• Journal of Digital Convergence
• /
• v.19 no.12
• /
• pp.29-42
• /
• 2021

As the low fertility intensifies in Korea, this study investigated fundamental differences between the government's low fertility policy and public perception of it. To this end, we selected four times 'Aging Society and Population Policy' documents and news comments for two weeks immediately after announcement of the third and fourth Policy as analysis targets. Then we conducted word frequency analysis, co-occurrence analysis and CONCOR analysis. As a result of analyses, first, direct childcare support during the first and second periods, and a social structural approach during third and fourth periods were noticeable. Second, it was revealed that both policies and comments aim for the work-family compatibility in 'parenting'. Lastly it was showed public interest in environment of raising children and the critical mind to effectiveness of the policy. This study is meaningful in that it confirmed the public perception using big data analysis, and it will help improve the direction for the future low fertility policy.

• Journal of Digital Convergence
• /
• v.20 no.5
• /
• pp.627-635
• /
• 2022

As the non-face-to-face situation continues for a long time due to COVID-19, the underlying technologies of the 4th industrial revolution such as IOT, AR, VR, and big data are affecting the metaverse platform overall. Such changes in the external environment such as society and culture can affect the development of academics, and it is very important to systematically organize existing achievements in preparation for changes. The Korea Educational Research Information Service (RISS) collected data including the 'metaverse platform' in the keyword and used the text mining technique, one of the big data analysis. The collected data were analyzed for word cloud frequency, connection strength between keywords, and semantic network analysis to examine the trends of metaverse platform research. As a result of the study, keywords appeared in the order of 'use', 'digital', 'technology', and 'education' in word cloud analysis. As a result of analyzing the connection strength (N-gram) between keywords, 'Edue→Tech' showed the highest connection strength and a total of three clusters of word chain clusters were derived. Detailed research areas were classified into five areas, including 'digital technology'. Considering the analysis results comprehensively, It seems necessary to discover and discuss more active research topics from the long-term perspective of developing a metaverse platform.

• The Journal of the Korea Contents Association
• /
• v.21 no.3
• /
• pp.33-46
• /
• 2021

Using news big data analysis, this study was aimed to examine the suicide phenomena in Korean society, and to evaluate whether suicide prevention policies reflect social phenomena appropriately. For this purpose, 9,142 news titles with suicide as the keyword were collected from eight central newspapers between 2000 to 2018. Nouns were extracted, and data was refined for network analysis. The total period was divided into 4 periods based on the 1st and 2nd suicide prevention policies, and the characteristics of suicide phenomena in each period were identified through the top 50 frequent main words and the clusters. As a result, period 1 (2000~2003) with 6 clusters (military, internet environment, economic problems, pessimism, school, corruption), period 2 (2004~2008) with 8 clusters (high social class, school, economic problems, suicide attempts, family issues, social problems, military, responsibilities), period 3 (2009~2013) with 6 clusters (school, family issues, suicide attempts, occupation, military, investigation), and period 4 (2014~2018) with 8 clusters (military, suicide insurance money, family issues, suicide attempts, occupation, job stress, celebrity, corruption) were identified. Study results suggested the characteristics of suicide phenomena in our society. Further, the appropriateness of the implementation of suicide prevention policies was discussed.

• The Journal of the Korea Contents Association
• /
• v.21 no.8
• /
• pp.376-387
• /
• 2021

This study is to analyze the research trends related to the 'glass ceiling' phenomenon using big data analysis methods and to suggest social implications. To analyze the research trends of 'glass ceiling', the historical event that broke the 'glass ceiling' was set as an important issue, and keywords were collected by dividing park's term into three. Before, throughout and after, her term. As a result of frequency analysis, research was conducted based on 'public servants' which was selected as the main keyword in the first period, while 'women's work family compatibility' was chosen as the main keyword group in the second period. In the third period, keywords for women's occupational groups were being diversified. As a result of applying CONCOR techniques to make the studied main topics grouped, we were able to confirm that the main issues were the differentiating factors, the customary gender discrimination culture, the jobs aimed for studying, the work-family balance, the glass ceiling and the organizational performance adjustment factors, the public sector, organizational performance, and the private sector. Besides work-family compatibility support system, it was suggested as a social implication that research on improving the system to resolve the glass ceiling factor and to expand the target jobs to give solutions to real-life issues were needed, and also suggested that research on the 'glass ceiling' which the general public perceives through social medias or articles in the news, was needed in the future.

• Management & Information Systems Review
• /
• v.39 no.2
• /
• pp.181-193
• /
• 2020

This study is a study that analyzes consumer perception by utilizing text mining, which is a recent issue. we analyzed the consumer's perception of Samsung Galaxy by analyzing consumer reviews of Samsung Galaxy YouTube ads. for analysis, 1,819 consumer reviews of YouTube ads were extracted. through this data pre-processing, keywords for advertisements were classified and extracted into nouns, adjectives, and adverbs. after that, frequency analysis and emotional analysis were performed. Finally, clustering was performed through CONCOR. the summary of this study is as follows. the first most frequently mentioned words were Galaxy Note (n = 217), Good (n = 135), Pen (n = 40), and Function (n = 29). it can be judged through the advertisement that consumers "Galaxy Note", "Good", "Pen", and "Features" have good functional aspects for Samsung mobile phone products and positively recognize the Note Pen. in addition, the recognition of "Samsung Pay", "Innovation", "Design", and "iPhone" shows that Samsung's mobile phone is highly regarded for its innovative design and functional aspects of Samsung Pay. second, it is the result of sentiment analysis on YouTube advertising. As a result of emotional analysis, the ratio of emotional intensity was positive (75.95%) and higher than negative (24.05%). this means that consumers are positively aware of Samsung Galaxy mobile phones. As a result of the emotional keyword analysis, positive keywords were "good", "good", "innovative", "highest", "fast", "pretty", etc., negative keywords were "frightening", "I want to cry", "discomfort", "sorry", "no", etc. were extracted. the implication of this study is that most of the studies by quantitative analysis methods were considered when looking at the consumer perception study of existing advertisements. In this study, we deviated from quantitative research methods for advertising and attempted to analyze consumer perception through qualitative research. this is expected to have a great influence on future research, and I am sure that it will be a starting point for consumer awareness research through qualitative research.