README.md

metadata

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:

@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}
}