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 test
unsplit: with a total of 89_754 examples in a single train split

name	train	validation	test
split	16000	2000	2000
unsplit	89754	n/a	n/a

Dataset Creation

Curation Rationale

This dataset is designed for training machine learning models to perform emotion analysis. It contains text samples from Twitter labeled with six different emotions: sadness, joy, love, anger, fear, and surprise. The dataset is balanced, meaning that it has an equal number of samples for each label.

This dataset is originally sourced from dair-ai's emotion dataset, but the initial dataset was unbalanced and had some duplicate samples. Thus, this dataset has been deduplicated and balanced to ensure an equal number of samples for each emotion label.

Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotations

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

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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.",
}

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