license: apache-2.0
language:
- English
tags:
- text-classification
- Sentiment
- RoBERTa
- Financial Statements
- Accounting
- Finance
- Business
- ESG
- CSR Reports
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")
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.