goemotions
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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.
Split |
Examples |
'test' |
5,427 |
'train' |
43,410 |
'validation' |
5,426 |
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 |
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 |
|
@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",
}
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2022-12-06 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2022-12-06 UTC."],[],[],null,["# goemotions\n\n\u003cbr /\u003e\n\n- **Description**:\n\nThe GoEmotions dataset contains 58k carefully curated Reddit comments labeled\nfor 27 emotion categories or Neutral. The emotion categories are admiration,\namusement, anger, annoyance, approval, caring, confusion, curiosity, desire,\ndisappointment, disapproval, disgust, embarrassment, excitement, fear,\ngratitude, grief, joy, love, nervousness, optimism, pride, realization, relief,\nremorse, sadness, surprise.\n\n- **Additional Documentation** :\n [Explore on Papers With Code\n north_east](https://paperswithcode.com/dataset/goemotions)\n\n- **Homepage** :\n \u003chttps://github.com/google-research/google-research/tree/master/goemotions\u003e\n\n- **Source code** :\n [`tfds.text.Goemotions`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/text/goemotions.py)\n\n- **Versions**:\n\n - **`0.1.0`** (default): No release notes.\n- **Download size** : `4.19 MiB`\n\n- **Dataset size** : `32.25 MiB`\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n Yes\n\n- **Splits**:\n\n| Split | Examples |\n|----------------|----------|\n| `'test'` | 5,427 |\n| `'train'` | 43,410 |\n| `'validation'` | 5,426 |\n\n- **Feature structure**:\n\n FeaturesDict({\n 'admiration': bool,\n 'amusement': bool,\n 'anger': bool,\n 'annoyance': bool,\n 'approval': bool,\n 'caring': bool,\n 'comment_text': Text(shape=(), dtype=string),\n 'confusion': bool,\n 'curiosity': bool,\n 'desire': bool,\n 'disappointment': bool,\n 'disapproval': bool,\n 'disgust': bool,\n 'embarrassment': bool,\n 'excitement': bool,\n 'fear': bool,\n 'gratitude': bool,\n 'grief': bool,\n 'joy': bool,\n 'love': bool,\n 'nervousness': bool,\n 'neutral': bool,\n 'optimism': bool,\n 'pride': bool,\n 'realization': bool,\n 'relief': bool,\n 'remorse': bool,\n 'sadness': bool,\n 'surprise': bool,\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|----------------|--------------|-------|--------|-------------|\n| | FeaturesDict | | | |\n| admiration | Tensor | | bool | |\n| amusement | Tensor | | bool | |\n| anger | Tensor | | bool | |\n| annoyance | Tensor | | bool | |\n| approval | Tensor | | bool | |\n| caring | Tensor | | bool | |\n| comment_text | Text | | string | |\n| confusion | Tensor | | bool | |\n| curiosity | Tensor | | bool | |\n| desire | Tensor | | bool | |\n| disappointment | Tensor | | bool | |\n| disapproval | Tensor | | bool | |\n| disgust | Tensor | | bool | |\n| embarrassment | Tensor | | bool | |\n| excitement | Tensor | | bool | |\n| fear | Tensor | | bool | |\n| gratitude | Tensor | | bool | |\n| grief | Tensor | | bool | |\n| joy | Tensor | | bool | |\n| love | Tensor | | bool | |\n| nervousness | Tensor | | bool | |\n| neutral | Tensor | | bool | |\n| optimism | Tensor | | bool | |\n| pride | Tensor | | bool | |\n| realization | Tensor | | bool | |\n| relief | Tensor | | bool | |\n| remorse | Tensor | | bool | |\n| sadness | Tensor | | bool | |\n| surprise | Tensor | | bool | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `None`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n Not supported.\n\n- **Examples**\n ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n\nDisplay examples... \n\n- **Citation**:\n\n @inproceedings{demszky-2020-goemotions,\n title = \"{G}o{E}motions: A Dataset of Fine-Grained Emotions\",\n author = \"Demszky, Dorottya and\n Movshovitz-Attias, Dana and\n Ko, Jeongwoo and\n Cowen, Alan and\n Nemade, Gaurav and\n Ravi, Sujith\",\n booktitle = \"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics\",\n month = jul,\n year = \"2020\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/2020.acl-main.372\",\n pages = \"4040--4054\",\n }"]]