This content originally appeared on DEV Community and was authored by Elie
Introduction
In today’s fast-moving tech job market, understanding what skills are in demand is crucial. I recently worked on a project that automatically analyzes LinkedIn job postings, extracts the technical skills mentioned in each job description, and groups similar roles together.
The goal is to help recruiters, job seekers, and data enthusiasts make sense of large volumes of unstructured job posting data.
You can check out the project here: GitHub Repository
How It Works
The workflow has four main steps:
Data Preparation
The project starts by collecting job postings from LinkedIn and creating a manageable sample for analysis. It prepares the text by removing unnecessary clutter like links, punctuation, and irrelevant words.Skill Extraction
Each job description is scanned for a list of technical skills such as Python, SQL, AWS, or machine learning. Abbreviations and variations are normalized so that “ML” is recognized as “Machine Learning,” for example.Clustering Similar Roles
Once the skills are extracted, the project groups job postings into clusters of similar roles. This allows you to see patterns in skill requirements across different positions, such as grouping data engineers, backend developers, or cloud specialists together.Optional Salary Insights
If salary data is available, the clusters can be analyzed to understand how skill sets relate to compensation. You can see which clusters tend to have higher average salaries and which skills are most in demand in those clusters.
What You Can Learn
This approach can provide insights such as:
- Job Role Archetypes: Identify common patterns of skills and group similar positions together.
- Skill Demand Trends: See which technical skills are frequently requested in the market.
- Salary Insights: Understand how skill sets correlate with pay.
It’s a practical example of how natural language processing (NLP) and unsupervised machine learning can help turn messy text data into meaningful insights.
Things to Keep in Mind
- The skill list is predefined, so it might miss rare or emerging skills.
- Clustering is an approximation; not all jobs will fit perfectly into a group.
- Salary analysis depends on accurate and available salary information.
Conclusion
This project shows how you can automatically extract, normalize, and cluster skills from LinkedIn job postings to better understand the tech job market. It’s a lightweight yet powerful pipeline for anyone interested in career analytics, recruitment trends, or skill mapping.
This content originally appeared on DEV Community and was authored by Elie
Elie | Sciencx (2025-11-19T07:43:56+00:00) Analyzing LinkedIn Job Postings: Skill Extraction & Clustering. Retrieved from https://www.scien.cx/2025/11/19/analyzing-linkedin-job-postings-skill-extraction-clustering-3/
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