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 contains 58K carefully curated Reddit comments labeled for 27 emotion categories: 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.

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.

GoEmotions 5-Point Sentiment Dataset

This dataset is a modified version of the GoEmotions dataset created by Google. The original dataset consists of 58k carefully curated Reddit comments labeled with 27 fine-grained emotion categories plus a neutral label.

  📘 About This Version

This version maps the original GoEmotions emotion labels into a 5-point sentiment scale, making it more suitable for traditional sentiment analysis tasks:

Original Label(s) Mapped Sentiment… See the full description on the dataset page: https://huggingface.co/datasets/spacesedan/goemotions-5point-sentiment.

Dataset

This dataset was created by Enes Ozturk

Contents

This dataset is designed for building and evaluating sentiment and emotion classification models in Natural Language Processing (NLP). It includes two well-known datasets:

• GoEmotions: A fine-grained emotion dataset developed by Google, containing 58k English Reddit comments labeled with 27 emotion categories plus Neutral.
• DailyDialog: A high-quality multi-turn dialog dataset with emotion and intent annotations, ideal for dialog modeling and conversational AI.

Each dataset is provided in CSV format and includes text samples along with corresponding emotion or sentiment labels.

This dataset is useful for:

• Emotion classification and multi-label sentiment analysis.
• Fine-tuning transformer models (e.g., BERT, RoBERTa).
• Training empathetic conversational agents.
• Research in affective computing and human-centered AI.

spacesedan/goemotions-5point-sentiment-refined dataset hosted on Hugging Face and contributed by the HF Datasets community

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

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.

alisha4walunj/goemotions-binary dataset hosted on Hugging Face and contributed by the HF Datasets community

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.

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.

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

GoEmotions Spanish

  A Spanish translation (using EasyNMT) of the GoEmotions dataset.





  For more information check the official Model Card

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.

Dataset Card for GoEmotions

  Dataset Summary

The RuGoEmotions dataset contains 34k Reddit comments labeled for 9 emotion categories (joy, interest, surprice, sadness, anger, disgust, fear, guilt and neutral). The dataset already 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 Russian.

  Dataset Structure





  Data… See the full description on the dataset page: https://huggingface.co/datasets/Djacon/ru_goemotions.