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Datasets:
ruanchaves
/
b2w-reviews01

Tasks:
Text Classification
Modalities:
Tabular
Text
Sub-tasks:
sentiment-analysis
sentiment-scoring
intent-classification
Languages:
Portuguese
Size:
100K - 1M
Tags:
reviews
DOI:
Libraries:
Datasets
License:
Dataset card Data Studio Files Community
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Dataset Card for Dataset Name

Dataset Description

Dataset Summary

B2W-Reviews01 is an open corpus of product reviews. It contains more than 130k e-commerce customer reviews, collected from the Americanas.com website between January and May, 2018. B2W-Reviews01 offers rich information about the reviewer profile, such as gender, age, and geographical location. The corpus also has two different review rates:

  • the usual 5-point scale rate, represented by stars in most e-commerce websites,
  • a "recommend to a friend" label, a "yes or no" question representing the willingness of the customer to recommend the product to someone else.

This dataset can be useful for several Natural Language Processing (NLP)/ Computational Linguistics (CL) tasks. The first that comes to mind is probably sentiment analysis. Sentiment analysis is the task of assigning a sentiment (or a position) to the content of a given text. For this task, B2W-Reviews01 offers the two distinct evaluation ratings.

Supported Tasks and Leaderboards

  • Sentiment Analysis
  • Topic Modeling

Languages

  • Portuguese

Dataset Structure

Data Instances 

{'submission_date': '2018-01-02 06:23:22',
 'reviewer_id': '6adc7901926fc1697d34181fbd88895976b4f3f31f0102d90217d248a1fad156',
 'product_id': '123911277',
 'product_name': 'Triciclo Gangorra Belfix Cabeça Cachorro Rosa',
 'product_brand': 'belfix',
 'site_category_lv1': 'Brinquedos',
 'site_category_lv2': 'Mini Veículos',
 'review_title': 'O produto não foi entregue',
 'overall_rating': 1,
 'recommend_to_a_friend': 'Yes',
 'review_text': 'Incrível o descaso com o consumidor. O produto não chegou, apesar de já ter sido pago. Não recebo qualquer informação sobre onde se encontra o produto, ou qualquer compensação do vendedor.  Não recomendo.',
 'reviewer_birth_year': 1981,
 'reviewer_gender': 'M',
 'reviewer_state': 'RJ'}

Data Fields

  • submission_date: the date and time when the review was submitted.
  • reviewer_id: a unique identifier for the reviewer.
  • product_id: a unique identifier for the product being reviewed.
  • product_name: the name of the product being reviewed.
  • product_brand: the brand of the product being reviewed.
  • site_category_lv1: the highest level category for the product on the site where the review is being submitted.
  • site_category_lv2: the second level category for the product on the site where the review is being submitted.
  • review_title: the title of the review.
  • overall_rating: the overall star rating given by the reviewer on a scale of 1 to 5.
  • recommend_to_a_friend: whether or not the reviewer would recommend the product to a friend (Yes/No).
  • review_text: the full text of the review.
  • reviewer_birth_year: the birth year of the reviewer.
  • reviewer_gender: the gender of the reviewer (F/M).
  • reviewer_state: the Brazilian state of the reviewer (e.g. RJ).

Data Splits

Citation Information 

@inproceedings{real2019b2w,
  title={B2W-reviews01: an open product reviews corpus},
  author={Real, Livy and Oshiro, Marcio and Mafra, Alexandre},
  booktitle={STIL-Symposium in Information and Human Language Technology},
  year={2019}
}

Contributions

Thanks to @ruanchaves for adding this dataset.