FinTwitBERT sentiment EU AI Act Compliance Profile

FinTwitBERT-sentiment

FinTwitBERT-sentiment is a finetuned model for classifying the sentiment of financial tweets. It uses FinTwitBERT as a base model, which has been pre-trained on 10 million financial tweets. 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. Therefore this model performs great on informal financial texts, seen on social media.

Intended Uses

FinTwitBERT-sentiment is intended for classifying financial tweets or other financial social media texts.

Dataset

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.

• TimKoornstra/financial-tweets-sentiment: 38,091 human-labeled tweets
• TimKoornstra/synthetic-financial-tweets-sentiment: 1,428,771 synethtic tweets

More Information

For a comprehensive overview, including the training setup and analysis of the model, visit the FinTwitBERT GitHub repository.

Usage

Using HuggingFace's transformers library the model and tokenizers can be converted into a pipeline for text classification.

from transformers import pipeline

# Create a sentiment analysis pipeline
pipe = pipeline(
    "sentiment-analysis",
    model="StephanAkkerman/FinTwitBERT-sentiment",
)

# Get the predicted sentiment
print(pipe("Nice 9% pre market move for $para, pump my calls Uncle Buffett 🤑"))

Citing & Authors

If you use FinTwitBERT or FinTwitBERT-sentiment in your research, please cite us as follows, noting that both authors contributed equally to th

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Base Model