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--- |
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pretty_name: "MoodPulse: Processed Data and Embeddings for Emotion Analysis" |
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license: mit |
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language: |
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- en |
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tags: |
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- emotion-classification |
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- affective-computing |
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- text-classification |
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- goemotions |
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- distilbert |
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- embeddings |
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task_categories: |
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- text-classification |
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dataset_info: |
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source_dataset: "GoEmotions" |
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includes: |
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- raw data |
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- tokenized data |
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- transformer embeddings |
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processed_by: "AffectiveLens pipeline" |
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--- |
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# 📊 MoodPulse: Processed Data and Embeddings for Emotion Analysis |
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**MoodPulse** provides a self-contained dataset repository for use with the [AffectiveLens](https://github.com/your-username/AffectiveLens) project—an end-to-end NLP pipeline for emotion detection in text. It includes the full processing stack from raw text to final DistilBERT-based sentence embeddings, allowing researchers to bypass time-consuming preprocessing and directly train or benchmark models. |
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--- |
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## 🧾 Dataset Description |
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This dataset builds upon the original **[GoEmotions](https://github.com/google-research/goemotions)** dataset by Google Research, which includes 58k carefully curated Reddit comments labeled with 28 fine-grained emotions. |
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In **MoodPulse**, these labels are condensed into **three mutually exclusive emotion classes**: |
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- Positive |
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- Neutral |
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- Negative |
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The dataset is structured to support every phase of the AffectiveLens pipeline: |
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- Raw CSVs |
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- Tokenized data in Hugging Face `datasets` format |
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- Precomputed `DistilBERT` embeddings |
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This enables full reproduction of results without requiring re-tokenization or embedding computation. |
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--- |
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## 🗂️ Dataset Structure |
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The dataset is organized into logical folders corresponding to different stages of processing: |
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``` |
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/ |
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├── data/ |
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│ ├── full\_dataset/ |
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│ │ ├── goemotions\_1.csv |
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│ │ ├── goemotions\_2.csv |
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│ │ └── goemotions\_3.csv |
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│ │ |
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│ ├── processed/ |
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│ │ ├── GoEmotions\_Tokenized\_Train\_Pool/ |
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│ │ └── GoEmotions\_Tokenized\_Test/ |
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│ │ |
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│ └── embeddings/ |
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│ ├── MentalTrain/ |
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│ └── MentalTest/ |
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```` |
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### 📁 Folder Descriptions |
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- **`data/full_dataset/`** |
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Original GoEmotions CSV files split into parts. |
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- **`data/processed/`** |
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Tokenized datasets using Hugging Face `datasets` format, ready for embedding extraction. |
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- **`data/embeddings/`** |
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Final DistilBERT `[CLS]` token embeddings for the training and test sets. These are saved as Hugging Face datasets and ready for model input. |
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--- |
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## 🚀 How to Use |
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You can load the tokenized data or precomputed embeddings directly using the Hugging Face `datasets` library. |
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```python |
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from datasets import load_dataset |
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# Define repository ID and folder to load |
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repo_id = "psyrishi/MoodPulse" |
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data_folder = "data/embeddings/MentalTrain" # or "data/embeddings/MentalTest" |
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# Load the dataset split |
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train_embeddings = load_dataset(repo_id, data_dir=data_folder, split='train') |
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print("Sample entry:") |
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print(train_embeddings[0]) |
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# Access embeddings and labels |
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embedding_vector = train_embeddings[0]['cls_embedding'] |
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label_vector = train_embeddings[0]['labels'] |
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```` |
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> 💡 Tip: You can replace `data_dir` to load the tokenized datasets instead, if desired. |
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--- |
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## 📌 Use Cases |
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* Train or benchmark emotion classification models using high-quality, preprocessed embeddings. |
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* Compare performance of traditional ML models vs. transformer-based models. |
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* Build emotion-aware applications for mental health, customer feedback, or social media monitoring. |
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--- |
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## 📚 Citation |
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This dataset is a **processed derivative** of the original GoEmotions dataset: |
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```bibtex |
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@inproceedings{demszky2020goemotions, |
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title={GoEmotions: A Dataset of Fine-Grained Emotions}, |
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author={Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith}, |
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booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL)}, |
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year={2020} |
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} |
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``` |
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If you use **MoodPulse** in your work, please cite both the original GoEmotions authors and link back to this repository. |
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--- |
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## ⚖️ Licensing |
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* **Original data**: Provided under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license by Google Research. |
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* **Code and processing logic**: Provided under the **MIT License**. |
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Please refer to the [LICENSE](./LICENSE) file for full details. |
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--- |
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## 🙏 Acknowledgments |
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Special thanks to Google Research for the creation and open release of the GoEmotions dataset, and to the Hugging Face team for providing the open-source tools that made this processing pipeline possible. |
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--- |
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## 🔗 Related Projects |
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* [GoEmotions Dataset (Google)](https://github.com/google-research/goemotions) |
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* [AffectiveLens](https://github.com/psywarrior1998/AffectiveLens) — Emotion detection pipeline built on top of this dataset. |
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--- |
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