README.md

metadata

dataset_info:
  features:
    - name: text
      dtype: string
    - name: label
      dtype:
        class_label:
          names:
            '0': sadness
            '1': joy
            '2': love
            '3': anger
            '4': fear
            '5': surprise
  splits:
    - name: train
      num_bytes: 36355191.79432066
      num_examples: 333447
    - name: validation
      num_bytes: 4544412.60283967
      num_examples: 41681
    - name: test
      num_bytes: 4544412.60283967
      num_examples: 41681
  download_size: 26751980
  dataset_size: 45444017
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
license: mit
task_categories:
  - text-classification
language:
  - en
tags:
  - emotion-Classitication
pretty_name: Emotion
size_categories:
  - 10K<n<100K

Dataset Card for "emotion"

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://github.com/dair-ai/emotion_dataset
• Repository: More Information Needed
• Paper: More Information Needed
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 16.13 MB
• Size of the generated dataset: 47.62 MB
• Total amount of disk used: 63.75 MB

Dataset Summary

Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

An example looks as follows.

{
  "text": "im feeling quite sad and sorry for myself but ill snap out of it soon",
  "label": 0
}

Data Fields

The data fields are:

• text: a string feature.
• label: a classification label, with possible values including sadness (0), joy (1), love (2), anger (3), fear (4), surprise (5).

Data Splits

The dataset has 2 configurations:

• split: with a total of 20_000 examples split into train, validation and split
• unsplit: with a total of 416_809 examples in a single train split

name	train	validation	test
split	16000	2000	2000
unsplit	416809	n/a	n/a

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

The dataset should be used for educational and research purposes only.

Citation Information

If you use this dataset, please cite:

@inproceedings{saravia-etal-2018-carer,
    title = "{CARER}: Contextualized Affect Representations for Emotion Recognition",
    author = "Saravia, Elvis  and
      Liu, Hsien-Chi Toby  and
      Huang, Yen-Hao  and
      Wu, Junlin  and
      Chen, Yi-Shin",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D18-1404",
    doi = "10.18653/v1/D18-1404",
    pages = "3687--3697",
    abstract = "Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semi-supervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks.",
}

Contributions

Thanks to @lhoestq, @thomwolf, @lewtun for adding this dataset.