• Journal of the Korea Convergence Society
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• v.11 no.6
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• pp.23-28
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• 2020

A comment system is essential for communication on the Internet. However, there are also malicious comments such as inappropriate expression of others by exploiting anonymity online. In order to protect users from malicious comments, classification of malicious / normal comments is necessary, and this can be implemented as text classification. Text classification is one of the important topics in natural language processing, and studies using pre-trained models such as BERT and graph structures such as GCN and GAT have been actively conducted. In this study, we implemented a comment classification system using BERT, GCN, and GAT for actual published comments and compared the performance. In this study, the system using the graph-based model showed higher performance than the BERT.

• Journal of Technology Innovation
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• v.24 no.3
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• pp.139-159
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• 2016

The purpose of this study is to find out the factors which promoting career development of scientist and engineer laborforce. We made up three logit analysis models to figure out the factors affecting the career of scientist and engineer laborforce. Dependent variables were composed of job attributes in 2011, current job attribute, changing of working conditions, efforts of job seeking, university education, and personal characters. Three analysis model were composed of demand side model which including job attributes factors, supply side model which focus on employment or university education characters and total model including the demand and the supply factors. The results showed a stable career to the development of scientist and engineer laborforce's job attributes on the demand side than the supply side, such as a college education even more important. After all, the initial stable jobs and good matching policy were the most important policies to be seated in the science and engineering professions.