Dataset Card for "yelp_polarity"

Dataset Summary

Large Yelp Review Dataset. This is a dataset for binary sentiment classification. We provide a set of 560,000 highly polar yelp reviews for training, and 38,000 for testing. ORIGIN The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data. For more information, please refer to

The Yelp reviews polarity dataset is constructed by Xiang Zhang ( from the above dataset. It is first used as a text classification benchmark in the following paper: Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).


The Yelp reviews polarity dataset is constructed by considering stars 1 and 2 negative, and 3 and 4 positive. For each polarity 280,000 training samples and 19,000 testing samples are take randomly. In total there are 560,000 trainig samples and 38,000 testing samples. Negative polarity is class 1, and positive class 2.

The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 2 columns in them, corresponding to class index (1 and 2) and review text. The review texts are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is " ".

Supported Tasks

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Dataset Structure

We show detailed information for up to 5 configurations of the dataset.

Data Instances


  • Size of downloaded dataset files: 158.67 MB
  • Size of the generated dataset: 421.28 MB
  • Total amount of disk used: 579.95 MB

An example of 'train' looks as follows.

This example was too long and was cropped:

    "label": 0,
    "text": "\"Unfortunately, the frustration of being Dr. Goldberg's patient is a repeat of the experience I've had with so many other doctor..."

Data Fields

The data fields are the same among all splits.


  • text: a string feature.
  • label: a classification label, with possible values including 1 (0), 2 (1).

Data Splits Sample Size

name train test
plain_text 560000 38000

Dataset Creation

Curation Rationale

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Source Data

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

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Citation Information

  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1509.01626},
  primaryClass = {cs},
  title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}},
  abstract = {This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.},
  journal = {arXiv:1509.01626 [cs]},
  author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
  month = sep,
  year = {2015},


Thanks to @patrickvonplaten, @lewtun, @mariamabarham, @thomwolf, @julien-c for adding this dataset.

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