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Online Job Posting Authenticity Prediction using Machine and Deep Learning Techniques

Malaichamy, Gayathri (2023) Online Job Posting Authenticity Prediction using Machine and Deep Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Today’s world is all about virtually managing every aspect of human existence, including banking online, education, security, and employment. A fraudster can easily swindle people and gain fast profits due to this rise in technology use. Nowadays, 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 these fake job postings, and they are then scammed out of their funds. Therefore, every individual will be quite concerned about the problem of predicting bogus job postings. To accomplish this, a methodology has been suggested that utilizes Machine Learning, Deep Learning and Natural Language Processing (NLP) techniques. For feature extraction, the N-Gram model (Unigram, Bigram and Trigram) and TFIDF (Term Frequency-Inverse Document Frequency) techniques were used. In this research, the impact of N-gram and TF-IDF feature techniques on fake job data classification has been analysed and it is found that the TF-IDF features performed better than N-Gram feature models. The analysis was done by using all five classifiers such as Naive Bayes, Random Forest, LightGBM, XGBoost and Multi Layer Perceptron (MLP) classifier. It is observed that the MLP classifier with ADAM optimizer outperformed all other classifiers with an accuracy of 95.68% and a prediction time of 13s. The second highest performer is the Naive Bayes classifier which attained an accuracy of 95.38% and a prediction time of 0.2s.

Item Type: Thesis (Masters)
Simiscuka, Anderson Augusto
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management > Human Resource Management > Recruitment > E-Recruitment
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 19 May 2023 16:07
Last Modified: 19 May 2023 16:07

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