zbnsl/emoteModified

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

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

default

• Size of downloaded dataset files: 1.97 MB
• Size of the generated dataset: 2.07 MB
• Total amount of disk used: 4.05 MB

An example of 'train' looks as follows.

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

emotion

• Size of downloaded dataset files: 1.97 MB
• Size of the generated dataset: 2.09 MB
• Total amount of disk used: 4.06 MB

An example of 'validation' looks as follows.

Data Fields

The data fields are the same among all splits.

default

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

emotion

• text: a string feature.
• label: a string feature.

Data Splits

name	train	validation	test
default	16000	2000	2000
emotion	16000	2000	2000

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

More Information Needed

Citation Information

@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.