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| license: cc-by-nc-4.0 |
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| # Synthetic Emotion Classification Dataset |
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| ## Dataset Overview |
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| This dataset contains **13,970 synthetic text samples** labeled across **7 emotion classes**: |
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| - Anger |
| - Happiness |
| - Sad |
| - Surprise |
| - Hate |
| - Love |
| - Fun |
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| 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 |
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| **Text:** |
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| > 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. |
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| **Emotion:** `Anger` |
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| ## Dataset Statistics |
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| - **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 |
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| - 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 |
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| - 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 |
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| - Hugging Face `datasets` library |
| - Kaggle notebooks |
| - PyTorch / TensorFlow NLP pipelines |
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