Financial-RoBERTa

Financial-RoBERTa is a pre-trained NLP model to analyze sentiment of financial text including:

• Financial Statements,
• Earnings Announcements,
• Earnings Call Transcripts,
• Corporate Social Responsibility (CSR) Reports,
• Environmental, Social, and Governance (ESG) News,
• Financial News,
• Etc.

Financial-RoBERTa is built by further training and fine-tuning the RoBERTa Large language model using a large corpus created from 10k, 10Q, 8K, Earnings Call Transcripts, CSR Reports, ESG News, and Financial News text.

The model will give softmax outputs for three labels: Positive , Negative or Neutral .

How to perform sentiment analysis:

The easiest way to use the model for single predictions is Hugging Face's sentiment analysis pipeline, which only needs a couple lines of code as shown in the following example:

  
from transformers import pipeline
sentiment_analysis = pipeline("sentiment-analysis",model="soleimanian/financial-roberta-large-sentiment")
print(sentiment_analysis("In fiscal 2021, we generated a net yield of approximately 4.19% on our investments, compared to approximately 5.10% in fiscal 2020."))
  

I provide an example script via Google Colab . You can load your data to a Google Drive and run the script for free on a Colab.

Citation and contact:

Please cite this paper when you use the model. Feel free to reach out to mohammad.soleimanian@concordia.ca with any questions or feedback you may have.