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amazon_polarity

Tasks:
Text Classification
Modalities:
Text
Formats:
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Sub-tasks:
sentiment-classification
Languages:
English
Size:
1M - 10M
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metadata
annotations_creators:
  - crowdsourced
language_creators:
  - crowdsourced
language:
  - en
license:
  - apache-2.0
multilinguality:
  - monolingual
size_categories:
  - 1M<n<10M
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - sentiment-classification
paperswithcode_id: null
pretty_name: Amazon Review Polarity
train-eval-index:
  - config: amazon_polarity
    task: text-classification
    task_id: binary_classification
    splits:
      train_split: train
      eval_split: test
    col_mapping:
      content: 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_info:
  features:
    - name: label
      dtype:
        class_label:
          names:
            '0': negative
            '1': positive
    - name: title
      dtype: string
    - name: content
      dtype: string
  config_name: amazon_polarity
  splits:
    - name: train
      num_bytes: 1604364432
      num_examples: 3600000
    - name: test
      num_bytes: 178176193
      num_examples: 400000
  download_size: 688339454
  dataset_size: 1782540625

Dataset Card for Amazon Review Polarity

Table of Contents

Dataset Description

Dataset Summary

The Amazon reviews dataset consists of reviews from amazon. The data span a period of 18 years, including ~35 million reviews up to March 2013. Reviews include product and user information, ratings, and a plaintext review.

Supported Tasks and Leaderboards

  • text-classification, sentiment-classification: The dataset is mainly used for text classification: given the content and the title, predict the correct star rating.

Languages

Mainly English.

Dataset Structure

Data Instances

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

An example from the AmazonPolarity test set looks as follows:

{
    'title':'Great CD',
    'content':"My lovely Pat has one of the GREAT voices of her generation. I have listened to this CD for YEARS and I still LOVE IT. When I'm in a good mood it makes me feel better. A bad mood just evaporates like sugar in the rain. This CD just oozes LIFE. Vocals are jusat STUUNNING and lyrics just kill. One of life's hidden gems. This is a desert isle CD in my book. Why she never made it big is just beyond me. Everytime I play this, no matter black, white, young, old, male, female EVERYBODY says one thing ""Who was that singing ?""",
    'label':1
}

Data Fields

  • 'title': a string containing the title of the review - 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".
  • 'content': a string containing the body of the document - 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': either 1 (positive) or 0 (negative) rating.

Data Splits

The Amazon reviews polarity dataset is constructed by taking review score 1 and 2 as negative, and 4 and 5 as positive. Samples of score 3 is ignored. Each class has 1,800,000 training samples and 200,000 testing samples.

Dataset Creation

Curation Rationale

The Amazon reviews polarity dataset is constructed by Xiang Zhang (xiang.zhang@nyu.edu). It is 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

[Needs More Information]

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

[Needs More Information]

Who are the annotators?

[Needs More Information]

Personal and Sensitive Information

[Needs More Information]

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

Apache License 2.0

Citation Information

McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. 2013.

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.