- Description:
The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral. The emotion categories are admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise.
Additional Documentation: Explore on Papers With Code
Homepage: https://github.com/google-research/google-research/tree/master/goemotions
Source code:
tfds.text.GoemotionsVersions:
0.1.0(default): No release notes.
Download size:
4.19 MiBDataset size:
32.25 MiBAuto-cached (documentation): Yes
Splits:
| Split | Examples |
|---|---|
'test' |
5,427 |
'train' |
43,410 |
'validation' |
5,426 |
- Feature structure:
FeaturesDict({
'admiration': bool,
'amusement': bool,
'anger': bool,
'annoyance': bool,
'approval': bool,
'caring': bool,
'comment_text': Text(shape=(), dtype=string),
'confusion': bool,
'curiosity': bool,
'desire': bool,
'disappointment': bool,
'disapproval': bool,
'disgust': bool,
'embarrassment': bool,
'excitement': bool,
'fear': bool,
'gratitude': bool,
'grief': bool,
'joy': bool,
'love': bool,
'nervousness': bool,
'neutral': bool,
'optimism': bool,
'pride': bool,
'realization': bool,
'relief': bool,
'remorse': bool,
'sadness': bool,
'surprise': bool,
})
- Feature documentation:
| Feature | Class | Shape | Dtype | Description |
|---|---|---|---|---|
| FeaturesDict | ||||
| admiration | Tensor | bool | ||
| amusement | Tensor | bool | ||
| anger | Tensor | bool | ||
| annoyance | Tensor | bool | ||
| approval | Tensor | bool | ||
| caring | Tensor | bool | ||
| comment_text | Text | string | ||
| confusion | Tensor | bool | ||
| curiosity | Tensor | bool | ||
| desire | Tensor | bool | ||
| disappointment | Tensor | bool | ||
| disapproval | Tensor | bool | ||
| disgust | Tensor | bool | ||
| embarrassment | Tensor | bool | ||
| excitement | Tensor | bool | ||
| fear | Tensor | bool | ||
| gratitude | Tensor | bool | ||
| grief | Tensor | bool | ||
| joy | Tensor | bool | ||
| love | Tensor | bool | ||
| nervousness | Tensor | bool | ||
| neutral | Tensor | bool | ||
| optimism | Tensor | bool | ||
| pride | Tensor | bool | ||
| realization | Tensor | bool | ||
| relief | Tensor | bool | ||
| remorse | Tensor | bool | ||
| sadness | Tensor | bool | ||
| surprise | Tensor | bool |
Supervised keys (See
as_superviseddoc):NoneFigure (tfds.show_examples): Not supported.
Examples (tfds.as_dataframe):
- Citation:
@inproceedings{demszky-2020-goemotions,
title = "{G}o{E}motions: A Dataset of Fine-Grained Emotions",
author = "Demszky, Dorottya and
Movshovitz-Attias, Dana and
Ko, Jeongwoo and
Cowen, Alan and
Nemade, Gaurav and
Ravi, Sujith",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.372",
pages = "4040--4054",
}