README.md

metadata

annotations_creators:
  - crowdsourced
language_creators:
  - found
language:
  - en
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - text-classification
task_ids:
  - sentiment-classification
paperswithcode_id: sst
pretty_name: Stanford Sentiment Treebank v2
dataset_info:
  features:
    - name: idx
      dtype: int32
    - name: sentence
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': negative
            '1': positive
  splits:
    - name: train
      num_bytes: 4681603
      num_examples: 67349
    - name: validation
      num_bytes: 106252
      num_examples: 872
    - name: test
      num_bytes: 216640
      num_examples: 1821
  download_size: 3331058
  dataset_size: 5004495
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Dataset Card for [Dataset Name]

Table of Contents

• Table of Contents
• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://nlp.stanford.edu/sentiment/
• Repository:
• Paper: Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
• Leaderboard:
• Point of Contact:

Dataset Summary

The Stanford Sentiment Treebank is a corpus with fully labeled parse trees that allows for a complete analysis of the compositional effects of sentiment in language. The corpus is based on the dataset introduced by Pang and Lee (2005) and consists of 11,855 single sentences extracted from movie reviews. It was parsed with the Stanford parser and includes a total of 215,154 unique phrases from those parse trees, each annotated by 3 human judges.

Binary classification experiments on full sentences (negative or somewhat negative vs somewhat positive or positive with neutral sentences discarded) refer to the dataset as SST-2 or SST binary.

Supported Tasks and Leaderboards

• sentiment-classification

Languages

The text in the dataset is in English (en).

Dataset Structure

Data Instances

{'idx': 0,
 'sentence': 'hide new secretions from the parental units ',
 'label': 0}

Data Fields

• idx: Monotonically increasing index ID.
• sentence: Complete sentence expressing an opinion about a film.
• label: Sentiment of the opinion, either "negative" (0) or positive (1). The test set labels are hidden (-1).

Data Splits

	train	validation	test
Number of examples	67349	872	1821

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

Rotten Tomatoes reviewers.

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

Unknown.

Citation Information

@inproceedings{socher-etal-2013-recursive,
    title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank",
    author = "Socher, Richard  and
      Perelygin, Alex  and
      Wu, Jean  and
      Chuang, Jason  and
      Manning, Christopher D.  and
      Ng, Andrew  and
      Potts, Christopher",
    booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2013",
    address = "Seattle, Washington, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D13-1170",
    pages = "1631--1642",
}

Contributions

Thanks to @albertvillanova for adding this dataset.