# Dataset Preview
To give you a preview of the Amazon Food Reviews dataset, I’ll provide a brief example of the data structure based on common fields included in the dataset. Here’s what a preview might look like:
Description of Columns
1.Id: A unique identifier for each review.
2.Product Id: The Amazon product ID for the reviewed food item.
3.User Id: A unique identifier for the user who wrote the review.
4.Profile Name: The name of the reviewer.
5.Helpfulness: A ratio showing how many users found the review helpful (e.g., 1/1 = 1 out of 1 users).
6.Score: The rating given by the reviewer, ranging from 1 (worst) to 5 (best).
7.Time: The timestamp when the review was written, usually in Unix epoch format.
8.Text: The detailed review text written by the user.
Key Insights from the Preview
1.Reviews contain both quantitati…
🍴 Amazon Food Reviews Dataset: A Comprehensive Overview 🍴
The Amazon Food Reviews Dataset is a rich resource containing customer feedback on a wide range of food products sold on Amazon. This dataset is widely used in natural language processing (NLP), sentiment analysis, and machine learning applications due to its detailed text reviews and accompanying metadata.
📂 Dataset Features
The dataset includes the following key fields:
Id: A unique identifier for each review.
ProductId: The unique identifier of the food product reviewed.
UserId: An identifier for the user who posted the review.
ProfileName: The name of the user, offering context about the reviewer.
Helpfulness: A score indicating how helpful other users found the review (e.g., "2/3" means 2 out of 3 people found the review helpful).
Score: The product rating given by the reviewer on a scale of 1 to 5, with 5 being the highest.
Time: A timestamp for when the review was posted, stored as a Unix epoch time.
Summary: A shor…
