finbert sentiment analysis flask api

this project provides a flask api for sentiment analysis on financial texts using the finbert model, a bert-based model pre-trained on financial texts. it's designed to offer easy-to-use http endpoints for processing financial texts to detect sentiments, making it suitable for integrating sentiment analysis into financial applications.

features

• sentiment analysis using the finbert model
• easy-to-use http post endpoint for analyzing lists of texts
• scalable and production-ready with docker deployment

prerequisites

before you begin, ensure you have met the following requirements:

• docker and docker compose installed on your machine
• basic knowledge of docker and flask applications

installation

to set up the project for development or deployment, follow these steps:

clone the repository:

git clone https://github.com/your-username/finbert-flask-analyzer.git
cd finbert-flask-analyzer

build and run with docker

use docker compose to build and run the application:

docker-compose up --build

this command builds the docker image and starts the flask application inside a docker container, making it accessible on http://localhost:5000.

usage

to use the api for sentiment analysis, send a post request to the /analyze endpoint with a json body containing a list of texts you want to analyze. example using curl:

curl -X POST http://localhost:5000/analyze -H "Content-Type: application/json" -d '{"texts": ["This company financial performance is outstanding.", "The market is facing a severe downturn."]}'

the api will return a json response with the sentiment analysis results.

development

for local development, you can run the flask application outside docker by first installing the required python packages:

pip install -r requirements.txt

then, set the flask application environment variables and run the server:

export FLASK_APP=your_flask_app.py
export FLASK_ENV=development
flask run

contributing

contributions to improve the project are welcome. please follow these steps to contribute:

• fork the repository
• create a new branch (git checkout -b feature/amazingfeature)
• commit your changes (git commit -m 'add some amazingfeature')
• push to the branch (git push origin feature/amazingfeature)
• open a pull request

license

distributed under the mit license. see license for more information.

contact

[email protected]

project link: https://github.com/det-peralta/sentiment-analysis-api