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Netflix App User Feedback
Entertainment & Media Consumption
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset provides insights into user feedback for the Netflix application on the Google Play Store. It captures valuable information about reviews, user ratings, the date each review was posted, and the number of likes received by individual reviews. This data is relevant for understanding app performance, user sentiment, and overall user satisfaction.
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Columns
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reviewId: A unique identifier for each review.
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id: Another unique identifier associated with the review or user.
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userName: The name of the user who submitted the review. This includes categories such as 'A Google user', 'Gaurav Parikh', and 'Other' users.
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reviewer: The entity or person conducting the review.
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content: The textual body of the user's review.
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review: A direct reference to the content of the review.
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score: The numerical rating given by the user, ranging from 1 to 5.
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rating: A direct reference to the user's numerical score.
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thumbsUpCount: The number of 'likes' or positive reactions a review has received.
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likes on review: A direct reference to the count of positive reactions.
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reviewCreatedVersion: The specific version of the app when the review was created. Examples include a specific build number or 'Other' versions.
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appVersion: A direct reference to the app version at the time of review.
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at: The date and time when the review was published.
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date of review: A direct reference to the publication date of the review.
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Distribution
The dataset is typically provided in a CSV file format. It is structured semi-formally, with distinct columns for various review attributes. While an exact total row count is not specified, various attribute counts indicate a substantial volume of records. For instance, there are 137,668 unique user names and data points for scores and likes that sum to over 130,000 entries. The dataset is regularly updated, with specific counts available for different ranges of likes and review dates.
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Usage
This dataset is ideal for:
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App review analysis: Understanding user experiences and identifying common themes in feedback.
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Sentiment analysis: Gauging overall public sentiment towards the Netflix app.
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App performance tracking: Monitoring changes in ratings and review volume over time.
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Natural Language Processing (NLP) tasks: Developing and training models for text analysis and topic extraction from reviews.
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Market research: Gaining insights into user preferences and satisfaction trends in entertainment apps.
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Coverage
The dataset has a global geographic scope. It covers reviews from Netflix users on the Google Play Store, providing a demographic insight into app users on this platform. The time range for the reviews spans from September 12, 2018, to July 1, 2025, indicating a wide historical and forward-looking data capture.
License
CC BY-NC-SA
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Who Can Use It
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Data Scientists and AI/ML Developers: For building and training models related to sentiment analysis, topic modelling, and user behaviour prediction.
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App Developers and Product Managers: To gain insights into user feedback for product improvement and feature development.
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Market Researchers: For understanding consumer trends and market perception of streaming services.
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Academics and Researchers: For studies on user engagement, app store dynamics, and digital media consumption.
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Dataset Name Suggestions
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Netflix Google Play Reviews
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Netflix App User Feedback
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Google Play Netflix Ratings
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Netflix Reviews Dataset (Daily Updated)
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Attributes
Original Data Source: Netflix Reviews [DAILY UPDATED]
QUALITY