• Journal of dental hygiene science
• /
• v.7 no.4
• /
• pp.251-256
• /
• 2007

The purpose of this study was to examine what induced dental hygienists to take up another employment and whether their job satisfaction had anything to do with it in an attempt to help curtail their turnover rate. The subjects in this study were approximately 200 dental hygienists who worked in dental institutions. A survey was conducted from July 24 through September 24, 2006, by using structured, self-administered questionnaires. For data analysis, SPSS 11.5 program was employed to see if their turnover experience was linked to their general characteristics, why they took up another employment, how long they wanted to do that and how their job satisfaction was related to that. The findings of the study were as follows: 1. In regard to turnover experience by age, marital status and career, those who had ever changed their employment accounted for 36.2 percent of the age group from 24 to 26, 83.0 percent of the unmarried ones and 50.0 percent of those whose career was less than one to three years (p < 0.001). By monthly mean income, 50.0 percent of the dental hygienists whose monthly mean income ranged from 1.0 to 1.29 million won had that experience(p < 0.05). The gap between these groups and the others was statistically significant. 2. As for the reason of turnover, working environments were cited most often(28.1%), followed by possibilities(18.0%), relationship with supervisors and colleagues(12.4%), and compensation(4.5%). 3. Concerning a preferred new workplace, 66.2 percent of the dental hygienists who worked in dentist's offices hoped to be newly hired by public dental clinics(p < 0.001). By education, 64.3 percent of the college-educated dental hygienists wanted to work at public dental clinics as well(p < 0.01). 4. The change of employment was under the greatest influence of the possibilities of workplace, followed by workload, pay and relationship with colleagues. All the factors had a negative impact on their turnover. Those who were less satisfied sought new employment more often, and job satisfaction made a statistically significant difference to that. The job satisfaction factors made a prediction of their turnover intention ($R^2=.254$).

• Journal of Intelligence and Information Systems
• /
• v.24 no.3
• /
• pp.199-219
• /
• 2018

With the everyday use of the Internet and the spread of various smart devices, users have been able to communicate in real time and the existing communication style has changed. Due to the change of the information subject by the Internet, data became more massive and caused the very large information called big data. These Big Data are seen as a new opportunity to understand social issues. In particular, text mining explores patterns using unstructured text data to find meaningful information. Since text data exists in various places such as newspaper, book, and web, the amount of data is very diverse and large, so it is suitable for understanding social reality. In recent years, there has been an increasing number of attempts to analyze texts from web such as SNS and blogs where the public can communicate freely. It is recognized as a useful method to grasp public opinion immediately so it can be used for political, social and cultural issue research. Text mining has received much attention in order to investigate the public's reputation for candidates, and to predict the voting rate instead of the polling. This is because many people question the credibility of the survey. Also, People tend to refuse or reveal their real intention when they are asked to respond to the poll. This study collected comments from the largest Internet portal site in Korea and conducted research on the 19th Korean presidential election in 2017. We collected 226,447 comments from April 29, 2017 to May 7, 2017, which includes the prohibition period of public opinion polls just prior to the presidential election day. We analyzed frequencies, associative emotional words, topic emotions, and candidate voting rates. By frequency analysis, we identified the words that are the most important issues per day. Particularly, according to the result of the presidential debate, it was seen that the candidate who became an issue was located at the top of the frequency analysis. By the analysis of associative emotional words, we were able to identify issues most relevant to each candidate. The topic emotion analysis was used to identify each candidate's topic and to express the emotions of the public on the topics. Finally, we estimated the voting rate by combining the volume of comments and sentiment score. By doing above, we explored the issues for each candidate and predicted the voting rate. The analysis showed that news comments is an effective tool for tracking the issue of presidential candidates and for predicting the voting rate. Particularly, this study showed issues per day and quantitative index for sentiment. Also it predicted voting rate for each candidate and precisely matched the ranking of the top five candidates. Each candidate will be able to objectively grasp public opinion and reflect it to the election strategy. Candidates can use positive issues more actively on election strategies, and try to correct negative issues. Particularly, candidates should be aware that they can get severe damage to their reputation if they face a moral problem. Voters can objectively look at issues and public opinion about each candidate and make more informed decisions when voting. If they refer to the results of this study before voting, they will be able to see the opinions of the public from the Big Data, and vote for a candidate with a more objective perspective. If the candidates have a campaign with reference to Big Data Analysis, the public will be more active on the web, recognizing that their wants are being reflected. The way of expressing their political views can be done in various web places. This can contribute to the act of political participation by the people.