  Both policy and research benefit from a better understanding of individuals'
jobs. However, as large-scale administrative records are increasingly employed
to represent labor market activity, new automatic methods to classify jobs will
become necessary. We developed an automatic job offers classifier using a
dataset collected from the largest job bank of Mexico known as Bumeran
https://www.bumeran.com.mx/ Last visited: 19-01-2022.. We applied machine
learning algorithms such as Support Vector Machines, Naive-Bayes, Logistic
Regression, Random Forest, and deep learning Long-Short Term Memory (LSTM).
Using these algorithms, we trained multi-class models to classify job offers in
one of the 23 classes (not uniformly distributed): Sales, Administration, Call
Center, Technology, Trades, Human Resources, Logistics, Marketing, Health,
Gastronomy, Financing, Secretary, Production, Engineering, Education, Design,
Legal, Construction, Insurance, Communication, Management, Foreign Trade, and
Mining. We used the SMOTE, Geometric-SMOTE, and ADASYN synthetic oversampling
algorithms to handle imbalanced classes. The proposed convolutional neural
network architecture achieved the best results when applied the Geometric-SMOTE
algorithm.
