Tinder App Historical Ratings and Feedback

Columns

• review_id: Unique identifier for the specific review.
• pseudo_author_id: Anonymized identifier for the review author.
• author_name: Display name of the reviewer (frequently listed as 'A Google user').
• review_text: The textual content of the user's feedback and opinion.
• review_rating: Numerical rating provided by the user, ranging from 1 to 5.
• review_likes: The number of other users who liked the review.
• author_app_version: The specific version of the Tinder application installed at the time of the review.
• review_timestamp: The date and time (UTC) when the review was submitted.

Distribution

• Format: Tabular CSV file.
• Size: Approximately 122.88 MB.
• Structure: 9 columns.
• Volume: Contains roughly 604,000 valid records (rows).

Usage

• Sentiment Analysis: Extracting and classifying user emotions and trends over time.
• Version Analysis: Identifying which application versions received the most positive or negative feedback.
• Topic Modelling: Detecting common pain points, bugs, or requested features within the text.
• NLP Training: Training Natural Language Processing models on informal, user-generated text.
• Market Research: Understanding long-term trends in the online dating market.

Coverage

• Time Range: 16 July 2013 to 17 November 2023.
• Geographic/Demographic: Global users of the Tinder application on the Google Store (Android ecosystem).
• Data Notes: The author_app_version column has approximately 21% missing values. The review_text column is 100% valid with a small percentage of missing entries.

License

Who Can Use It

• Data Scientists working on NLP and sentiment classification.
• Product Managers analysing app lifecycle and user feedback loops.
• Market Analysts studying the dating app economy.
• Sociologists researching online relationship formation and digital communication patterns.

Dataset Name Suggestions

• Tinder: A Decade of Google Store User Sentiment
• 10 Years of Tinder Android Reviews
• Tinder App Historical Ratings and Feedback
• Longitudinal Tinder User Sentiment Data

Attributes