---
license: apache-2.0
task_categories:
- text-classification
language:
- en
tags:
- emotion
pretty_name: Go-Emotions (Preprocessed)
size_categories:
- 10K<n<100K
task_ids:
- multi-label-classification
dataset_info:
  features:
  - name: text
    dtype: string
  - name: labels
    dtype: sequence
    feature:
      dtype: int32
  splits:
  - name: train
  - name: validation
  - name: test
---

# GoEmotions (Preprocessed)

## Dataset Description

This dataset contains a **preprocessed and standardized version of GoEmotions** for **multi-label emotion classification**.  
It is designed for seamless use with **transformer-based language models** and consistent benchmarking alongside other emotion datasets.

The preprocessing ensures unified label representations and removes unnecessary metadata while preserving the original semantic and emotional content.

---

## Supported Tasks

- Multi-label emotion classification
- Emotion representation learning
- Cross-dataset benchmarking

---

## Dataset Structure

The dataset is split into:

- `train`
- `validation`
- `test`

Each split follows the same schema.

---

## Data Format

Each example consists of:

- **`text`** (`string`): Preprocessed text input
- **`labels`** : Multi-one-hot encoded emotion labels (length = 28)

Each label is binary:
- `1` → emotion present  
- `0` → emotion absent  

Multiple emotions may be active for a single sample.

---

## Emotion Label Mapping (28 Classes)

| Index | Emotion |
|------:|---------|
| 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 | Nervousness |
| 20 | Optimism |
| 21 | Pride |
| 22 | Realization |
| 23 | Relief |
| 24 | Remorse |
| 25 | Sadness |
| 26 | Surprise |
| 27 | Neutral |

---

## Preprocessing Details

The following preprocessing steps were applied:

- Conversion to **multi-one-hot label encoding**
- Standardization to a fixed **28-class emotion space**
- Removal of extraneous metadata
- Text normalization
- Preprocessing applied **before tokenization**

---

## Intended Use

This dataset is intended for:

- Training and evaluating multi-label emotion classifiers
- Transformer-based NLP experiments
- Emotion analysis and representation learning

---

## Limitations

- The dataset contains **preprocessed text only**
- Raw GoEmotions data is not included
- Emotion annotations reflect annotator perception and may contain subjectivity

---

## Citation

If you use this dataset, please cite the original GoEmotions paper:

```bibtex
@inproceedings{demszky2020goemotions,
  title     = {GoEmotions: 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},
  year      = {2020}
}

also cite our paper:
@article{DualObjectivesEmotion2026,
  author  = {Arnab Karmakar, Subinoy Bera}
  title   = {Do We Need a Classifier? Dual Objectives Go Beyond Baselines in Fine-Grained Emotion Classification},
  year    = {2026},
  journal = {Research Gate},
  doi     = {10.13140/RG.2.2.16084.46728},
  url     = {https://www.researchgate.net/publication/399329430_Do_We_Need_a_Classifier_Dual_Objectives_Go_Beyond_Baselines_in_Fine-Grained_Emotion_Classification}
}