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--- |
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language: |
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- en |
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tags: |
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- text-classification |
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- distilbert |
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- financial-emotion-analysis |
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- emotion |
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- twitter |
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- stocktwits |
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- pytorch |
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license: mit |
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datasets: |
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- emotion |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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widget: |
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- text: "to the moon 🚀🚀🚀" |
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--- |
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# EmTract (DistilBERT-Base-Uncased) |
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## Model Description |
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`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. |
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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. |
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## Training Data |
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The training data was obtained from the Unify Emotion Datasets available at [here](https://github.com/sarnthil/unify-emotion-datasets). |
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## Evaluation Metrics |
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The model was evaluated using the following metrics: |
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- Accuracy |
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- Precision |
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- Recall |
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- F1-score |
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## Research |
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The underlying research for emotion extraction from financial social media can be found in: [EmTract: Extracting Emotions from Social Media](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3975884). |
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### Citation |
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Please cite the following if you use this model: |
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Vamossy, Domonkos F., and Rolf Skog. "EmTract: Extracting Emotions from Social Media." Available at SSRN 3975884 (2023). |
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BibTex citation: |
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``` |
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@article{vamossy2023emtract, |
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title={EmTract: Extracting Emotions from Social Media}, |
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author={Vamossy, Domonkos F and Skog, Rolf}, |
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journal={Available at SSRN 3975884}, |
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year={2023} |
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} |
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``` |
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### Research using EmTract |
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[Social Media Emotions and IPO Returns](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4384573) |
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[Investor Emotions and Earnings Announcements](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3626025]) |
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## License |
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This project is licensed under the terms of the MIT license. |
