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Datasets:
yelp
/
yelp_review_full

Tasks:
Text Classification
Modalities:
Text
Formats:
parquet
Sub-tasks:
sentiment-classification
Languages:
English
Size:
100K - 1M
ArXiv:
Libraries:
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pandas
License:
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metadata
annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - en
license:
  - other
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - sentiment-classification
pretty_name: YelpReviewFull
license_details: yelp-licence
dataset_info:
  config_name: yelp_review_full
  features:
    - name: label
      dtype:
        class_label:
          names:
            '0': 1 star
            '1': 2 star
            '2': 3 stars
            '3': 4 stars
            '4': 5 stars
    - name: text
      dtype: string
  splits:
    - name: train
      num_bytes: 483811554
      num_examples: 650000
    - name: test
      num_bytes: 37271188
      num_examples: 50000
  download_size: 322952369
  dataset_size: 521082742
configs:
  - config_name: yelp_review_full
    data_files:
      - split: train
        path: yelp_review_full/train-*
      - split: test
        path: yelp_review_full/test-*
    default: true
train-eval-index:
  - config: yelp_review_full
    task: text-classification
    task_id: multi_class_classification
    splits:
      train_split: train
      eval_split: test
    col_mapping:
      text: text
      label: target
    metrics:
      - type: accuracy
        name: Accuracy
      - type: f1
        name: F1 macro
        args:
          average: macro
      - type: f1
        name: F1 micro
        args:
          average: micro
      - type: f1
        name: F1 weighted
        args:
          average: weighted
      - type: precision
        name: Precision macro
        args:
          average: macro
      - type: precision
        name: Precision micro
        args:
          average: micro
      - type: precision
        name: Precision weighted
        args:
          average: weighted
      - type: recall
        name: Recall macro
        args:
          average: macro
      - type: recall
        name: Recall micro
        args:
          average: micro
      - type: recall
        name: Recall weighted
        args:
          average: weighted

Dataset Card for YelpReviewFull

Table of Contents

Dataset Description

Dataset Summary

The Yelp reviews dataset consists of reviews from Yelp. It is extracted from the Yelp Dataset Challenge 2015 data.

Supported Tasks and Leaderboards

  • text-classification, sentiment-classification: The dataset is mainly used for text classification: given the text, predict the sentiment.

Languages

The reviews were mainly written in english.

Dataset Structure

Data Instances

A typical data point, comprises of a text and the corresponding label.

An example from the YelpReviewFull test set looks as follows:

{
    'label': 0,
    'text': 'I got \'new\' tires from them and within two weeks got a flat. I took my car to a local mechanic to see if i could get the hole patched, but they said the reason I had a flat was because the previous patch had blown - WAIT, WHAT? I just got the tire and never needed to have it patched? This was supposed to be a new tire. \\nI took the tire over to Flynn\'s and they told me that someone punctured my tire, then tried to patch it. So there are resentful tire slashers? I find that very unlikely. After arguing with the guy and telling him that his logic was far fetched he said he\'d give me a new tire \\"this time\\". \\nI will never go back to Flynn\'s b/c of the way this guy treated me and the simple fact that they gave me a used tire!'
}

Data Fields

  • '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 "\n".
  • 'label': Corresponds to the score associated with the review (between 1 and 5).

Data Splits

The Yelp reviews full star dataset is constructed by randomly taking 130,000 training samples and 10,000 testing samples for each review star from 1 to 5. In total there are 650,000 trainig samples and 50,000 testing samples.

Dataset Creation

Curation Rationale

The Yelp reviews full star dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu) from the Yelp Dataset Challenge 2015. 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).

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

You can check the official yelp-dataset-agreement.

Citation Information

Xiang Zhang, Junbo Zhao, Yann LeCun. Character-level Convolutional Networks for Text Classification. Advances in Neural Information Processing Systems 28 (NIPS 2015).

Contributions

Thanks to @hfawaz for adding this dataset.