---
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](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.