• Proceedings of the Korea Information Processing Society Conference
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• 2020.05a
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• pp.389-392
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• 2020

The growth in social media, blogs and restaurant listing directories have led to increasing customer reviews about restaurants, their quality of food items and services available on the internet. These user reviews offer a massive amount of valuable information that can be used for various decision-making purposes. Currently, most food recommendation sites provide recommendation scores about restaurants rather than food items of the restaurant and the provided recommendation scores may be biased since they are calculated only from user reviews listed only in their sites. Usually, people wants a reliable recommendation about foods, not restaurant. In this paper, we present a reliable Korean food items rating method; we first extract food items by applying NER technique to restaurant reviews collected from many Korean restaurant recommendation web sites, blogs and web data. Then, we apply lexicon-based sentiment analysis on collected user reviews and predict people's opinions as sentiment polarity scores (+1 for positive; -1 for negative; 0 for neutral). Finally, by taking average of all calculated polarity scores about a food item, we obtain a rating to individual menu items of the restaurant. The proposed food item rating is more reliable since it does not depend on reviews of only one site.

• Asia-Pacific Journal of Business
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• v.14 no.4
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• pp.37-48
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• 2023

Purpose - This study aims to explore the influence of emotional discrepancies among investors on the cryptocurrency market. It focuses on how varying emotions affect market dynamics such as volatility and trading volume in the context of Bitcoin trading. Design/methodology/approach - This study involves analyzing data from Bitcointalk.org, consisting of 57,963 posts and 2,215,776 responses from November 22, 2009, to December 31, 2022. Tools used include the Linguistic Inquiry and Word Count (LIWC) software for classifying emotional content and the Python Pattern library for sentiment analysis. Findings - The results show that heterogeneous emotional feedback, whether positive or negative, significantly influences Bitcoin's intraday volatility, skewness, and trading volume. These findings are more pronounced when the underlying emotion in the feedback is amplified. Research implications or Originality - This study underscores the significance of emotional factors in financial decision-making, especially within the realm of social media. It suggests that investors and market strategists should consider the emotional landscape of online forums when making investment choices or formulating market strategies. The research also paves the way for future studies regarding the behavioral impact of emotions on the cryptocurrency market.

• The Journal of the Korea Contents Association
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• v.20 no.11
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• pp.110-118
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• 2020

Social media data serves as a proxy indicator to understand the problems and the future of public opinion in Korean society. This research used 109,015 news data from 2016 to 2018 to analyze the sensitivity of the elderly and employment in Korean society, and explored the possibility of expanding the labor force in Korean society, which is facing a cliff between the elderly and the population. Topic keywords for employment of the elderly include "elderly*employment", "elderly*employment", and "elderly*wage". As a result of the analysis, positive sensitivity prevails for most of the period, and it is possible to expand the working-age population. Positive feelings about expanding employment opportunities for the elderly and negative feelings about low wages have brought to light the reality of the elderly who are still poor despite their work. In this study, social big data was used to analyze the perceptions and sensibilities of Korean society related to the elderly and employment through hierarchical crowd analysis and related text mining analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.2
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• pp.260-267
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• 2016

The Internet has brought lots of changes to us sharing information mutually. However, as all social symptom have double-sided character, it has serious social problem. Vicious users have been taking advantage of anonymity on the Internet, stating comments aggressively for defamation, personal attacks, privacy violation and more. Malicious comments on the Internet are creating the biggest problem regarding unlawful acts and insults which occur on the Internet. In order to solve the issues, several studies have been done to efficiently manage the comments. However, there are limitations to recognize modified malicious vocabulary in previous research. So, in this paper, we propose a malicious comments detection technique by improving limitation of previous studies. The experimental result has shown accuracy of 87.8% providing higher accuracy as compared to previous studies done.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.11
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• pp.419-426
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• 2018

Due to the FMD(foot-and-mouth disease), the domestic animal husbandry and related industries suffer enormous damage every year. Although various academic researches related to FMD are ongoing, engineering studies on the social effects of FMD are very limited. In this study, we propose a systematic methodology to analyze emotional responses of regular citizens on FMD using text mining techniques. The proposed system first collects data related to FMD from the tweets posted on Twitter, and then performs a polarity classification process using a deep-learning technique. Second, keywords are extracted from the tweet using LDA, which is one of the typical techniques of topic modeling, and a keyword network is constructed from the extracted keywords. Finally, we analyze the various social effects of regular citizens on FMD through keyword network. As a case study, we performed the emotional analysis experiment of regular citizens about FMD from July 2010 to December 2011 in Korea.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.533-541
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• 2020

This study aims for investigating the consumers' reaction to the New Hanbok Style of K-pop idol groups. We collected YouTube videos and user comments that include 'Black Pink New Hanbok' as a keyword, applying social network analysis and sentiment analysis. First, the New Hanbok of Black Pink was designed as a mini-dress to make it easier to dance and turned out that it reinterpreted traditional elements modernly. Second, the issue about revealing costumes appeared as a keyword in domestic reactions, it did not appear in international reaction. Third, as a result of sentiment analysis, international audience viewed New Hanbok outfit more positively than domestic audience. This study is significant in that it suggests the direction to which New Hanbok should head to by investigating extensive consumers' reaction and finding out the positive and negative elements of New Hanbok.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.107-115
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• 2023

This study analyzes the content of YouTube Vlog videos created by patients of Coronavirus disease 2019 ("COVID-19") in South Korea and viewer comments on those videos. As this new infectious disease started to sweep the world in late 2019 and early 2020, the public started facing fear and uncertainty stemming from the lack of sufficient and accurate information about the virus. At the same time, as COVID-19 patients in South Korea were treated in isolation to prevent the spread of the virus, the patients themselves were experiencing anxiety and exclusion from the society. During this period, there was an increase in YouTube Vlog videos created by the patients in which they shared their experiences going through the treatment and recovery processes. To understand how these YouTube Vlog videos were being used by the patients to connect with the society and seek support in a state of isolation and anxiety, this study conducted a qualitative multi-case analysis of three sample YouTube Vlog video channels to analyze their content, as well as a lexicon-based sentiment analysis of viewer comments to understand the experiences and reactions of viewers. The patients' YouTube Vlog videos showed that they shared similar stages of progress, despite each emphasizing a different main theme. Overall, the tone of the viewer comments became increasingly positive over time, although with some variance among different patient cases and stages. The results confirmed that Vlogs of patients played a significant role in reducing the uncertainty around COVID-19 and strengthening social support for the patients. The findings of this study can improve an understanding of the psychological and behavioral aspects of patient experience in isolated treatment and the impact of shared communication among members of society in times of crisis.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.25-52
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• 2018

This study performs social network analysis based on consumer sentiment related to a location in Seoul using data reflecting consumers' web search activities and emotional evaluations associated with commerce. The study focuses on large commercial districts in Seoul. In addition, to consider their various aspects, social network indexes were combined with the trading area's public data to verify factors affecting the area's sales. According to R square's change, We can see that the model has a little high R square value even though it includes only the district's public data represented by static data. However, the present study confirmed that the R square of the model combined with the network index derived from the social network analysis was even improved much more. A regression analysis of the trading area's public data showed that the five factors of 'number of market district,' 'residential area per person,' 'satisfaction of residential environment,' 'rate of change of trade,' and 'survival rate over 3 years' among twenty two variables. The study confirmed a significant influence on the sales of the trading area. According to the results, 'residential area per person' has the highest standardized beta value. Therefore, 'residential area per person' has the strongest influence on commercial sales. In addition, 'residential area per person,' 'number of market district,' and 'survival rate over 3 years' were found to have positive effects on the sales of all trading area. Thus, as the number of market districts in the trading area increases, residential area per person increases, and as the survival rate over 3 years of each store in the trading area increases, sales increase. On the other hand, 'satisfaction of residential environment' and 'rate of change of trade' were found to have a negative effect on sales. In the case of 'satisfaction of residential environment,' sales increase when the satisfaction level is low. Therefore, as consumer dissatisfaction with the residential environment increases, sales increase. The 'rate of change of trade' shows that sales increase with the decreasing acceleration of transaction frequency. According to the social network analysis, of the 25 regional trading areas in Seoul, Yangcheon-gu has the highest degree of connection. In other words, it has common sentiments with many other trading areas. On the other hand, Nowon-gu and Jungrang-gu have the lowest degree of connection. In other words, they have relatively distinct sentiments from other trading areas. The social network indexes used in the combination model are 'density of ego network,' 'degree centrality,' 'closeness centrality,' 'betweenness centrality,' and 'eigenvector centrality.' The combined model analysis confirmed that the degree centrality and eigenvector centrality of the social network index have a significant influence on sales and the highest influence in the model. 'Degree centrality' has a negative effect on the sales of the districts. This implies that sales decrease when holding various sentiments of other trading area, which conflicts with general social myths. However, this result can be interpreted to mean that if a trading area has low 'degree centrality,' it delivers unique and special sentiments to consumers. The findings of this study can also be interpreted to mean that sales can be increased if the trading area increases consumer recognition by forming a unique sentiment and city atmosphere that distinguish it from other trading areas. On the other hand, 'eigenvector centrality' has the greatest effect on sales in the combined model. In addition, the results confirmed a positive effect on sales. This finding shows that sales increase when a trading area is connected to others with stronger centrality than when it has common sentiments with others. This study can be used as an empirical basis for establishing and implementing a city and trading area strategy plan considering consumers' desired sentiments. In addition, we expect to provide entrepreneurs and potential entrepreneurs entering the trading area with sentiments possessed by those in the trading area and directions into the trading area considering the district-sentiment structure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2571-2586
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• 2022

The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research. This study uses Python language to analyze the big data of the Chinese provincial government Weibo. First, this study uses a web crawler approach to collect and statistically describe over 360,000 data from 31 provincial government microblogs in China, covering the period from January 2018 to April 2022. Second, a word separation engine is constructed and these text data are analyzed using word cloud word frequencies as well as semantic relationships. Finally, the text data were analyzed for sentiment using natural language processing methods, and the text topics were studied using LDA algorithm. The results of this study show that, first, the number and scale of posts on the Chinese government Weibo have grown rapidly. Second, government Weibo has certain social attributes, and the epidemics, people's livelihood, and services have become the focus of government Weibo. Third, the contents of government Weibo account for more than 30% of negative sentiments. The classified topics show that the epidemics and epidemic prevention and control overshadowed the other topics, which inhibits the diversification of government Weibo.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.202-206
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• 2023

Today, social media has become a vital tool. The world communicates and reaches the news and each other's opinions through social media accounts. Recently, considerable research has been done on analyzing social media due to its rich data content. At the same time, since the beginning of the COVID-19 pandemic, which has afflicted so many around the world, the search for a vaccine has been intense. There have been many studies analyzing people's feelings during a crisis. This study aims to understand people's opinions about available Coronavirus vaccines through a learning model that was developed for this purpose. The dataset was collected using Twitter's streaming Application Programming Interface (API) , then combined with another dataset that had already been collected. The final dataset was cleaned, then analyzed using Python. Polarity and subjectivity functions were used to obtain the results. The results showed that most people had positive opinions toward vaccines in general and toward the Pfizer one. Our study should help governments and decision-makers dispel people's fears and discover new symptoms linked to those listed by the World Health Organization.