Word embedding for job market spatial representation: tracking changes and predicting skills demand

What will the job market of the future look like? What jobs will be popular, and which skills will they require? Modeling the temporal progression of the job market, as represented by job ads, may help us answer this question. This paper represents a first step in this direction. In order to build a spatial representation of job market that allows to track changes in skills’ demand, authors are training models to classify job tasks. Different natural language processing and classification approaches were compared, including term frequency - inverse document frequency, principal components analysis, word2vec, GloVe, fastText and BERT models, and feedforward neural networks, support vector machines, and bidirectional long short term memory recurrent neural networks. BERT obtained the best accuracy results with 52% for 94 classes and 65% for 22 classes.