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

• Korean Journal of Childcare and Education
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• v.9 no.6
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• pp.243-265
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• 2013

The purpose of this study is to figure out how a child, as an active responder, responds to the characters of fantastic picture books by analyzing the child's questions and comments through the reading aloud approach. The subjects of this study are fifty-four children under five years old. Nine fantastic picture books are used as the study materials. The contents of the analysis are the frequency of children's questions and comments, the types of responses and the reasons of preferences to the characters. The results of the analysis are as follows: Firstly, the frequency of comments is three times higher than the frequency of questions. Secondly, the frequency of "evaluative questions" is the highest and "imaginative questions" is the next highest. The frequency of "transparent questions" and "personal questions" are comparatively low. Thirdly, most of the children answered that the reason of preference of the characters was "the character's appearance" and then "their subjective feeling to the character", "the character's role" and "the character's characteristics" in that order. Only one child answered that it was "the character's gender." This study will contribute to the planning and implementation of the strategies of reading picture books and to the strategic study to improve children's responses as well.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.7
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• pp.475-486
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• 2020

The purpose of present study is to identify the regional tourism resources using Webometric network analysis. The study focuses on Suseong area in Daegu metropolitan city. Various kinds of web-based data, for example, hit counts, online news, and public comments, were used to discover hot places and people's responses. The research question is, 'First, what is the optimum level of the search engine for suseong? Second, what is the online appearance of tourist resources in suseong? Which region is the center of tourism with high levels of emergence? Third, what are the main contents of news articles and comments related to the Suseong pond?'. The results show that the search engine optimization level in Suseong is lower than that in other areas in Daegu. In other words, tourism information and contents regarding Suseong are not highly visible on cyber space. Importantly, Suseong pond had the highest online presence. A close analysis of both online news and users' comments on Suseong pond, however, revealed the biggest concern as calling for improving public accessibility to tourism infrastructure. The findings are expected to contribute to policy development and service operation related to tourism resources in Suseong.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.89-103
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• 2017

This paper analyzed the influence of government-owned social media's environmental characteristics on the level of users' active participation and types of message diffusion during government's crisis. The social media environment was categorized based on human interactivity and media interactivity. Users' active participation was measured based on amount of like/dislike and number of comments. User's comments were categorized through network analysis. This study found that the level of user's active participation was high on the information-provider participation type and the relationship-oriented participation type. The level of user's active participation was low on the information-provider type and the limited platform. The analysis of message diffusion type showed that a restrictive rational opinion type was found for the limited platform and diffusive or restrictive emotional opinion types were found for other types of social media environment. This study found that during MERS crisis, the government did not provide messages suitable for the social media environment, and the media environment influenced users' participation and comments. The government should provide user friendly social media environment by increasing interaction with users and should make efforts to communicate with users in crisis situation.

• Korean journal of communication and information
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• v.75
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• pp.43-79
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• 2016

An increase in the number of immigrants to Korea since the late 1980s' has signified the proliferation of globalization and global capitalism. In Korea, most married immigrants are women, as the culture emphasizes patrilineage and the stability of the institution of marriage, particularly in rural areas. Immigrant women have experienced dual ordeals. The Aogra Internet community in Korea has been one of the most representative sites that has shown the power of communities in cyberspace since 2002, leading the discussion of social issues and deliberative democracy both online and offline. This paper analyzed Koreans' writings (such as long comments) on immigrant women in the Agora community. The analysis revealed the following results: first, immigrant women were referred to using terms related to prostitution, with excessive expression of disgust, which is called a "narrative of identity." Second, anti-multiculturalists called Korean men victims of married immigrant women and expressed hatred toward immigrant women, which is called a "narrative of sacrifice." Third, anti-multiculturalists justified their emotions as just resentment based on ideas of justice, equality, and patriotism, concealing the emotion of disgust, which is called the "narrative of justice, equality." Fourth, antimulticulturalists played roles to spread the emotion of disgust, by repeatedly referring to international marriage fraud and immigrant workers' crimes, which is called "narrative of crime." Fifth, some positive writings on immigrant women were based on empathy(a concept defined in this context by Martha Nussbaum), but they can be analyzed as narratives encouraging cultural integration through the perspective of orientalism. Therefore, comments on immigrant women in the Agora represent a "catch-22" dilemma. To deal with conflicts arising from disgust and violations of human rights, civic education focusing on humanism is needed in this multicultural era.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.830-860
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• 2022

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

• The Journal of the Convergence on Culture Technology
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• v.8 no.4
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• pp.435-444
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• 2022

There are three focuses in the paper. Firstly, the study identified what channels were most viewed by YouTube users to watch the 'Daejang-dong scandal,' which was the most powerful agenda to influence the candidate preference among voters during the 20th presidential election. Secondly, the study analyzed whether the political tone of the first videos was in line with that of the subsequent videos. Finally, we compared the sentiment of comments on the first and subsequent videos. The results showed that TBS 'News Factory' and 'TV Chosun News' represented liberal and conservative factions, respectively. Secondly, the political tone of channels that were viewed subsequently was neutral, but the conservative channel users left more negative comments and that was significant statistically. In addition, about 80% of the conservative and liberal channel users shared the same political tendency with the channel they watched first, and more than 90% of the comments left at the subsequent videos in line with that of at the first news. Based on these results, the study concluded that the voters tended to seek political news that was similar with their political ideology, and it was considered a sort of echo chamber phenomenon on the YouTube. The study suggests that the performance of high-quality journalism by traditional news outlet might contribute to decrease the negative influence of political contents on YouTube users.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.119-133
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• 2022

One of the most important current features of food related industry is the growth of food delivery service. Another notable food related culture is, with the advent of Youtube, the popularity of Mukbang, which refers to content that records eating. Based on these background, this study intended to focus on two things. First, we tried to see the impact of Youtube Mukbang and the sentiments of Mukbang comments on the number of related food deliveries. Next, we tried to set up the predictive modeling of chicken delivery order with machine learning method. We used Youtube Mukbang comments data as well as weather related data as main independent variables. The dependent variable used in this study is the number of delivery order of fried chicken. The period of data used in this study is from June 3, 2015 to September 30, 2019, and a total of 1,580 data were used. For the predictive modeling, we used machine learning methods such as linear regression, ridge, lasso, random forest, and gradient boost. We found that the sentiment of Youtube Mukbang and comments have impacts on the number of delivery orders. The prediction model with Mukban data we set up in this study had better performances than the existing models without Mukbang data. We also tried to suggest managerial implications to the food delivery service industry.

• Knowledge Management Research
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• v.23 no.2
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• pp.211-228
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• 2022

As social media marketing becomes prevalent, it is necessary to understand the administrative role of managers in promoting user engagement. However, little is known about how community managers enhance user engagement in social media. In this research, we study how managers can boost online user participation, including clicking likes and writing comments. Using the SUR (Seemingly Unrelated Regression) model, we find out that the active participation of managers increases user engagement of both passive (likes) and active (comments) ones. In addition, we find that the number of emotional words included in posts has a positive effect on the passive engagement whereas it negatively affects the active engagement. Lastly, the congruency between posts and comments positively affects users' passive engagement. This study contributes to prior literature related to online community management and text analyses. Furthermore, our findings offer managerial insights for practitioners and social media managers to further facilitate user engagement.

• Korean Journal of Communication Studies
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• v.25 no.5
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• pp.81-101
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• 2017

The purpose of this paper is two folds: Korean media contents, which has led the Korean Wave in China in 1990s will be reviewed, and the causes of the negative feelings of Korean Wave that have occurred among Chinese netizens will be factored out in order to suggest the solutions to this conflict situation. The reviews and comments on the China's major portal site, Tienya were analyzed by the key words that causes the conflict between China and Korea. Of the total 340,000 responses, politics, history and entertainment are categorized by the keywords, and the largest portion of the netizen's comments are found to be political issues with 34%, particularly the issues related to the THAAD. This means that the negative feeling toward the Korean Wave is more closely related to politics rather than the media contents. Therefore, in order to overcome the negative feelings in China and maintain the stable relationship with the two countries in the midst of the changing US-China situation, it is necessary to lead the media business with high quality contents along with the mutual understanding and cooperation of the media content producers. It is also necessary to try to approach Chinese market in a cooperative and stable way through co-production or joint venture with Chinese media. In consequence, the excellence of Korean cultural contents and the cultural ties with Chinese media market will be identified with in-depth understanding of Chinese nationalism, Sinocentrism and Chinese culture.