• Journal of Contemporary Eastern Asia
• /
• v.23 no.1
• /
• pp.18-34
• /
• 2024

In what was labeled the "nut rage" incident, the vice president of Korean Air, Hyun-Ah Cho (Heather Cho), demonstrated behavior that exemplifies corporate transgression and deviation from societal moral standards toward a flight attendant aboard a flight. Such behavior instigated the public to express negative sentiment on various social media platforms. This study investigates word-of-mouth network on YouTube in response to the crisis, patterns of co-commenting activities across selected YouTube videos, as well as public responses to the incident by employing social and semantic network analysis. A total of 512 YouTube videos featuring the crisis from December 8, 2014 through November 11, 2018, and 52,772 public comments to the videos were collected. The central videos in the network successfully attracted the public's attention and engagements. The results suggest that the video network was decentralized, with multiple videos acting as hubs in the network. The public commented on various videos instead of focusing on a few. The contents of influential videos uploaded by popular news organizations revealed not only Cho's behaviors related to the nut rage crisis but also unrelated illegal behaviors and the moral violations committed by the family members of Korean Air. The public attached derogatory remarks to Cho and her family, and the comments also addressed ethical concerns, management issues of the company, and boycott intentions. The results imply that adverse public reaction was related to the long-standing problem caused by family ownership and governance in large Korean corporations. This Korean Air scandal illustrates backlash toward a leadership breakdown by the family business conglomerate prevalent in the Korean society. This study provides insights for effective handling of similar crises.

• Journal of Digital Convergence
• /
• v.12 no.12
• /
• pp.249-255
• /
• 2014

Recently, creation and sharing of information make progress actively through the SNS(Social Network Service) such as twitter, facebook and so on. It is necessary to extract the knowledge from aggregated information and data mining is one of the knowledge based approach. Especially, emotion analysis is a recent subdiscipline of text classification, which is concerned with massive collective intelligence from an opinion, policy, propensity and sentiment. In this paper, We propose the emotion prediction method, which extracts the significant key words and related key words from SNS paragraph, then predicts the emotion using these extracted emotion features.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.10
• /
• pp.5120-5142
• /
• 2018

Social media enables customers to share their views, opinions and experiences as product reviews. These product reviews facilitate customers in buying quality products. Due to the significance of online reviews, fake reviews, commonly known as spam reviews are generated to mislead the potential customers in decision-making. To cater this issue, review spam detection has become an active research area. Existing studies carried out for review spam detection have exploited feature engineering approach; however limited number of features are considered. This paper proposes a Feature-Centric Model for Review Spam Detection (FMRSD) to detect spam reviews. The proposed model examines a wide range of feature sets including ratings, sentiments, content, and users. The experimentation reveals that the proposed technique outperforms the baseline and provides better results.

• Journal of Korea Game Society
• /
• v.21 no.6
• /
• pp.75-86
• /
• 2021

Brand image refers to how customers, stakeholders and the market see and recognize the brand. A positive brand image leads to continuous purchases, but a negative brand image is directly linked to consumers' buying behavior, such as stopping purchases, so from the corporate perspective, it needs to be quickly and accurately identified. Currently, methods of investigating brand images include surveys and SNS surveys, which have limited number of samples and are time-consuming and costly. Therefore, in this study, we are going to conduct an emotional analysis of text data on social media by utilizing the machine learning based KoBERT model, and then suggest how to use it for game corporate brand image analysis and verify its performance. The result has proved some degree of usability showing the same ranking within five brands when compared with the BRI Korea's brand reputation ranking.

• Journal of the Korea Convergence Society
• /
• v.11 no.5
• /
• pp.199-207
• /
• 2020

Recently, big data analysis has drawn attention in various fields as it can generate value from large amounts of data and is also used to run political campaigns or predict results. However, existing research had limitations in compiling information about candidates at a high-level by analyzing only specific SNS data. Therefore, this study analyses news trends, topics extraction, sentiment analysis, keyword analysis, comment analysis for the 2017 presidential election of South Korea. The results show that various topics had been generated, and online opinions are extracted for trending keywords of respective candidates. This study also shows that portal news and comments can serve as useful tools for predicting the public's opinion on social issues. This study will This paper advances a building strategic course of action by providing a method of analyzing public opinion across various fields.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.15 no.4
• /
• pp.739-746
• /
• 2020

The fourth industrial revolution introduced at world economic forum in 2016 has had huge effects on tourism industries as well as the change of core technologies in ICT such as big data, IoT, etc, This paper proposes the methods to propel tourism of Yoesu city through big data analysis and questionnaires. Sensitive words and positive-negative trend are extracted by Social Metrics and the keywords for Yeosu tour trends are extracted and analyzed by Naver datalab, and the results are visualized by R language. And frequency, difference, factor, covariance and regression analysis in SPSS are executed for the questionnaires for 493 visitors who traveled in Yeosu city. Sentiment analysis for Yeosu tour and maritime cable car shows that positive effect is much more than negative one. The analyses for questionnaires in SPSS show that Yeosu area is statistically significant to tour satisfaction index and tour revitalization for Yeosu, and favorite sightseeing places and searching electronic devices for age groups are different. The sightseeing places such as a maritime park with soft contents that give joyfulness and healing to tourists are highly attracted in both the big data and questionnaires analysis.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.9
• /
• pp.115-123
• /
• 2021

This study aims to analyze the changes in public perception of abandoned dogs through big data analysis. Data from January 2017 to July 2020 were collected to analyze how the quantitative change in social issues with abandoned dogs as a keyword had an effect on public perception of abandoned dogs, and factors that influence positive/negative perceptions. As a result of the study, it was confirmed that the number of stray dogs and the number of documents related to stray dogs had a positive correlation, and specific time series changes were found through various analysis techniques such as text mining, network analysis, and sentiment analysis. This study will have significance as basic data that can be used for policy establishment or other research on abandoned dogs. we hope it will help to solve problems so as to improve awareness of abandoned dogs and develop a sense of responsibility.

• International Journal of Computer Science & Network Security
• /
• v.22 no.10
• /
• pp.57-66
• /
• 2022

The aim of the article is to investigate socio-political processes in Ukraine on the basis of institutional and behavioral approaches, in particular their regulatory and informational support. Methodology. To determine the nature and content of sociopolitical processes, the following approaches have been used: 1. Institutional approach in order to analyze the development of Ukraine's political institutions. 2. The behavioral approach has been used for the analysis of socio-political processes in Ukraine in the context of political behavior of citizens, their political activity which forms the political culture of the country. Results. The general features of the socio-political situation in Ukraine are as follows: the formed model of government, which can be conditionally described as "presidential"; public demand for new leaders remains at a high level; the society has no common vision of further development; significant tendency of reduction of real incomes of a significant part of the society and strengthening of fiscal pressure on businessmen will get a public response after some time. Increasing levels of voice, accountability, efficiency of governance and the quality of the regulatory environment indicate a slow change in the political system, which will have a positive impact on public sentiment in the future. At the same time, there has been little change in the quality of Ukraine's institutions to ensure political stability, the rule of law and control of corruption. There are no cardinal changes in the development of the institution of property rights, protection of intellectual rights, changes in the sphere of ethics and control of corruption. Thus, Ukraine's political institutions have not been able to bring about any change in the social-political processes. Accordingly, an average level of trust and confidence of citizens in political institutions and negative public sentiment regarding their perception and future change can be traced in Ukraine.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.46 no.2
• /
• pp.102-108
• /
• 2023

Recently in Korea, YouTube stock channels increased rapidly due to the high social interest in the stock market during the COVID-19 period. Accordingly, the role of new media channels such as YouTube is attracting attention in the process of generating and disseminating market information. Nevertheless, prior studies on the market forecasting power of YouTube stock channels remain insignificant. In this study, the market forecasting power of the information from the YouTube stock channel was examined and compared with traditional news media. To measure information from each YouTube stock channel and news media, positive and negative opinions were extracted. As a result of the analysis, opinion in channels operated by media outlets were found to be leading indicators of KOSPI market returns among YouTube stock channels. The prediction accuracy by using logistic regression model show 74%. On the other hand, Sampro TV, a popular YouTube stock channel, and the traditional news media simply reported the market situation of the day or instead showed a tendency to lag behind the market. This study is differentiated from previous studies in that it verified the market predictive power of the information provided by the YouTube stock channel, which has recently shown a growing trend in Korea. In the future, the results of advanced analysis can be confirmed by expanding the research results for individual stocks.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.47 no.1
• /
• pp.41-50
• /
• 2024

Korea is facing a significant problem with historically low fertility rates, which is becoming a major social issue affecting the economy, labor force, and national security. This study analyzes the factors contributing to the regional gap in fertility rates and derives policy implications. The government and local authorities are implementing a range of policies to address the issue of low fertility. To establish an effective strategy, it is essential to identify the primary factors that contribute to regional disparities. This study identifies these factors and explores policy implications through machine learning and explainable artificial intelligence. The study also examines the influence of media and public opinion on childbirth in Korea by incorporating news and online community sentiment, as well as sentiment fear indices, as independent variables. To establish the relationship between regional fertility rates and factors, the study employs four machine learning models: multiple linear regression, XGBoost, Random Forest, and Support Vector Regression. Support Vector Regression, XGBoost, and Random Forest significantly outperform linear regression, highlighting the importance of machine learning models in explaining non-linear relationships with numerous variables. A factor analysis using SHAP is then conducted. The unemployment rate, Regional Gross Domestic Product per Capita, Women's Participation in Economic Activities, Number of Crimes Committed, Average Age of First Marriage, and Private Education Expenses significantly impact regional fertility rates. However, the degree of impact of the factors affecting fertility may vary by region, suggesting the need for policies tailored to the characteristics of each region, not just an overall ranking of factors.