Datasets:
Tasks:
Text Classification
Modalities:
Text
Sub-tasks:
multi-class-classification
Languages:
English
Size:
100K<n<1M
License:
metadata
pretty_name: Emotion
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
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:
- emotion-classification
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: 1741541
num_examples: 16000
- name: validation
num_bytes: 214699
num_examples: 2000
- name: test
num_bytes: 217177
num_examples: 2000
download_size: 2069616
dataset_size: 2173417
Dataset Card for "emotion"
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: https://github.com/dair-ai/emotion_dataset
- Repository: More Information Needed
- Paper: More Information Needed
- Point of Contact: More Information Needed
- 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.
{
"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: astringfeature.label: a classification label, with possible values includingsadness(0),joy(1),love(2),anger(3),fear(4),surprise(5).
emotion
text: astringfeature.label: astringfeature.
Data Splits
| name | train | validation | test |
|---|---|---|---|
| default | 16000 | 2000 | 2000 |
| emotion | 16000 | 2000 | 2000 |
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
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.