Recipe Reviews and Sentiment Interactions

Columns

• ID: Sequential identifier for the record.
• recipe_number: Unique identifier for recipes.
• recipe_code: Code associated with the recipe.
• recipe_name: Name of the recipe (e.g., Cheeseburger Soup, Creamy White Chili).
• comment_id: Unique identifier for review comments.
• user_id: Unique identifier for users.
• user_name: Name of the user.
• user_reputation: Internal user reputation score.
• created_at: Date and time of creation (Unix timestamp).
• reply_count: Number of replies received.
• thumbs_up: Count of up-votes received.
• thumbs_down: Count of down-votes received.
• stars: Users' sentiment quantified on a 1 to 5-star rating scale.
• best_score: Best overall score associated with the entry.
• text: Text content of the recipe or comment.

Distribution

• Format: CSV (Recipe Reviews and User Feedback Dataset.csv)
• Size: 6.07 MB
• Structure: 18,200 valid rows with 15 columns.
• Data Quality: 100% valid entries with zero missing or mismatched values across key columns.

Usage

• Sentiment Analysis: Evaluating user satisfaction and emotional response to specific recipes.
• User Behaviour Analysis: Understanding engagement patterns through voting and commenting history.
• Recipe Recommendation Systems: Building algorithms to suggest dishes based on user ratings and reputation.
• Text Classification: Categorising feedback based on the textual content of reviews.

Coverage

• Temporal Scope: The timestamps indicate activity spanning from roughly early 2021 to late 2022 (Unix timestamps approx. 1.61b to 1.67b).
• Content Scope: Contains data on 100 unique recipes, with significant focus on popular items like 'Cheeseburger Soup' and 'Creamy White Chili'.
• Demographic Scope: Includes interactions from over 13,800 unique users, tracked via user IDs and reputation scores.

License

Who Can Use It

• Data Scientists: For training models on sentiment analysis and recommendation logic.
• Market Analysts: To study trends in culinary preferences and user engagement.
• Culinary Researchers: Investigating the relationship between recipe types and user feedback.
• Application Developers: Creating food-focused platforms requiring initial user interaction data.

Dataset Name Suggestions

• Culinary User Feedback and Ratings
• Recipe Reviews and Sentiment Interactions
• Gastronomic Engagement Metrics
• User Recipe Comment and Rating Logs

Attributes