license: mit
language:
- en
tags:
- NLP
- BERT
- FinBERT
- FinTwitBERT
- sentiment
- finance
- financial-analysis
- sentiment-analysis
- financial-sentiment-analysis
- twitter
- tweets
- tweet-analysis
- stocks
- stock-market
- crypto
- cryptocurrency
datasets:
- StephanAkkerman/stock-market-tweets-data
- StephanAkkerman/financial-tweets
- StephanAkkerman/crypto-stock-tweets
metrics:
- perplexity
widget:
- text: Paris is the [MASK] of France.
example_title: Generic 1
- text: The goal of life is [MASK].
example_title: Generic 2
- text: AAPL is a [MASK] sector stock.
example_title: AAPL
- text: I predict that this stock will go [MASK].
example_title: Stock Direction
- text: $AAPL is the ticker for the company named [MASK].
example_title: Ticker
base_model: yiyanghkust/finbert-pretrain
model-index:
- name: FinTwitBERT
results:
- task:
type: financial-tweet-prediction
name: Financial Tweet Prediction
dataset:
name: Stock Market Tweets Data
type: finance
metrics:
- type: Perplexity
value: 5.022
FinTwitBERT
FinTwitBERT is a language model specifically pre-trained on a large dataset of financial tweets. This specialized BERT model aims to capture the unique jargon and communication style found in the financial Twitter sphere, making it an ideal tool for sentiment analysis, trend prediction, and other financial NLP tasks.
Sentiment Analysis
The FinTwitBERT-sentiment model leverages FinTwitBERT for the sentiment analysis of financial tweets, offering nuanced insights into the prevailing market sentiments.
Dataset
FinTwitBERT is pre-trained on several financial tweets datasets, consisting of tweets mentioning stocks and cryptocurrencies:
- StephanAkkerman/crypto-stock-tweets: 8,024,269 tweets
- StephanAkkerman/stock-market-tweets-data: 923,673 tweets
- StephanAkkerman/financial-tweets: 263,119 tweets
Model Details
Based on the FinBERT model and tokenizer, FinTwitBERT includes additional masks (@USER and [URL]) to handle common elements in tweets. The model underwent 10 epochs of pre-training, with early stopping to prevent overfitting.
More Information
For a comprehensive overview, including the complete training setup details and more, visit the FinTwitBERT GitHub repository.
Usage
Using HuggingFace's transformers library the model and tokenizers can be converted into a pipeline for masked language modeling.
from transformers import pipeline
pipe = pipeline(
"fill-mask",
model="StephanAkkerman/FinTwitBERT",
)
print(pipe("Bitcoin is a [MASK] coin."))
Citing & Authors
If you use FinTwitBERT or FinTwitBERT-sentiment in your research, please cite us as follows, noting that both authors contributed equally to this work:
@misc{FinTwitBERT,
author = {Stephan Akkerman, Tim Koornstra},
title = {FinTwitBERT: A Specialized Language Model for Financial Tweets},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/TimKoornstra/FinTwitBERT}}
}
Additionally, if you utilize the sentiment classifier, please cite:
@misc{FinTwitBERT-sentiment,
author = {Stephan Akkerman, Tim Koornstra},
title = {FinTwitBERT-sentiment: A Sentiment Classifier for Financial Tweets},
year = {2023},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/StephanAkkerman/FinTwitBERT-sentiment}}
}
License
This project is licensed under the MIT License. See the LICENSE file for details.