
# Dataset Card for "emotion"

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [https://github.com/dair-ai/emotion_dataset](https://github.com/dair-ai/emotion_dataset)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 3.95 MB
- **Size of the generated dataset:** 4.16 MB
- **Total amount of disk used:** 8.11 MB

### 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](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Languages

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## 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](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the source language producers?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Annotations

#### Annotation process

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

#### Who are the annotators?

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Personal and Sensitive Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Discussion of Biases

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Other Known Limitations

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Additional Information

### Dataset Curators

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Licensing Information

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### 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](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun) for adding this dataset.
task_categories:text-classification task_ids:multi-class-classification annotations_creators:machine-generated language_creators:machine-generated multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:unknown emotion-classification region:us
{"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "task_ids": ["multi-class-classification"], "paperswithcode_id": "emotion", "pretty_name": "Emotion", "tags": ["emotion-classification"], "train-eval-index": [{"config": "default", "task": "text-classification", "task_id": "multi_class_classification", "splits": {"train_split": "train", "eval_split": "test"}, "col_mapping": {"text": "text", "label": "target"}, "metrics": [{"type": "accuracy", "name": "Accuracy"}, {"type": "f1", "name": "F1 macro", "args": {"average": "macro"}}, {"type": "f1", "name": "F1 micro", "args": {"average": "micro"}}, {"type": "f1", "name": "F1 weighted", "args": {"average": "weighted"}}, {"type": "precision", "name": "Precision macro", "args": {"average": "macro"}}, {"type": "precision", "name": "Precision micro", "args": {"average": "micro"}}, {"type": "precision", "name": "Precision weighted", "args": {"average": "weighted"}}, {"type": "recall", "name": "Recall macro", "args": {"average": "macro"}}, {"type": "recall", "name": "Recall micro", "args": {"average": "micro"}}, {"type": "recall", "name": "Recall weighted", "args": {"average": "weighted"}}]}]}
TAGS
#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #emotion-classification #region-us 

Dataset Card for "emotion"
==========================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: URL
* Repository:
* Paper:
* Point of Contact:
* Size of downloaded dataset files: 3.95 MB
* Size of the generated dataset: 4.16 MB
* Total amount of disk used: 8.11 MB


### 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


### Languages


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.


#### 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



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information


### Contributions


Thanks to @lhoestq, @thomwolf, @lewtun for adding this dataset.



### 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 ### Languages


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. #### 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



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions


Thanks to @lhoestq, @thomwolf, @lewtun for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-multi-class-classification #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #emotion-classification #region-us 
 ### 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 ### Languages


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. #### 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



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information ### Contributions


Thanks to @lhoestq, @thomwolf, @lewtun for adding this dataset.