rotten_tomatoes / README.md
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metadata
annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - en
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - sentiment-classification
paperswithcode_id: mr
pretty_name: RottenTomatoes - MR Movie Review Data
dataset_info:
  features:
    - name: text
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': neg
            '1': pos
  splits:
    - name: train
      num_bytes: 1074810
      num_examples: 8530
    - name: validation
      num_bytes: 134679
      num_examples: 1066
    - name: test
      num_bytes: 135972
      num_examples: 1066
  download_size: 487770
  dataset_size: 1345461
train-eval-index:
  - config: default
    task: text-classification
    task_id: binary_classification
    splits:
      train_split: train
      eval_split: test
    col_mapping:
      text: text
      label: target
    metrics:
      - type: accuracy
        name: Accuracy
      - type: f1
        name: F1
        args:
          average: binary
      - 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 "rotten_tomatoes"

Table of Contents

Dataset Description

Dataset Summary

Movie Review Dataset. This is a dataset of containing 5,331 positive and 5,331 negative processed sentences from Rotten Tomatoes movie reviews. This data was first used in Bo Pang and Lillian Lee, ``Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales.'', Proceedings of the ACL, 2005.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

default

  • Size of downloaded dataset files: 0.49 MB
  • Size of the generated dataset: 1.34 MB
  • Total amount of disk used: 1.84 MB

An example of 'validation' looks as follows.

{
    "label": 1,
    "text": "Sometimes the days and nights just drag on -- it 's the morning that make me feel alive . And I have one thing to thank for that : pancakes . "
}

Data Fields

The data fields are the same among all splits.

default

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

Data Splits

Reads Rotten Tomatoes sentences and splits into 80% train, 10% validation, and 10% test, as is the practice set out in

Jinfeng Li, ``TEXTBUGGER: Generating Adversarial Text Against Real-world Applications.''

name train validation test
default 8530 1066 1066

Dataset Creation

Curation Rationale

More Information Needed

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

More Information Needed

Citation Information

@InProceedings{Pang+Lee:05a,
  author =       {Bo Pang and Lillian Lee},
  title =        {Seeing stars: Exploiting class relationships for sentiment
                  categorization with respect to rating scales},
  booktitle =    {Proceedings of the ACL},
  year =         2005
}

Contributions

Thanks to @thomwolf, @jxmorris12 for adding this dataset.