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README.md

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----
-license: mit
----
+
+---
+pretty_name: "MoodPulse: Processed Data and Embeddings for Emotion Analysis"
+license: mit
+language:
+- en
+tags:
+- emotion-classification
+- affective-computing
+- text-classification
+- goemotions
+- distilbert
+- embeddings
+task_categories:
+- text-classification
+dataset_info:
+  source_dataset: "GoEmotions"
+  includes:
+    - raw data
+    - tokenized data
+    - transformer embeddings
+  processed_by: "AffectiveLens pipeline"
+---
+
+# 📊 MoodPulse: Processed Data and Embeddings for Emotion Analysis
+
+**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.
+
+---
+
+## 🧾 Dataset Description
+
+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.
+
+In **MoodPulse**, these labels are condensed into **three mutually exclusive emotion classes**:
+- Positive
+- Neutral
+- Negative
+
+The dataset is structured to support every phase of the AffectiveLens pipeline:
+- Raw CSVs
+- Tokenized data in Hugging Face `datasets` format
+- Precomputed `DistilBERT` embeddings
+
+This enables full reproduction of results without requiring re-tokenization or embedding computation.
+
+---
+
+## 🗂️ Dataset Structure
+
+The dataset is organized into logical folders corresponding to different stages of processing:
+
+```
+
+/
+├── data/
+│   ├── full\_dataset/
+│   │   ├── goemotions\_1.csv
+│   │   ├── goemotions\_2.csv
+│   │   └── goemotions\_3.csv
+│   │
+│   ├── processed/
+│   │   ├── GoEmotions\_Tokenized\_Train\_Pool/
+│   │   └── GoEmotions\_Tokenized\_Test/
+│   │
+│   └── embeddings/
+│       ├── MentalTrain/
+│       └── MentalTest/
+
+````
+
+### 📁 Folder Descriptions
+
+- **`data/full_dataset/`**  
+  Original GoEmotions CSV files split into parts.
+
+- **`data/processed/`**  
+  Tokenized datasets using Hugging Face `datasets` format, ready for embedding extraction.
+
+- **`data/embeddings/`**  
+  Final DistilBERT `[CLS]` token embeddings for the training and test sets. These are saved as Hugging Face datasets and ready for model input.
+
+---
+
+## 🚀 How to Use
+
+You can load the tokenized data or precomputed embeddings directly using the Hugging Face `datasets` library.
+
+```python
+from datasets import load_dataset
+
+# Define repository ID and folder to load
+repo_id = "psyrishi/MoodPulse"
+data_folder = "data/embeddings/MentalTrain"  # or "data/embeddings/MentalTest"
+
+# Load the dataset split
+train_embeddings = load_dataset(repo_id, data_dir=data_folder, split='train')
+
+print("Sample entry:")
+print(train_embeddings[0])
+
+# Access embeddings and labels
+embedding_vector = train_embeddings[0]['cls_embedding']
+label_vector = train_embeddings[0]['labels']
+````
+
+> 💡 Tip: You can replace `data_dir` to load the tokenized datasets instead, if desired.
+
+---
+
+## 📌 Use Cases
+
+* Train or benchmark emotion classification models using high-quality, preprocessed embeddings.
+* Compare performance of traditional ML models vs. transformer-based models.
+* Build emotion-aware applications for mental health, customer feedback, or social media monitoring.
+
+---
+
+## 📚 Citation
+
+This dataset is a **processed derivative** of the original GoEmotions dataset:
+
+```bibtex
+@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 (ACL)},
+  year={2020}
+}
+```
+
+If you use **MoodPulse** in your work, please cite both the original GoEmotions authors and link back to this repository.
+
+---
+
+## ⚖️ Licensing
+
+* **Original data**: Provided under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license by Google Research.
+* **Code and processing logic**: Provided under the **MIT License**.
+
+Please refer to the [LICENSE](./LICENSE) file for full details.
+
+---
+
+## 🙏 Acknowledgments
+
+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.
+
+---
+
+## 🔗 Related Projects
+
+* [GoEmotions Dataset (Google)](https://github.com/google-research/goemotions)
+* [AffectiveLens](https://github.com/psywarrior1998/AffectiveLens) — Emotion detection pipeline built on top of this dataset.
+
+---