Positive Press (Backend and API)
My BSc Final Year Project. I created a Python API that uses Natural Language Processing (NLP) to analyse news headlines and provide a curated feed of news ranked by positivity. This is served by the backend app using FastAPI, and stored in a NoSQL database. The documentation of the endpoints found at the link below is provided using Swagger. There is a consuming frontend app, built in NextJS.
Rational for the project:
There is a growing body of evidence that negative news can have a detrimental effect on mental health. This site was created to provide a curated feed of news that is ranked by positivity. The project uses Natural Language Processing (NLP) to analyse news headlines and provide a curated feed of news ranked by positivity. The app uses a FastAPI backend to provide the API for the frontend to consume. The project also uses a NoSQL database to store the news headlines and their associated sentiment scores. Sentry is used for error tracking and Swagger for API documentation. The frontend is built using NextJS and TailwindCSS and the project is deployed on Vercel.Technologies used:
Python
Pandas
NoSQL
FastAPI
Pydantic
Swagger - API documentation
Pytest