
# 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 financial social media can be found on: [arxiv](https://arxiv.org/abs/2112.03868) and [SSRN](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3975884).

### Citation

Please cite the following if you use this model:

Vamossy, Domonkos F., and Rolf Skog. "EmTract: Extracting Emotions from Social Media." Available at SSRN 3975884 (2023).

BibTex citation:

```
@article{vamossy2023emtract,
  title={EmTract: Extracting Emotions from Social Media},
  author={Vamossy, Domonkos F and Skog, Rolf},
  journal={Available at SSRN 3975884},
  year={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.
{"language": ["en"], "license": "mit", "tags": ["text-classification", "distilbert", "financial-emotion-analysis", "emotion", "twitter", "stocktwits", "pytorch"], "datasets": ["emotion"], "metrics": ["accuracy", "precision", "recall", "f1"], "widget": [{"text": "to the moon \ud83d\ude80\ud83d\ude80\ud83d\ude80"}]}
transformers pytorch safetensors distilbert text-classification financial-emotion-analysis emotion twitter stocktwits en dataset:emotion arxiv:2112.03868 license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #financial-emotion-analysis #emotion #twitter #stocktwits #en #dataset-emotion #arxiv-2112.03868 #license-mit #autotrain_compatible #endpoints_compatible #region-us 


# EmTract (DistilBERT-Base-Uncased)

## Model Description

'emtract-distilbert-base-uncased-emotion' is a specialized model finetuned on a combination of 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 URL notebook.

## Training Data

The training data was obtained from the Unify Emotion Datasets available at here.

## Evaluation Metrics

The model was evaluated using the following metrics:
- Accuracy
- Precision
- Recall
- F1-score

## Research

The underlying research for emotion extraction from financial social media can be found on: arxiv and SSRN.

Please cite the following if you use this model:

Vamossy, Domonkos F., and Rolf Skog. "EmTract: Extracting Emotions from Social Media." Available at SSRN 3975884 (2023).

BibTex citation:



### Research using EmTract

Social Media Emotions and IPO Returns

Investor Emotions and Earnings Announcements

## License

This project is licensed under the terms of the MIT license.
# EmTract (DistilBERT-Base-Uncased) ## Model Description

'emtract-distilbert-base-uncased-emotion' is a specialized model finetuned on a combination of 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 URL notebook. ## Training Data

The training data was obtained from the Unify Emotion Datasets available at here. ## Evaluation Metrics

The model was evaluated using the following metrics:
- Accuracy
- Precision
- Recall
- F1-score ## Research

The underlying research for emotion extraction from financial social media can be found on: arxiv and SSRN.

Please cite the following if you use this model:

Vamossy, Domonkos F., and Rolf Skog. "EmTract: Extracting Emotions from Social Media." Available at SSRN 3975884 (2023).

BibTex citation: ### Research using EmTract

Social Media Emotions and IPO Returns

Investor Emotions and Earnings Announcements ## License

This project is licensed under the terms of the MIT license.
TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #financial-emotion-analysis #emotion #twitter #stocktwits #en #dataset-emotion #arxiv-2112.03868 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
 # EmTract (DistilBERT-Base-Uncased) ## Model Description

'emtract-distilbert-base-uncased-emotion' is a specialized model finetuned on a combination of 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 URL notebook. ## Training Data

The training data was obtained from the Unify Emotion Datasets available at here. ## Evaluation Metrics

The model was evaluated using the following metrics:
- Accuracy
- Precision
- Recall
- F1-score ## Research

The underlying research for emotion extraction from financial social media can be found on: arxiv and SSRN.

Please cite the following if you use this model:

Vamossy, Domonkos F., and Rolf Skog. "EmTract: Extracting Emotions from Social Media." Available at SSRN 3975884 (2023).

BibTex citation: ### Research using EmTract

Social Media Emotions and IPO Returns

Investor Emotions and Earnings Announcements ## License

This project is licensed under the terms of the MIT license.
passage: TAGS
#transformers #pytorch #safetensors #distilbert #text-classification #financial-emotion-analysis #emotion #twitter #stocktwits #en #dataset-emotion #arxiv-2112.03868 #license-mit #autotrain_compatible #endpoints_compatible #region-us 
# EmTract (DistilBERT-Base-Uncased)## Model Description

'emtract-distilbert-base-uncased-emotion' is a specialized model finetuned on a combination of 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 URL notebook.## Training Data

The training data was obtained from the Unify Emotion Datasets available at here.## Evaluation Metrics

The model was evaluated using the following metrics:
- Accuracy
- Precision
- Recall
- F1-score## Research

The underlying research for emotion extraction from financial social media can be found on: arxiv and SSRN.

Please cite the following if you use this model:

Vamossy, Domonkos F., and Rolf Skog. "EmTract: Extracting Emotions from Social Media." Available at SSRN 3975884 (2023).

BibTex citation:### Research using EmTract

Social Media Emotions and IPO Returns

Investor Emotions and Earnings Announcements## License

This project is licensed under the terms of the MIT license.