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Synthetic Restaurant Lifecycle Reviews
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About
Offers a detailed look into how fictional dining establishments evolve across their lifecycles. The data tracks shifts in customer sentiment, rating trajectories, service quality, and operational trends over time. Each establishment features a structured timeline of performance phases, such as ‘Opening Hype’ or ‘Decline’, with dozens of richly written, AI-generated reviews per phase. This dataset was created synthetically using OpenAI GPT-4.1 Nano to simulate realistic customer feedback over extended business cycles.
Columns
restaurant_id: A unique UUID used to identify the specific dining establishment to which the review belongs.review_period: A numeric integer representing the particular phase or time period within the restaurant's timeline (e.g., 1 = first phase).review_period_description: A string label detailing the specific phase of the restaurant’s lifecycle, such as "Opening Hype (Year 1)" or "Needs Overhaul (Year 4)". This is valuable for temporal and trend-based analysis.reviewer_type: A description of the reviewer's persona or context (e.g., “Local Food Blogger,” “Art Student on a Budget”). This is useful for simulating diverse reviewer demographics and behavioural modelling.review_text: The full text of the review, written in natural language. This text is rich with sentiment signals and is ideal for various Natural Language Processing tasks.rating: An integer rating, ranging from 1 to 5, which reflects the customer’s satisfaction level.
Distribution
The primary review data is available in both CSV (file size approximately 2.3 MB) and JSON formats. Additionally, an owner report JSON file summarising strengths, weaknesses, and trends for each establishment is included. The dataset currently contains 3,594 records across 6 columns, representing reviews for 135 unique restaurant IDs. The data is 100% AI-generated and was created using OpenAI’s GPT-4.1 Nano model.
Usage
This data product is suited for several advanced analytical applications:
- Sentiment analysis across distinct time periods.
- Time-series modelling to predict customer experience trends.
- Prediction of customer satisfaction and business decline/success.
- Review summarisation techniques.
- Use in Retrieval Augmented Generation (RAG) pipelines.
- Fine-tuning of Large Language Models (LLM) for dialogue or review generation tasks.
Coverage
Since the data is synthetically generated, it contains no real customer information and is designed purely for research and modelling purposes. The time scope focuses on simulating extended business cycles, structured into 18 distinct temporal performance phases. The data covers 135 fictional dining establishments.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: To build predictive models for customer experience and track performance changes across temporal slices.
- Machine Learning Engineers: For training and validating NLP models focused on sentiment classification and text generation.
- Academic Researchers: To study the dynamics of synthetic data generation and temporal sentiment analysis.
Dataset Name Suggestions
- Synthetic Restaurant Lifecycle Reviews
- Temporal Sentiment Trajectories Data
- AI-Generated Food Review Time Series
Attributes
Original Data Source:Synthetic Restaurant Lifecycle Reviews
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