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ChatGPT User Satisfaction Ratings
Reviews & Ratings
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset provides user reviews for ChatGPT, offering valuable qualitative feedback, satisfaction ratings, and submission dates. It captures a diverse array of user sentiments, from concise remarks to more detailed feedback. The ratings are provided on a scale of 1 to 5, indicating different levels of user satisfaction. The dataset spans several months, which allows for temporal analysis of sentiment trends, as each review includes a timestamp. This data is ideal for gaining insights into user characteristics and for improving application features and services.
Columns
- Review Id: A unique identifier for each individual review. This is formatted as a String, typically in a UUID structure.
- Review: The actual text of the user's feedback, offering qualitative insights into their experience with the application. This is a String data type.
- Ratings: User-submitted numerical ratings, ranging from 1 (lowest satisfaction) to 5 (highest satisfaction), indicating their level of contentment. This is an Integer data type.
- Review Date: The timestamp when the review was originally submitted, recorded in MM/DD/YYYY HH:MM format, serving as a Date_Time data type.
Distribution
The dataset is provided as a free resource. While a sample file will be updated separately to the platform, the data quality is assessed as 5 out of 5, with the current version being 1.0. It was listed on 08/06/2025, with 1 view and 0 downloads recorded so far. The dataset contains approximately 193,154 unique reviews.
Usage
This dataset is particularly useful for various analytical applications, including:
- Sentiment Analysis: Developing models to predict the emotional tone or sentiment conveyed in user reviews.
- Customer Feedback Analysis: Extracting actionable insights that can inform and guide improvements to application features and services.
- Review Classification: Building machine learning models to categorise user reviews, for instance, as positive or negative.
- Data Visualisation: Creating visual representations of review patterns and trends.
- Exploratory Data Analysis: Investigating the characteristics and underlying patterns within the review data.
- Natural Language Processing (NLP): Applying NLP techniques to understand and process the textual feedback.
- Text Mining: Discovering patterns and insights from the large collection of text reviews.
- Time-Series Analysis: Examining how sentiment and ratings evolve over time based on review timestamps.
Coverage
This dataset comprises user reviews for ChatGPT collected from 25th July 2023 to 24th August 2024. The data collection is global, reflecting feedback from users worldwide.
License
CCO
Who Can Use It
This dataset is ideal for a range of users interested in understanding user feedback and sentiment, including:
- Data Scientists and Machine Learning Engineers for building and training sentiment analysis and classification models.
- Product Managers and App Developers to gain actionable insights for product improvement and feature development.
- Market Researchers to understand user satisfaction and market perception of AI applications.
- Academic Researchers studying human-computer interaction, natural language processing, or user behaviour.
Dataset Name Suggestions
- ChatGPT User Reviews
- GPT User Review Sentiment Data
- AI App User Feedback Dataset
- ChatGPT User Satisfaction Ratings
Attributes
Original Data Source: ChatGPT Users Reviews
QUALITY