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数据集:
dair-ai/emotion
任务:
文本分类
语言:
en
计算机处理:
monolingual大小:
10K<n<100K语言创建人:
machine-generated批注创建人:
machine-generated源数据集:
original许可:
other
英文"情感" 数据集卡片
数据集概述
"情感" 数据集是一个包含六种基本情感（愤怒、恐惧、喜悦、爱、伤心、惊讶）的英文 Twitter 消息数据集。更详细的信息请参考相关论文。
支持的任务和排行榜
语言
数据集结构
数据实例
一个示例如下所示。
{
"text": "im feeling quite sad and sorry for myself but ill snap out of it soon",
"label": 0
}
数据字段
数据字段包括：
- text: 一个字符串特征。
- label: 一个分类标签，可能的值包括伤心（0）、喜悦（1）、爱（2）、愤怒（3）、恐惧（4）、惊讶（5）。
数据划分
数据集有两个配置：
- split：共 20,000 个示例，划分为训练集、验证集和测试集。
- unsplit：共 416,809 个示例，单个训练集划分。
| name | train | validation | test |
|---|---|---|---|
| split | 16000 | 2000 | 2000 |
| unsplit | 416809 | n/a | n/a |
数据集创建
策划理由
源数据
数据收集和规范化 谁是源语言的生产者？注释
注释过程 谁是注释者？个人和敏感信息
使用数据的注意事项
数据集的社会影响
偏见讨论
其他已知限制
附加信息
数据集策划者
许可信息
该数据集仅限用于教育和研究目的。
引用信息
如果您使用了该数据集，请引用：
@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.",
}