您尚未登录账户请先登录您的atyun账户，方可使用该功能
您的企业账号申请正在审核中审核通过后即可使用此功能，请耐心等待~
数据集:
philschmid/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"emotion"数据集的数据卡片
数据集摘要
Emotion是一个包含六种基本情绪（愤怒，恐惧，喜悦，爱，悲伤和惊讶）的英文推特消息数据集。详细信息请参考论文。
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。
{
"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，可能的值包括悲伤（0），喜悦（1），爱（2），愤怒（3），恐惧（4），惊讶（5）。
数据拆分
数据集有两个配置：
- 拆分：共有20,000个示例，拆分为训练、验证和测试集。
- 未拆分：一个单独的训练集，共有416,809个示例。
| 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
初始数据收集和标准化 源语言制作者是谁？注释
注释流程 注释者是谁？个人和敏感信息
使用数据的注意事项
数据的社会影响
潜在偏见讨论
其他已知限制
附加信息
数据集策划者
许可信息
该数据集仅用于教育和研究目的。
引用信息
如果使用该数据集，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.",
}