Abstract

This study uses NLP to examine the relationships between skills needed, wage trends, and job attributes in over 33,000 LinkedIn job posts. NLP enabled us to analyze a large dataset and gain deeper insights into the employment market. We obtained and cleansed the data ethically to guarantee that it was accurate for NLP analysis. This investigation included techniques such as keyword extraction and sentiment analysis to uncover important talents, job market attitudes, and industry trends. We also examined pay trends to better comprehend the market's economic aspects. By merging NLP and machine learning, this study offers a novel methodological framework for assessing employment markets utilizing comparable information. The findings provide significant insights for a variety of stakeholders. They may help job seekers build their talents and plan their careers, while HR professionals can improve their recruiting efforts. Policymakers and educators may use this knowledge to create training programs that meet market demands. This work makes a substantial contribution to job market research by analyzing LinkedIn job advertisements using natural language processing. It illustrates the complicated interplay between talents, positions, and economic conditions, offering useful information to labor market participants.

Library of Congress Subject Headings

LinkedIn (Electronic resource); Job hunting--Data processing; Natural language processing (Computer science)

Publication Date

Fall 2024

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Ehsan Warriach

Campus

RIT Dubai

Plan Codes

PROFST-MS

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