Abstract

In this paper, we investigated multiple machine learning classifiers which are, Multinomial Naive Bayes, Support Vector Machine, Decision Tree, K Nearest Neighbors, and Random Forest in a text classification problem. The data we used contains real and fake job posts. We cleaned and pre-processed our data, then we applied TF-IDF for feature extraction. After we implemented the classifiers, we trained and evaluated them. Evaluation metrics used are precision, recall, f-measure, and accuracy. For each classifier, results were summarized and compared with others.