---
license: mit
task_categories:
- text-classification
- sentiment-analysis
language:
- en
tags:
- emotion-detection
- emotion-classification
- nlp
- sentiment
pretty_name: 20-Emotion Text Classification Dataset
size_categories:
- 10K<n<100K
---

# 20-Emotion Text Classification Dataset

A comprehensive dataset for fine-grained emotion classification containing 79,595 sentences labeled with 20 distinct emotions.

## Dataset Description

This dataset is designed for training emotion classification models that can detect nuanced emotional states in text. Unlike basic sentiment analysis (positive/negative/neutral), this dataset provides fine-grained emotion labels that better capture the complexity of human emotions.

### Dataset Summary

- **Total samples**: 79,595
- **Number of emotions**: 20
- **Language**: English
- **Task**: Multi-class text classification
- **Format**: CSV

### Emotions Covered

The dataset includes the following 20 emotions with balanced distribution:

1. **happiness** (7,797 samples)
2. **disgust** (4,315 samples)
3. **jealousy** (4,081 samples)
4. **surprise** (4,062 samples)
5. **gratitude** (4,004 samples)
6. **relief** (3,964 samples)
7. **guilt** (3,961 samples)
8. **anger** (3,860 samples)
9. **disappointment** (3,832 samples)
10. **embarrassment** (3,788 samples)
11. **anxiety** (3,776 samples)
12. **pride** (3,772 samples)
13. **hope** (3,770 samples)
14. **loneliness** (3,751 samples)
15. **excitement** (3,643 samples)
16. **fear** (3,574 samples)
17. **sadness** (3,559 samples)
18. **confusion** (3,429 samples)
19. **love** (3,365 samples)
20. **frustration** (3,292 samples)

## Dataset Structure

### Data Fields

- `sentence`: The original text expressing an emotion (string)
- `emotion`: The emotion label (string, one of 20 emotions)
- `cleaned_text`: Preprocessed/cleaned version of the sentence (string)

### Data Example

```json
{
  "sentence": "I'm so furious with you right now.",
  "emotion": "anger",
  "cleaned_text": "i'm so furious with you right now."
}
```

## Usage

### Load with Pandas

```python
import pandas as pd

# Load the dataset
df = pd.read_csv("hf://datasets/shreyaspulle98/emotion-dataset-20-emotions/emotion_dataset_v5_clean.csv")

# View emotion distribution
print(df['emotion'].value_counts())

# Example: Get all samples for a specific emotion
anger_samples = df[df['emotion'] == 'anger']
```

### Load with HuggingFace Datasets

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("shreyaspulle98/emotion-dataset-20-emotions")

# Access the data
print(dataset)
```

### Train/Test Split Example

```python
from sklearn.model_selection import train_test_split

# Load data
df = pd.read_csv("hf://datasets/shreyaspulle98/emotion-dataset-20-emotions/emotion_dataset_v5_clean.csv")

# Split into train/test (80/20)
train_df, test_df = train_test_split(df, test_size=0.2, stratify=df['emotion'], random_state=42)

print(f"Training samples: {len(train_df)}")
print(f"Testing samples: {len(test_df)}")
```

## Dataset Creation

### Source Data

This dataset was synthetically generated using advanced language models (DeepInfra API with various LLMs) to create diverse, natural emotional expressions for each of the 20 emotion categories.

### Data Collection Process

1. **Prompt Engineering**: Carefully designed prompts to generate natural emotional expressions
2. **Multi-Model Generation**: Used multiple LLMs to ensure variety and quality
3. **Quality Control**: Filtered and cleaned samples to remove duplicates and low-quality text
4. **Balancing**: Ensured relatively balanced distribution across all 20 emotions

### Preprocessing

- Text normalization (lowercase)
- Special character handling
- Duplicate removal
- Quality filtering

## Use Cases

This dataset is ideal for:

- **Emotion Classification Models**: Train models to detect fine-grained emotions in text
- **Sentiment Analysis**: Go beyond positive/negative to understand nuanced emotions
- **Mental Health Applications**: Detect emotional states in user communications
- **Customer Service**: Understand customer emotions in feedback and support tickets
- **Social Media Analysis**: Analyze emotional content in posts and comments
- **Chatbots & Virtual Assistants**: Enable emotion-aware responses
- **Content Moderation**: Detect concerning emotional states in user-generated content

## Limitations

- **Synthetic Data**: The dataset is synthetically generated, not collected from real human conversations
- **English Only**: Currently limited to English language
- **Cultural Context**: May not fully capture cultural variations in emotional expression
- **Ambiguity**: Some sentences may express multiple emotions, but are labeled with single emotion
- **Modern Language**: Generated with current language models, may not capture historical or evolving language use

## Ethical Considerations

- This dataset should be used responsibly, especially in sensitive applications like mental health
- Emotion detection models should complement, not replace, human judgment
- Consider privacy implications when deploying emotion detection in user-facing applications
- Be aware of potential biases in synthetic data generation

## Citation

If you use this dataset in your research or applications, please cite:

```bibtex
@dataset{emotion_dataset_20_2025,
  author = {Shreyas Pulle},
  title = {20-Emotion Text Classification Dataset},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/datasets/shreyaspulle98/emotion-dataset-20-emotions}
}
```

## License

This dataset is released under the MIT License.

## Contact

For questions, issues, or suggestions:
- HuggingFace: [@shreyaspulle98](https://huggingface.co/shreyaspulle98)
- Dataset Repository: [emotion-dataset-20-emotions](https://huggingface.co/datasets/shreyaspulle98/emotion-dataset-20-emotions)

## Acknowledgments

Generated using DeepInfra API and various open-source language models.