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--- |
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license: mit |
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datasets: |
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- TimKoornstra/financial-tweets-sentiment |
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- TimKoornstra/synthetic-financial-tweets-sentiment |
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language: |
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- en |
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metrics: |
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- accuracy |
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- f1 |
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pipeline_tag: text-classification |
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tags: |
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- NLP |
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- BERT |
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- FinBERT |
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- FinTwitBERT |
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- sentiment |
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- finance |
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- financial-analysis |
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- sentiment-analysis |
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- financial-sentiment-analysis |
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- twitter |
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- tweets |
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- tweet-analysis |
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- stocks |
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- stock-market |
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- crypto |
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- cryptocurrency |
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base_model: StephanAkkerman/FinTwitBERT |
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widget: |
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- text: Nice 9% pre market move for $para, pump my calls Uncle Buffett 🤑 |
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example_title: Bullish Crypto Tweet |
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- text: It is about damn time that my $ARB and $ETH bags pump FFS. 🚀 |
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example_title: Bullish Crypto Tweet 2 |
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- text: $SPY $SPX closed higher 8th consecutive weeks. Last time it closed 9th straight was 20 years ago. |
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example_title: Bullish Stock Tweet |
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- text: $TCBP Lowest float stock in the market. Float just 325k. Don’t sell for pennies, this one will be a monster. Still early |
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example_title: Bullish Stock Tweet 2 |
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- text: Italian companies braced for more political uncertainty |
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example_title: Bearish News |
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--- |
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# FinTwitBERT-sentiment |
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FinTwitBERT-sentiment is a finetuned model for classifying the sentiment of financial tweets. It uses [FinTwitBERT](https://huggingface.co/StephanAkkerman/FinTwitBERT) as a base model, which has been pre-trained on 10 million financial tweets. |
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This approach ensures that the FinTwitBERT-sentiment has seen enough financial tweets, which have an informal nature, compared to other financial texts, such as news headlines. |
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Therefore this model performs great on informal financial texts, seen on social media. |
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## Intended Uses |
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FinTwitBERT-sentiment is intended for classifying financial tweets or other financial social media texts. |
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## Dataset |
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FinTwitBERT-sentiment has been trained on two datasets. One being a collection of several financial tweet datasets and the other a synthetic dataset created out of the first. |
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- [TimKoornstra/financial-tweets-sentiment](https://huggingface.co/datasets/TimKoornstra/financial-tweets-sentiment): 38,091 human-labeled tweets |
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- [TimKoornstra/synthetic-financial-tweets-sentiment](https://huggingface.co/datasets/TimKoornstra/synthetic-financial-tweets-sentiment): 1,428,771 synethtic tweets |
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## More Information |
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For a comprehensive overview, including the training setup and analysis of the model, visit the [FinTwitBERT GitHub repository](https://github.com/TimKoornstra/FinTwitBERT). |
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## Usage |
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Using [HuggingFace's transformers library](https://huggingface.co/docs/transformers/index) the model and tokenizers can be converted into a pipeline for text classification. |
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```python |
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from transformers import pipeline |
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# Create a sentiment analysis pipeline |
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pipe = pipeline( |
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"sentiment-analysis", |
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model="StephanAkkerman/FinTwitBERT-sentiment", |
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) |
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# Get the predicted sentiment |
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print(pipe("Nice 9% pre market move for $para, pump my calls Uncle Buffett 🤑")) |
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``` |
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## Citing & Authors |
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If you use FinTwitBERT or FinTwitBERT-sentiment in your research, please cite us as follows, noting that both authors contributed equally to this work: |
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``` |
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@misc{FinTwitBERT, |
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author = {Stephan Akkerman, Tim Koornstra}, |
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title = {FinTwitBERT: A Specialized Language Model for Financial Tweets}, |
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year = {2023}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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howpublished = {\url{https://github.com/TimKoornstra/FinTwitBERT}} |
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} |
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``` |
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Additionally, if you utilize the sentiment classifier, please cite: |
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``` |
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@misc{FinTwitBERT-sentiment, |
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author = {Stephan Akkerman, Tim Koornstra}, |
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title = {FinTwitBERT-sentiment: A Sentiment Classifier for Financial Tweets}, |
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year = {2023}, |
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publisher = {Hugging Face}, |
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howpublished = {\url{https://huggingface.co/StephanAkkerman/FinTwitBERT-sentiment}} |
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} |
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``` |
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## License |
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This project is licensed under the MIT License. See the [LICENSE](https://choosealicense.com/licenses/mit/) file for details. |
