
# Dataset Card for Dataset Name

## Dataset Description

- **Repository:** https://github.com/americanas-tech/b2w-reviews01
- **Paper:** http://comissoes.sbc.org.br/ce-pln/stil2019/proceedings-stil-2019-Final-Publicacao.pdf
- **Point of Contact:** Livy Real

### 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.

### 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. `"%Y-%m-%d %H:%M:%S"`.
* **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

|  name   |train|
|---------|----:|
|b2w-reviews01|132373|

### 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](https://github.com/ruanchaves) for adding this dataset.
task_categories:text-classification task_ids:sentiment-analysis task_ids:sentiment-scoring task_ids:intent-classification task_ids:topic-classification annotations_creators:found language_creators:found multilinguality:monolingual size_categories:100M<n<1B source_datasets:original language:pt license:cc-by-4.0 reviews doi:10.57967/hf/0282 region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["pt"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["100M<n<1B"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["sentiment-analysis", "sentiment-scoring", "intent-classification", "topic-classification"], "pretty_name": "B2W-Reviews01", "tags": ["reviews"]}
TAGS
#task_categories-text-classification #task_ids-sentiment-analysis #task_ids-sentiment-scoring #task_ids-intent-classification #task_ids-topic-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100M<n<1B #source_datasets-original #language-Portuguese #license-cc-by-4.0 #reviews #doi-10.57967/hf/0282 #region-us 

Dataset Card for Dataset Name
=============================


Dataset Description
-------------------


* Repository: URL
* Paper: URL
* Point of Contact: Livy Real


### Dataset Summary


B2W-Reviews01 is an open corpus of product reviews. It contains more than 130k e-commerce customer reviews, collected from the URL 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.


### Supported Tasks and Leaderboards


* Sentiment Analysis
* Topic Modeling


### Languages


* Portuguese


Dataset Structure
-----------------


### Data Instances


### Data Fields


* submission\_date: the date and time when the review was submitted. '"%Y-%m-%d %H:%M:%S"'.
* 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



### Contributions


Thanks to @ruanchaves for adding this dataset.



### Dataset Summary


B2W-Reviews01 is an open corpus of product reviews. It contains more than 130k e-commerce customer reviews, collected from the URL 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. ### Supported Tasks and Leaderboards


* Sentiment Analysis
* Topic Modeling ### Languages


* Portuguese


Dataset Structure
----------------- ### Data Instances ### Data Fields


* submission\_date: the date and time when the review was submitted. '"%Y-%m-%d %H:%M:%S"'.
* 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 ### Contributions


Thanks to @ruanchaves for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-sentiment-analysis #task_ids-sentiment-scoring #task_ids-intent-classification #task_ids-topic-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100M<n<1B #source_datasets-original #language-Portuguese #license-cc-by-4.0 #reviews #doi-10.57967/hf/0282 #region-us 
 ### Dataset Summary


B2W-Reviews01 is an open corpus of product reviews. It contains more than 130k e-commerce customer reviews, collected from the URL 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. ### Supported Tasks and Leaderboards


* Sentiment Analysis
* Topic Modeling ### Languages


* Portuguese


Dataset Structure
----------------- ### Data Instances ### Data Fields


* submission\_date: the date and time when the review was submitted. '"%Y-%m-%d %H:%M:%S"'.
* 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 ### Contributions


Thanks to @ruanchaves for adding this dataset.
passage: TAGS
#task_categories-text-classification #task_ids-sentiment-analysis #task_ids-sentiment-scoring #task_ids-intent-classification #task_ids-topic-classification #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-100M<n<1B #source_datasets-original #language-Portuguese #license-cc-by-4.0 #reviews #doi-10.57967/hf/0282 #region-us 
### Dataset Summary


B2W-Reviews01 is an open corpus of product reviews. It contains more than 130k e-commerce customer reviews, collected from the URL 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.### Supported Tasks and Leaderboards


* Sentiment Analysis
* Topic Modeling### Languages


* Portuguese


Dataset Structure
-----------------### Data Instances