This study analyzed the characteristics of ‘woman scientists and technicianss’ in the newspaper articles using the semantic network analysis. The data collection utilized the search service provided by the Korea Press Foundation(KPF), which provides Bigkinds, a newspaper search service. Through Bigkinds, 1,714 articles containing ‘woman scientists and technicianss’ were included in the title of articles were extracted. The collected data were subjected to a refinement process and 50 keywords were selected based on the word frequency. The selected key words were converted into matrix data and analyzed by social network analysis. The results of this study are as follows. First, ‘woman’ was the most socially recognized keyword in ‘woman scientists and technicianss’ network. And next keywords were ‘scientists and technicianss’, ‘science and technology’, ‘WISET’, and ‘science’. Second, With regards to degree centrality, closeness centrality, and betweenness centrality, ‘scientists and technicianss’, ‘woman’, ‘science and technology’, ‘science’, ‘support’, ‘WISET’ were among the highest 6 keywords. Third, as a result of visualizing the network analysis, selected 50 keywords were networked with one another.