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.
