Impact Factor: 7.883
Submit Paper
Volume 12, Issue 4 (April 2026)

Detection Of Fake And Irrelevant Job Postings Using Passive Aggressive Classifier

Impact Factor
7.883
Call For Paper
Volume 12 Issue 07

July 2026

Download Paper Format
Paper Format
Copyright Form
Copy Right Format
Under License Of
IJSART License
Creative Commons Attribution-NonCommercial
4.0 International License
Share on:

Author(s)

Prof Sasikala S AbinayaShri S Dhivya Dharshini R Sathya D Vaishnavi R

Abstract

With The Rapid Growth Of Online Job Portals, The Number Of Fake And Irrelevant Job Postings Has Significantly Increased. These Fraudulent Listings Mislead Job Seekers, Waste Time, And Sometimes Lead To Financial Loss. This Paper Presents A Machine Learning–based Approach To Detect Fake And Irrelevant Job Postings Using A Passive Aggressive Classifier. A Dataset Of 10,000 Job Postings Collected From Kaggle Was Used For Training And Evaluation. Natural Language Processing (NLP) Techniques Such As TF-IDF Are Applied To Convert Textual Data Into Numerical Features. The Proposed System Is Integrated Into A Web Platform Named TrueHire, Which Provides Verified Job Listings. The Model Achieves An Accuracy Of 71.93%, Demonstrating Its Effectiveness In Identifying Fraudulent And Irrelevant Postings.

Keywords

Fake Job Detection Passive Aggressive Classifier TF-IDF NLP Machine Learning Job Portal Fraud Detection.

Paper ID

IJSARTV12I4105019

Publication Date

April 15, 2026

Research Area

Computer Science And Engineering

Download Full Article

Submit Your Paper to IJSART

Join the global research community with IJSART. Submit your paper, share your work, and gain worldwide recognition!

Submit Your Paper