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:
  - 100K<n<1M

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.

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 1 configurations:

split: with a total of 416809 examples split into train, validation and split

name	train	validation	test
split	333447	41681	41681

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.