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---
language:
- en
tags:
- text-classification
- distilbert
- financial-emotion-analysis
- emotion
- twitter
- stocktwits
- pytorch
license: mit
datasets:
- emotion
metrics:
- accuracy
- precision
- recall
- f1
widget:
- text: "to the moon 🚀🚀🚀."
---
# EmTract (DistilBERT-Base-Uncased)
## Model Description
`emtract-distilbert-base-uncased-emotion` is a specialized model finetuned on a combination of [unify-emotion-datasets](https://github.com/sarnthil/unify-emotion-datasets), containing around 250K texts labeled across seven emotion categories: neutral, happy, sad, anger, disgust, surprise, and fear. This model was later adapted to a smaller set of 10K hand-tagged messages from StockTwits. The model is designed to excel at emotion detection in financial social media content such as that found on StockTwits.
Model parameters were as follows: sequence length of 64, learning rate of 2e-5, batch size of 128, trained for 8 epochs. For steps on how to use the model for inference, please refer to the accompanying Inference.ipynb notebook.
## Training Data
The training data was obtained from the Unify Emotion Datasets available at [here](https://github.com/sarnthil/unify-emotion-datasets).
## Evaluation Metrics
The model was evaluated using the following metrics:
- Accuracy
- Precision
- Recall
- F1-score
## Research
The underlying research for emotion extraction from social media can be found in the paper [EmTract: Extracting Emotions from Social Media](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3975884).
### Citation
Vamossy, Domonkos F., and Rolf Skog. "EmTract: Extracting Emotions from Social Media." Available at SSRN 3975884 (2023).
### Research using EmTract
[Social Media Emotions and IPO Returns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4384573)
[Investor Emotions and Earnings Announcements](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3626025])
## License
This project is licensed under the terms of the MIT license. |