Dataset Card for "emotion"

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

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Languages

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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

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Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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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.