---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- cc0-1.0
multilinguality:
- monolingual
pretty_name: amazon-food-reviews-dataset
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- amazon
- reviews
- food reviews
- business
task_categories:
- text-classification
task_ids: []
---
# Dataset Card for "Amazon Food Reviews"

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:**
- **Repository:**
- **Paper:**
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

This dataset consists of reviews of fine foods from amazon. The data span a period of more than 10 years, including all ~500,000 reviews up to October 2012. Reviews include product and user information, ratings, and a plain text review. It also includes reviews from all other Amazon categories.


### Supported Tasks and Leaderboards

This dataset can be used for numerous tasks like sentiment analysis, text classification, and user behavior analysis. It's particularly useful for training models to understand customer feedback and preferences.


### Languages

The reviews are primarily in English.

## Dataset Structure

### Data Instances

A typical data instance comprises a review with fields like product ID, user ID, rating, review text, helpfulness votes, and time of the review.

### Data Fields

- `ProductId`: Unique identifier for the product
- `UserId`: Unique identifier for the user
- `ProfileName`: Profile name of the user
- `HelpfulnessNumerator`: Number of users who found the review helpful
- `HelpfulnessDenominator`: Number of users who indicated whether they found the review helpful or not
- `Score`: Rating between 1 and 5
- `Time`: Timestamp of the review
- `Summary`: Brief summary of the review
- `Text`: Text of the review

### Data Splits

The dataset is not split into standard training/validation/testing sets. Users may need to create these splits as per their requirement.


## Dataset Creation

### Curation Rationale

The dataset was created to provide a large collection of textual reviews with sentiment labels, useful for tasks in sentiment analysis and natural language processing.

### Source Data

#### Initial Data Collection and Normalization

The data was collected from Amazon's food reviews section.

#### Who are the source language producers?

The source language producers are the Amazon users / customers who provided these reviews.

### Annotations

#### Annotation process

The reviews come with ratings that can be converted into sentiment labels, but no additional annotation process was described.

#### Who are the annotators?

The annotators are the Amazon users who left the reviews and ratings.

### Personal and Sensitive Information

The dataset contains user IDs and profile names which could potentially be used to identify the reviewers.

## Considerations for Using the Data

### Social Impact of Dataset

This dataset provides insights into consumer preferences and sentiment, which can be valuable for businesses and researchers. However, care should be taken to ensure that models trained on this data do not reinforce stereotypes or biases present in the reviews.

### Discussion of Biases

The dataset may contain biases inherent in the user base of Amazon, which may not be representative of the general population.

### Other Known Limitations

The dataset's scope is limited to food products and may not generalize well to other types of products or reviews.

## Additional Information

### Dataset Curators

The dataset was originally curated by the SNAP group.

### Licensing Information

The dataset is available under a CC BY-SA 4.0 license.

### Citation Information

If you publish articles based on this dataset, please cite the following paper:

J. McAuley and J. Leskovec. _From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews_. WWW, 2013.

### Contributions

Thanks to [@Stanford Network Analysis Project](https://www.kaggle.com/datasets/snap/amazon-fine-food-reviews/data) for adding this dataset.