Create README.md

README.md

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+---
+# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
+# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
+{}
+---
+
+# Dataset Card for Dataset Name
+
+## Dataset Description
+
+- **Homepage:** 
+- **Repository:** https://github.com/americanas-tech/b2w-reviews01
+- **Paper:** 
+- **Leaderboard:** 
+- **Point of Contact:** 
+
+### 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.
+
+Product reviews often have complex information, related not only to the product that was purchased, but also to the online shopping experience, payment methods, or even the product delivery process. Therefore, different facts and opinions can be extracted from such a corpus, and classifying sentiment may not be enough to capture the content of reviews. For real world applications, dealing with topic modeling, user intent identification and feature extraction also become necessary. It is relevant to know not only the reviewer’s sentiment, but also the object of this feeling.
+
+Since B2W-Reviews01 offers the exact text written by users, this corpus also offers rich material for those interested on out-of-vocabulary words, slang identification, or spell-checker tasks. For those interested on socio-linguistics analysis, the present corpus offers a rich possibility of crossing reviewer information considering gender, age and geographical location. One can, for example, find easily how negative or positive reviews are distributed among age groups or which product categories receive more reviews from women or men. 
+
+### 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
+
+[More Information Needed]
+
+### 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
+
+[More Information Needed]