A Preference Study of Naming Children Based in the Big Data

[Purpose] Big data analytics is the art of effectively analyzing structured data organized in databases, as well as unstructured data such as web documents, emails, and social data generated by the internet, social networking services, and mobile environments. Text mining is the art of finding new and useful information from unstructured text data, and it is based on natural language processing techniques to process unstructured data. In other words, preprocessing is the process of turning unstructured data into structured data to extract features. [Methodology] In recent years, most of the data types used in big data analytics are unstructured text data. The data used in this big data analysis study was based on college students from Pusan National University in Busan and Changwon National University in Changwon. First, based on the data extracted using the charting function provided by the R language, 200 data from the 1st to 200th place obtained through frequency analysis were visualized and expressed in the form of a bar graph. [Findings] The analysis shows that the most mentioned keywords are Yoojin (46), Jiwon (45), Soobin (34), Minji (29), and Jungmin (26), which are located at the bottom of the figure. The least mentioned keywords are Dawon(4), Nahyun(4), Kihwan(4), Geunyoung(4), and Kyuwon(4), which confirms. Next, we visualized and presented the first name data, including last name, from the frequency analysis in a tabular format, ranked from 1 to 100. The top name with the surname Kim is Kim Minji (13), the top name with the surname Lee is Lee Yoojin (13), and the top name with the surname Park is Park Hyunsoo (6). [Implications] Finally, the practical implications and limitations of the study are discussed and conclusions are reached based on the results of the analysis.