Recipe Reviews and User Feedback
About
The "Recipe Reviews and User Feedback Dataset" is a comprehensive repository of data encompassing various aspects of recipe reviews and user interactions. It includes essential information such as the recipe name, its ranking on the top 100 recipes list, a unique recipe code, and user details like user ID, user name, and an internal user reputation score.
Each review comment is uniquely identified with a comment ID and comes with additional attributes, including the creation timestamp, reply count, and the number of up-votes and down-votes received. Users' sentiment towards recipes is quantified on a 1 to 5 star rating scale, with a score of 0 denoting an absence of rating.
This dataset is a valuable resource for researchers and data scientists, facilitating endeavors in sentiment analysis, user behavior analysis, recipe recommendation systems, and more. It offers a window into the dynamics of recipe reviews and user feedback within the culinary website domain.
Subject Area
Computer Science
Instances
18,182
Features
15
Data Types
Tabular
Tasks
Classification
Feature Types
Continuous, Categorical, Integer
Features
Name | Role | Type | Units | Missing Values | Description |
---|---|---|---|---|---|
num_records | Feature | Integer | - | No | |
recipe_number | Feature | Integer | - | No | |
recipe_code | Feature | Integer | - | No | |
recipe_name | Feature | Categorical | - | No | |
comment_id | Feature | Categorical | - | No | |
user_id | Feature | Categorical | - | No | |
user_name | Feature | Categorical | - | No | |
user_reputation | Feature | Integer | - | No | |
created_at | Feature | Integer | - | No | |
reply_count | Feature | Integer | - | No | |
thumbs_up | Feature | Integer | - | No | |
thumbs_down | Feature | Integer | - | No | |
stars | Feature | Integer | - | No | |
best_score | Feature | Integer | - | No | |
text | Feature | Categorical | - | Yes |
Introductory Paper
Textual Taste Buds: A Profound Exploration of Emotion Identification in Food Recipes through BERT and AttBiRNN Models
Amir Ali, Stanislaw Matuszewski, Jacek Czupyt, Usman Ahmad. 2023.
International Journal of Novel Research and Development