evaluating sentiment analysis models, text classification systems, and other NLP applications.
- Researchers: To study user behaviour, app success factors, and the dynamics of online reviews.
- App Developers and Product Managers: To understand user feedback, identify pain points, and prioritise feature development based on sentiment.
- Market Analysts: To monitor brand perception, conduct competitor analysis, and track market trends in the app industry.
- Students: As an excellent practical resource for learning about data cleaning, text preprocessing, and sentiment analysis techniques.
Dataset Name Suggestions
- Google Play Store User Reviews
- Mobile App Sentiment Analysis Dataset
- App Store Ratings & Feedback
- Digital Product Review Data
- Consumer App Review Dataset
Attributes
Original Data Source: Google Play Store Reviews
QUALITY