Nail your resume and cover letter by leveraging AI and NLP to extract keywords from online job postings.
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The job search process can be very difficult, especially with Covid-19's impact on the economy. That's why its more important than ever to nail your resume and cover letter. That's why we created ResumeWords. This website uses NLP and AI, coupled with webscraping, to extract keywords from online job postings. Using the right keywords can play a large role in getting your resume shortlisted, so make sure you use them in your resume and cover letter when applying!
This project consists of multiple Dockerized microservices which are individually deployed on Google Cloud Platform with CI/CD.
Services:
- API Gateway
- Linkedin webscraper
- Indeed webscraper
- Monster webscraper
- NLP machine learning model service
The frontend is a static site, built with React and deployed using Firebase.
This is an example of how you may give instructions on setting up your project locally. To get a local copy up and running follow these simple example steps.
- Docker
- NPM (frontend)
To spin up the microservices:
docker-compose upTo test the frontend:
cd frontend-website
npm startPaste a link for any job posting from LinkedIn, Indeed, or Monster. Next, select a number of words and click submit. We'll scrape the job description from the website and analyze the resulting text with our pre-trained NLP model to return a list of keywords and their corresponding TF/IDF scores. Clicking on a keyword will take you to a thesaurus entry for the word.
Proposed future features:
- Support for more job posting platforms.
- Option to paste a job description (instead of a link) for analysis.
- Chrome extension to scan the job posting page directly.
Distributed under the MIT License. See LICENSE for more information.
Shahmeer Shahid - shahmeer800@gmail.com