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Car Review
Reviews & Ratings
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About
This dataset contains detailed user reviews and ratings for various car vehicles. Each entry captures a unique user review, including details on the vehicle model, user experiences, and overall satisfaction ratings. This dataset is ideal for analysing user sentiment, vehicle performance perceptions, and the features contributing to customer satisfaction for different Toyota models.
Dataset Features:
- User ID: Unique identifier for the user who provided the review.
- Review Date: Date and time when the review was posted, including timezone information.
- Author Name: Name of the user who submitted the review.
- Vehicle Title: Full title and model details of the Toyota vehicle being reviewed.
- Review Title: Title of the review that summarises the user’s primary opinion.
- Review: Full text of the user’s experience and feedback on the vehicle.
- Rating: A numerical rating provided by the user, typically ranging from 1 to 5, reflecting overall satisfaction.
Usage:
This dataset is valuable for training and testing models in natural language processing, sentiment analysis, and customer satisfaction prediction. Potential applications include:
- Analysing user sentiment trends across different car models.
- Identifying key factors in customer satisfaction for cars.
- Developing recommendation systems for potential buyers based on previous user feedback.
- Benchmarking vehicle performance and reliability over time based on user-reported experiences.
Coverage:
The dataset covers a range of user experiences and satisfaction levels with different models, providing insights into customer perceptions over several years. It encompasses reviews of minivans and sedans and various performance, reliability, and feature-specific feedback.
License:
CC0 (Public Domain)
Who can use it:
This dataset is intended for data scientists, machine learning practitioners, researchers, and automotive industry analysts interested in exploring consumer feedback and sentiment analysis.
How to use it:
- Build models to classify or predict vehicle satisfaction ratings.
- Conduct text analysis to understand common themes and issues in customer reviews.
- Explore the correlation between vehicle models and user satisfaction based on features and feedback.
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