Classification of job offers of the World Wide Web

To automate the retrieval of online job opportunities of a specific domain, text classification is an only viable method. In this paper, we investigated eight classifiers and defined a new preprocessing method for job offers classification, and study their accuracies and generalization behaviors. Different job offers websites are used to collect data. We organized data into six different dataset groups, by applying different existing and a new preprocessing method. Classifiers are regularized to avoid high variance, and their accuracies and generalization errors are evaluated. All the classifiers showed more than 0.9 accuracy, but their generalization errors varied. Ridge Regression and Stochastic Gradient Decent generalized well on new data — for all the dataset groups. On the contrary Random Forest and Perceptron tenacious toward high variance. Remaining classifiers exhibited both behaviors for different dataset groups. We found two classifiers that generalized well to new data. Our finding highlight the potential use of classifiers to automate the job offers retrieval from the World Wide Web.