jhan21/amazon-beauty-reviews-dataset

Dataset Card for "Amazon Beauty Reviews"

Dataset Summary

This dataset consists of reviews of "All Beauty" category from amazon. The data includes all ~700,000 reviews up to 2023. Reviews include product and user information, ratings, and a plain text review.

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 rating, title, text, user ID, helpfulness votes, and time of the review. It also includes some minor fields like images, asin, parent_asin, and verified_purchase.

Data Fields

Field	Type	Explanation
rating	float	Rating of the product (from 1.0 to 5.0).
title	str	Title of the user review.
text	str	Text body of the user review.
images	list	Images that users post after they have received the product. Each image has different sizes (small, medium, large), represented by the small_image_url, medium_image_url, and large_image_url respectively.
asin	str	ID of the product.
parent_asin	str	Parent ID of the product. Note: Products with different colors, styles, sizes usually belong to the same parent ID. The “asin” in previous Amazon datasets is actually parent ID. Please use parent ID to find product meta.
user_id	str	ID of the reviewer
timestamp	int	Time of the review (unix time)
verified_purchase	bool	User purchase verification
helpful_vote	int	Helpful votes of the review

Quick Start

Load User Reviews:

from datasets import load_dataset

dataset = load_dataset("jhan21/amazon-beauty-reviews-dataset", trust_remote_code=True)

print(dataset["full"][1])

{'rating': 4.0,
 'title': 'Works great but smells a little weird.',
 'text': 'This product does what I need it to do, I just wish it was odorless or had a soft coconut smell. Having my head smell like an orange coffee is offputting. (granted, I did know the smell was described but I was hoping it would be light)',
 'images': [],
 'asin': 'B081TJ8YS3',
 'parent_asin': 'B081TJ8YS3',
 'user_id': 'AGKHLEW2SOWHNMFQIJGBECAF7INQ',
 'timestamp': 1588615855070,
 'helpful_vote': 1,
 'verified_purchase': True}

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 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 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 beauty products and may not generalize well to other types of products or reviews.

Additional Information

Dataset Curators

The dataset was originally collected in 2023 by McAuley Lab, and it includes rich features such as:

• User Reviews (ratings, text, helpfulness votes, etc.);
• Item Metadata (descriptions, price, raw image, etc.);
• Links (user-item / bought together graphs).

Please also visit amazon-reviews-2023.github.io/ for more details, loading scripts, and preprocessed benchmark files.

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:

@article{hou2024bridging,
  title={Bridging Language and Items for Retrieval and Recommendation},
  author={Hou, Yupeng and Li, Jiacheng and He, Zhankui and Yan, An and Chen, Xiusi and McAuley, Julian},
  journal={arXiv preprint arXiv:2403.03952},
  year={2024}
}

Contributions

Thanks to @McAuley-Lab for adding this dataset.

Contact

• Report Bugs: To report bugs in the dataset, please file an issue on our GitHub.

• Others: For research collaborations or other questions, please email yphou AT ucsd.edu.