Online Job Posting Authenticity Prediction with Machine and Deep Learning: Performance Comparison Between N-Gram and TF-IDF

Malaichamy, Gayathri, Muntean, Cristina Hava and Simiscuka, Anderson Augusto (2024) Online Job Posting Authenticity Prediction with Machine and Deep Learning: Performance Comparison Between N-Gram and TF-IDF. In: Deep Learning Theory and Applications. Communications in Computer and Information Science (2171). Springer, Cham, pp. 143-162. ISBN 978-3-031-66694-0

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Abstract

Fraudulent job postings are a widespread scam. People give their personal details as well as processing fees to scammers when they submit an application for fake job postings, and they are then scammed out of their funds. This research paper aims to help address this concern by proposing a methodology that utilizes Machine Learning, Deep Learning and Natural Language Processing (NLP) algorithms for detection of fake job ads online. For feature extraction, the N-Gram model (Unigram, Bigram and Trigram) and TF-IDF (Term Frequency-Inverse Document Frequency) techniques were investigated. The results have shown that the TF-IDF feature extraction techniques performed better than the N-Gram technique. The analysis considered five classifier algorithms: Naive Bayes, Random Forest, LightGBM, XGBoost and Multi Layer Perceptron (MLP). It was observed that the MLP classifier with ADAM optimizer outperformed all other classifiers with an accuracy of 95.68% and a prediction time of 13 s. The second highest performer was the Naive Bayes classifier which attained an accuracy of 95.38% and a prediction time of 0.2 s.

Item Type:	Book Section
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HD Industries. Land use. Labor > Issues of Labour and Work > Job Seeking Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Tamara Malone
Date Deposited:	20 Dec 2024 15:25
Last Modified:	20 Dec 2024 15:25
URI:	https://norma.ncirl.ie/id/eprint/7231

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