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.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('goemotions', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

Dataset Card for GoEmotions

  Dataset Summary

The GoEmotions dataset contains 58k carefully curated Reddit comments labeled for 27 emotion categories or Neutral. The raw data is included as well as the smaller, simplified version of the dataset with predefined train/val/test splits.

  Supported Tasks and Leaderboards

This dataset is intended for multi-class, multi-label emotion classification.

  Languages

The data is in English and Brazilian Portuguese… See the full description on the dataset page: https://huggingface.co/datasets/antoniomenezes/go_emotions_ptbr.

GoEmotions Ekman Emotions Dataset

  Dataset Description

This dataset contains 10,000 text samples from Reddit comments mapped to the 7 basic Ekman emotions. It's derived from the original GoEmotions dataset and processed specifically for emotion classification research using Paul Ekman's fundamental emotion model.

  Supported Tasks

Text Classification: Multi-class emotion classification Sentiment Analysis: Fine-grained emotion detection Psychology Research:… See the full description on the dataset page: https://huggingface.co/datasets/Frankhihi/goemotion-ekman-emotions.

The GoEmotions dataset, a collection of 58k Reddit comments labeled according to the publisher affect from a taxonomy of 28 emotions.

Hostileic/hinglish-goemotions dataset hosted on Hugging Face and contributed by the HF Datasets community

Original dataset: GoEmotions dataset Added labels_ekman column with multi-label emotion annotations mapped to 7 base emotions as per Dr. Ekman theory. Column labels contains multi-label emotion annotations with 28 emotion labels as per GoEmotion dataset: 0: admiration 1: amusement 2: anger 3: annoyance 4: approval 5: caring 6: confusion 7: curiosity 8: desire 9: disappointment 10: disapproval 11: disgust 12: embarrassment 13: excitement 14: fear 15: gratitude 16: grief 17: joy 18: love 19:… See the full description on the dataset page: https://huggingface.co/datasets/SkyWater21/en_go_emotions.

AutoTrain Dataset for project: twitter-goemotions-binary-fear-classification

  Dataset Description

This dataset has been automatically processed by AutoTrain for project twitter-goemotions-binary-fear-classification.

  Languages

The BCP-47 code for the dataset's language is unk.

  Dataset Structure





  Data Instances

A sample from this dataset looks as follows: [ { "text": "Downvoting comments you don't like is your right.", "feat_id":… See the full description on the dataset page: https://huggingface.co/datasets/garrettbaber/twitter-roberta-goemotions-binary-fear-classification.

GoEmotions dataset

The original dataset: GoEmotions (paper). The derived dataset contains an additional labels_ekman column with emotion labels as per Paul Ekman's theory.

The original 27 + neutral emotion labels (may contain more than one label per sample):

0: admiration 1: amusement 2: anger 3: annoyance 4: approval 5: caring 6: confusion 7: curiosity 8: desire 9: disappointment 10: disapproval 11: disgust 12: embarrassment 13: excitement 14: fear 15: gratitude 16: grief 17: joy… See the full description on the dataset page: https://huggingface.co/datasets/AiLab-IMCS-UL/go_emotions-en.

Humans' emotional perception is subjective by nature, in which each individual could express different emotions regarding the same textual content. Existing datasets for emotion analysis commonly depend on a single ground truth per data sample, derived from majority voting or averaging the opinions of all annotators. We introduce a new non-aggregated dataset, namely StudEmo, that contains 5,182 customer reviews, each annotated by 25 people with intensities of eight emotions from Plutchik's model, extended with valence and arousal. We also propose three personalized models that use not only textual content but also the individual human perspective, providing the model with different approaches to learning human representations. The experiments were carried out as a multitask classification on two datasets: our StudEmo dataset and GoEmotions dataset, which contains 28 emotional categories. The proposed personalized methods significantly improve prediction results, especially for emotions that have low inter-annotator agreement.

Original dataset: GoEmotions dataset Filtered using the following mapping based on the basic emotions found in Plutchik's Wheel of Emotions and filtered to use only the sentences with one label wheel_dict = { "joy": [ "joy", "amusement", "excitement", "gratitude", "pride", "relief", "admiration", "love", "optimism", ], "trust": ["approval", "caring"], "fear": ["fear", "nervousness"], "surprise":… See the full description on the dataset page: https://huggingface.co/datasets/Jsevisal/go_emotions_wheel_unilabel.

数据名称：Go Emotions：Google 情绪数据集 数据说明：Google AI GoEmotions 数据集由 Reddit 用户的评论组成，并附有情绪色彩标签。GoEmotions 旨在训练神经网络对文本的音调进行深入分析。大多数现有的情绪分类数据集涵盖某些领域（例如新闻标题和电影字幕），规模较小，并且仅使用六种基本情绪（愤怒、惊讶、厌恶、喜悦、恐惧和悲伤）的量表。扩大数据集中考虑的情绪范围可以创建更灵敏的聊天机器人、用于检测互联网上危险行为的模型，以及改善客户支持服务。 数据时间：更新于 2021年 数据来源：互联网数据

Original dataset: GoEmotions dataset Filtered using "ekman_mapping.json" from original dataset repo and filtered to use only the sentences with one label Dataset contains 7 emotion labels as per Dr. Ekman theory. Labels are as follows: 0: anger 1: disgust 2: fear 3: joy 4: sadness 5: surprise 6: neutral

tgelton/GoEmotions dataset hosted on Hugging Face and contributed by the HF Datasets community

sangkm/go-emotions-cleaned dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset Card for RuIzardEmotions

  Dataset Summary

The RuIzardEmotions dataset is a high-quality translation of the go-emotions dataset and the other emotion-detection dataset. It contains 30k Reddit comments labeled for 10 emotion categories (joy, sadness, anger, enthusiasm, surprise, disgust, fear, guilt, shame and neutral). The datasets were translated using the accurate translator DeepL and additional processing. The idea for the dataset was inspired by the Izard's… See the full description on the dataset page: https://huggingface.co/datasets/Djacon/ru-izard-emotions.

Lithuanian GoEmotions dataset

The original dataset: GoEmotions (paper). The derived dataset was machine translated from English into Lithuanian using the free Google Translate API (with deep-translator). The translation script: from datasets import load_dataset from deep_translator import GoogleTranslator from deep_translator.exceptions import TranslationNotFound

original_dataset = load_dataset("go_emotions", name="simplified") translator = GoogleTranslator(source="en"… See the full description on the dataset page: https://huggingface.co/datasets/AiLab-IMCS-UL/go_emotions-lt.

Dataset Card for CAMEO

Dataset to accompany the EMNLP'23 paper titled: "Misery Loves Complexity: Exploring Linguistic Complexity in the Context of Emotion Detection".

  Dataset Details

50,000 subset from the GoEmotions Dataset automatically annotated with the following linguistic complexity measures:

idt: Incomplete Dependency Theory dlt: Dependency Locality Theory nnd: Nested-Nouns Distance le: Left-embededness percentage_polysyllable_words: % of polysyllable words… See the full description on the dataset page: https://huggingface.co/datasets/pranaydeeps/CAMEO.

Russian GoEmotions dataset

The original dataset: GoEmotions (paper). The derived dataset was machine translated from English into Russian using the free Google Translate API (with deep-translator). The translation script: from datasets import load_dataset from deep_translator import GoogleTranslator from deep_translator.exceptions import TranslationNotFound

original_dataset = load_dataset("go_emotions", name="simplified") translator = GoogleTranslator(source="en", target="ru")

def… See the full description on the dataset page: https://huggingface.co/datasets/AiLab-IMCS-UL/go_emotions-ru.

Original dataset: GoEmotions dataset The dataset was machine translated to Latvian using free Google Translate API. Tool used for translation: deep-translator Translation script: from datasets import load_dataset from deep_translator import GoogleTranslator from deep_translator.exceptions import TranslationNotFound

original_dataset = load_dataset("go_emotions", name="simplified") translator = GoogleTranslator(source="en", target="lv")

def translate_batch(batch): original_text =… See the full description on the dataset page: https://huggingface.co/datasets/SkyWater21/lv_go_emotions.