딥러닝기반 텍스트 분석을 통한 직업분류시스템 구축에 관한 연구

Abstract

The purpose of this study is to create a classification model that can identify the type of job by using online job posting text data and evaluate the performance of the model. By applying the latest deep learning machine learning method to Work-Net online job postings(OJPs) text data, it is to automatically determine the occupat ional code of the OJPs. Considering the research trends shifting from a rule-based model to an artificial neural network model. and the merit of handling large-scale online job posting materials and the contextual meaning of text well. the latest models of artificial neural networks. Bi-LSTM and KoBERT models, were applied. As a result of applying the model to 8 million text data of employment insurance Work-Net job posting data from 1999 to 2001 . matching accuracy of 0.62 to 0.82 was achieved. The result is not very high performance. but it is generally judged to be a model that can determine the occupation. In particular. high accuracy was achieved in professions where job descriptions were specific and precise. Although it is not yet perfect for practical use, it is expected that the performance of the automatic occupational classification system will improve in the future when recruitment practices into the job-type labor market change and more precise data pre-processing and model applications are made.