Restaurant Reviews with Sentiment Labels for Food, Interior, and Service

47,139 user reviews about restaurants. Each review is tagged with a general sentiment and three aspect-specific sentiments for food, interior, and service.

Use Cases

• Train a text classification model to predict the general sentiment label from the review text.
• Perform multi-aspect sentiment analysis by comparing the food, interior, and service sentiment labels for each review.
• Analyze correlations between the general sentiment and the three specific aspect sentiments (food, interior, service) across the 47,139 reviews.

Strengths

• 47,139 user reviews provide a substantial text corpus for analysis.
• Each review includes four distinct sentiment labels: general, food, interior, and service.

Limitations

• The dataset's geographic and linguistic scope is not specified, potentially limiting generalizability.
• The source and method of sentiment labeling (e.g., automated vs. human) are unknown, which may affect label reliability.

Provenance

Source

Author 'blinoff' on Hugging Face.

Collection Method

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Time Range

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Freshness

Last updated on 2022-10-23.

Geography

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