• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1595-1603
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• 2020

Online defamation incidents such as Internet news comments on portal sites, SNS, and community sites are increasing in recent years. Bias and hate expressions threaten online service users in various forms, such as invasion of privacy and personal attacks, and defamation issues. In the past few years, academia and industry have been approaching in various ways to solve this problem The purpose of this study is to build a dataset and experiment with deep learning classification modeling for detecting various bias expressions as well as hate expressions. The dataset was annotated 7 labels that 10 personnel cross-checked. In this study, each of the 7 classes in a dataset of about 137,111 Korean internet news comments is binary classified and analyzed through deep learning techniques. The Proposed technique used in this study is multi-channel CNN model with attention. As a result of the experiment, the weighted average f1 score was 70.32% of performance.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.6
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• pp.875-885
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• 2018

This study analyzed the major issues of the Candlelight Rally and Taegukgi Rally registered in news comments of the politics section of the portal site from October 24, 2016 to March 19, 2017. We examined the political recognition of the two rallies with the Named Entity Recognition. The main analytical items are the responsibility for impeachment, the subject and method of settlement, and other major issues. As a result of the analysis, the comments of the Candlelight Rally focused on the impeachment support and the legal penalties of the regime ministers, and insisted on resolving the political situation through the next election after impeachment. The comments of the Taegukgi Rally focused on the rejection of the impeachment to maintain the regime and insisted on rejecting the impeachment of the Constitutional Court. The conflicts between the group that supported Candlelight Rallis and the group that supported Taegukgi rallies are predicted to last at least for the time being (Park Geun-hye's trial period) after the presidential election. After the impeachment of the President and replacement of the regime this conflict will develop into the confrontation between the pursuit of liquidation and new politics and the attempt to influence the trial of Park Geun-hye. Therefore, the efforts to integrate society in the aftermath are necessary.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.12
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• pp.1880-1889
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• 2022

The online portal platform provides online news with online comments, but the anonymity of comments causes incivility, and online comments are considered social problems. While there are many foreign language-based incivility detection studies, in-depth research is not being conducted in Korea since there has not been implemented Korean language dataset which is labeled detailed criteria of incivility. In this study, the incivility notation of comments was conducted in a total of 13 items, uncivil words were summarized. Furthermore, Attention algorithm was applied to each comment and summary to extract embedding vectors. 2-d CNN followed at the end to detect incivility in given data. As a result, we showed that the proposed algorithm is useful for anti-citizen detection such as name-calling and offensive tones. This study is expected to contribute to the formation of a healthy online comment culture by detecting uncivil comments which hinder democratic discourse.

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.257-280
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• 2022

In this study, we analyzed comments on news articles of representative companies of the three industries (i.e., semiconductor, secondary battery, and bio industries) that had been listed as national strategic technology projects of South Korea to identify public opinions towards them. In addition, we analyzed the relationship between changes in public opinion and stock price. 'Samsung Electronics' and 'SK Hynix' in the semiconductor industry, 'Samsung SDI' and 'LG Chem' in the secondary battery industry, and 'Samsung Biologics' and 'Celltrion' in the bio-industry were selected as the representative companies and 47,452 comments of news articles about the companies that had been published from January 1, 2020, to December 31, 2020, were collected from Naver News. The comments were grouped into positive, neutral, and negative emotions, and the dynamic topics of comments over time in each group were analyzed to identify the trends of public opinion in each industry. As a result, in the case of the semiconductor industry, investment, COVID-19 related issues, trust in large companies such as Samsung Electronics, and mention of the damage caused by changes in government policy were the topics. In the case of secondary battery industries, references to investment, battery, and corporate issues were the topics. In the case of bio-industries, references to investment, COVID-19 related issues, and corporate issues were the topics. Next, to understand whether the sentiment of the comments is related to the actual stock price, for each company, the changes in the stock price and the sentiment values of the comments were compared and analyzed using visual analytics. As a result, we found a clear relationship between the changes in the sentiment value of public opinion and the stock price through the similar patterns shown in the change graphs. This study analyzed comments on news articles that are highly related to stock price, identified changes in public opinion trends in the COVID-19 era, and provided objective feedback to government agencies' policymaking.

• 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.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.105-117
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• 2021

This study examined the factors that influence the emotional expression in comments on the hot news about the 'Restaurant Collapse in Linfen City, Shanxi Province' published in Sina Weibo.. As a result of the study, first, there were differences in emotional expression according to gender. Women expressed stronger anger, disappointment, sadness, and condemnation than men. Second, the intensity of emotional expression of users in the eastern region was significantly higher than that of users in the central and western region. Third, the greater the number of Weibo, the total number of blogs where users participated in comments and posted emotional expressions, the stronger the emotional expression was. Fourth, unauthenticated users showed stronger emotional expressions of disappointment and sadness than authenticated users. The results of this study present implications for the factors influencing emotional expression on hot news. This study is meaningful in that it can be compared with social networks such as Twitter and Facebook in the West by looking at the factors that influence emotional expression in the process of online public opinion formation in China, and also meaningful in that a big data analysis method was used in online news analysis.

• Journal of Food Hygiene and Safety
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• v.30 no.4
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• pp.329-342
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• 2015

The purpose of this study was to investigate consumers' perceptions of sodium saccharin in social media. Data was collected from Naver blogs and Naver web communities (Korean representative portal web-site), and media reports including comment sections on a Yonhap news website (Korean largest news agency). The results from Naver blogs and Naver web communities showed that it was primarily mentioned 'sodium saccharin-no added' products, properties of sodium saccharin, and methods of reducing sodium saccharin in food. When media reported the expansion of food categories permitted to use sodium saccharin, search volume for sodium saccharin has increased in both PC and mobile search engines. Also, it was mainly commented about distrust of government, criticism of food product price, and distrust of food companies below the news on the news site. The label of sodium saccharin-no added products in market emphasized "no added-sodium saccharin". These results suggest that consumers are interested in sodium saccharin and especially when media reported the expansion of food categories permitted to use it. Consumers were able to search various information on sodium saccharin except safety or acceptable daily intake through social media. Therefore media or competent authority should report item on sodium saccharin with information including safety or acceptable daily intake based on scientific background and reference or experts' interview for consumers to get reliable information.

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

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.3
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• pp.1795-1805
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• 2015

This paper analyzed the recommendation stories in the online journalism on the user's news choice by college students in two ways. One way is recommendation stories, and the other one is their arrangement and the index of use. From the results of the analysis, 7 out of 11 types of recommendation stories had positive effects on selecting news stories, while the 4 other types had little effect. Most of the recommendation stories that had little effect on the user's news selection were 'comments' or 'things' related to tweets' on SNS. The arrangements of new stories and the searched keywords had some effects on the user's news choice but had no effect on the index of use. In addition, the hours of using news stories and the types of recommendation stories were mostly correlated with each other. Consequently, formal factors, such as the arrangement of news stories and the recommendation stories of online journalism, had positive effects on the user's news selection, as well as headlines and keywords of news stories.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.25-31
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• 2018

Online news has replaced the traditional newspaper and has brought about a profound transformation in the way we access and share information. News websites have had the ability for users to post comments for quite some time, and some have also begun to crowdsource reactions to news articles. The field of sentiment analysis seeks to computationally model the emotions and reactions experienced when presented with text. In this work, we analyze more than 100,000 news articles over ten categories with five user-generated emotional annotations to determine whether or not these reactions have a mathematical correlation to the news body text and propose a simple sentiment analysis algorithm that requires minimal preprocessing and no machine learning. We show that it is effective even for a morphologically complex language like Korean.