README.md

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license: cc-by-nc-4.0
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# Synthetic Emotion Classification Dataset

## Dataset Overview

This dataset contains **13,970 synthetic text samples** labeled across **7 emotion classes**:

- Anger  
- Happiness  
- Sad  
- Surprise  
- Hate  
- Love  
- Fun  

The data was generated using **Mistral-7B**, producing diverse and realistic short-to-medium length emotional expressions.  
It is suitable for benchmarking NLP models such as **RNNs, BERT-based models, and LLMs** in multi-class emotion detection.

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## Example

**Text:**

> John clenched his fists, his face turning red as he paced back and forth in the room.  
> His eyes flashed with frustration as he muttered under his breath about the latest setback at work.

**Emotion:** `Anger`

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## Dataset Statistics

- **Total samples:** 13,970  
- **Columns:** `text`, `emotion`  
- **Emotion classes:** 7 (balanced)  
- **Generator model:** Mistral-7B  
- **Data type:** Fully synthetic  
- **File format:** CSV  

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## Use Cases

- Train or fine-tune emotion classifiers (LSTM, GRU, DistilBERT, RoBERTa)
- Compare traditional ML models vs transformer-based models
- Evaluate LLMs in zero-shot or few-shot emotion classification
- Augment real-world datasets with balanced emotion samples
- Educational projects in NLP and sentiment analysis

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## Notes

- This dataset is **fully synthetic** and contains **no personally identifiable information (PII)**.
- Labels were auto-generated using structured LLM prompting.
- Users should check for:
  - Duplicate samples
  - Prompt-induced stylistic bias
  - Generalization limits before production use

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## Compatibility

- Hugging Face `datasets` library
- Kaggle notebooks
- PyTorch / TensorFlow NLP pipelines