• Journal of Korean Library and Information Science Society
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• v.53 no.4
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• pp.143-162
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• 2022

Altmetrics is an alternative impact evaluation index that evaluates the social interest in the research performance of individuals or institutions in universities, research institutions, and research fund support institutions. This study empirically analyzed what kind of attention a papers of domestic library and information science researchers published in an international academic journal was receiving in the international community using Altmetric explorer. As a result of the analysis, 230 papers were tracked. The average Altmetric Attention Score (AAS) was 6.63, but there were 2 papers that received overwhelming attention (over 170 points) as they were mentioned in news report and Twitter. Second, there was a tendency for high AAS to appear in cases where a domestic researcher participated as a co-author and the main author belonged to an overseas institution, and in the case where the research funds were supported by foreign government agencies. In addition to the field of the library information science or information system, the papers classified as the field of public health service and education showed high AAS, and it was confirmed that these papers were published in the journals of various fields such as life science. Finally, it was confirmed that there was a weak correlation of r =0.25 between the AAS and the number of citations of the analyzed paper, but a strong correlation of r =0.68 between the number of Mendeley readers and the number of citations.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.317-340
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• 2023

In a business context, ESG is defined as the use of environmental, social, and governance factors to assess a firm's progress in terms of sustainability. Social media has enabled the public to actively share firms' good and/or bad deeds, increasing public interest in ESG management. Therefore, this study aimed to investigate the association of firm performances with the respective sentiments towards each of environmental, social, and governance activities, as well as comprehensive ESG sentiments, which encompass all environmental, social, and governance sentiments. This study used panel regression models to examine the relationship between social media ESG sentiment and the Return on Assets (ROA) and Return on Equity (ROE) of 143 companies listed on the KOSPI 200. We collected data from 2018 to 2021, including sentiment data from a variety of social media channels, such as online communities, Instagram, blogs, Twitter, and other news. The results indicated that firm performance is significantly related to respective ESG and comprehensive ESG sentiments. This study has several implications. By using data from various social media channels, it presents an unbiased view of public ESG sentiment, rather than relying on ESG ratings, which may be influenced by rating agencies. Furthermore, the findings can be used to help firms determine the direction of their ESG management. Therefore, this study provides theoretical and practical insights for researchers and firms interested in ESG management.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.127-135
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• 2023

This research paper explores the application of FinBERT, a variational BERT-based model pre-trained on financial domain, for sentiment analysis in the financial domain while focusing on the process of identifying suitable training data and hyperparameters. Our goal is to offer a comprehensive guide on effectively utilizing the FinBERT model for accurate sentiment analysis by employing various datasets and fine-tuning hyperparameters. We outline the architecture and workflow of the proposed approach for fine-tuning the FinBERT model in this study, emphasizing the performance of various datasets and hyperparameters for sentiment analysis tasks. Additionally, we verify the reliability of GPT-3 as a suitable annotator by using it for sentiment labeling tasks. Our results show that the fine-tuned FinBERT model excels across a range of datasets and that the optimal combination is a learning rate of 5e-5 and a batch size of 64, which perform consistently well across all datasets. Furthermore, based on the significant performance improvement of the FinBERT model with our Twitter data in general domain compared to our news data in general domain, we also express uncertainty about the model being further pre-trained only on financial news data. We simplify the complex process of determining the optimal approach to the FinBERT model and provide guidelines for selecting additional training datasets and hyperparameters within the fine-tuning process of financial sentiment analysis models.

• Asia Marketing Journal
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• v.16 no.4
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• pp.75-87
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• 2015

As more companies become interested in global markets, it has become crucial for firms to create globalized brands whose positioning, advertising strategy, personality, looks, and feel are consistent across nations. The purpose of this study is to investigate the global branding strategy of the Hyundai Motor Company (hereafter HMC) in order to show how the company processes its branding strategy. HMC, one of the leading global companies in the automobile industry, set up its brand identity as "Modern premium", in alignment with their new slogan "New Thinking New Possibilities", in 2011. The aim of the "Modern premium" concept was to provide consumers with new experiences and values beyond their expectations. HMC wanted their consumers to think of their cars as not only a medium of transportation but as a life space, where they can share experiences alongside HMC. In an effort to conduct consumer research in 5 different nations, HMC selected "brilliant" as a key communication concept. The word "brilliant" expresses the functional, experiential, and emotional dimensions of HMC. HMC furthermore chose "live brilliant" as a key campaign message in order to reinforce their communication concept. After this decision, the "live brilliant" campaign was exhibited through major broadcast channels around the world. The campaign was the company's first worldwide brand campaign, where a single message was applied to all major markets, with the goal of building up a consistent image as a global brand. This global branding strategy is worth examining due to its significant contribution to growth generation in the global market. Overall, the 'live brilliant' global brand campaign not only improved HMC's reputation image-wise, with the 'Modern Premium' conceptualization of the brand as 'simple', 'creative' and 'caring', but also improved the consumer's familiarity, preference and purchase intention of HMC. In fact, the "live brilliant" campaign was a successful campaign which increased HMC's brand value. Notably, HMC's brand value increased continuously and reached 9 billion US dollars in 2013, leading it to reach 43rd place in the Global Brand Rankings according to the brand consulting group Interbrand. Its brand value largely surpassed that of Nissan (65th) and Chevrolet (89th) in 2013. While it is true that the global branding strategy of HMC involved higher risks, it was highly successful according to cross-nation consumer research. Therefore, this paper concludes that the global branding strategy of HMC made a positive impact on its performance. We further suggest HMC to combine its successful marketing with social media such as Facebook, Twitter, and Instagram and embrace digital media by extending its brand communication horizon to the mobile internet

• Journal of the Korean Society for information Management
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• v.39 no.1
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• pp.91-117
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• 2022

The number of depressed patients in Korea and around the world is rapidly increasing every year. However, most of the mentally ill patients are not aware that they are suffering from the disease, so adequate treatment is not being performed. If depressive symptoms are neglected, it can lead to suicide, anxiety, and other psychological problems. Therefore, early detection and treatment of depression are very important in improving mental health. To improve this problem, this study presented a deep learning-based depression tendency model using Korean social media text. After collecting data from Naver KonwledgeiN, Naver Blog, Hidoc, and Twitter, DSM-5 major depressive disorder diagnosis criteria were used to classify and annotate classes according to the number of depressive symptoms. Afterwards, TF-IDF analysis and simultaneous word analysis were performed to examine the characteristics of each class of the corpus constructed. In addition, word embedding, dictionary-based sentiment analysis, and LDA topic modeling were performed to generate a depression tendency classification model using various text features. Through this, the embedded text, sentiment score, and topic number for each document were calculated and used as text features. As a result, it was confirmed that the highest accuracy rate of 83.28% was achieved when the depression tendency was classified based on the KorBERT algorithm by combining both the emotional score and the topic of the document with the embedded text. This study establishes a classification model for Korean depression trends with improved performance using various text features, and detects potential depressive patients early among Korean online community users, enabling rapid treatment and prevention, thereby enabling the mental health of Korean society. It is significant in that it can help in promotion.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.9
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• pp.59-70
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• 2016

The rapid development of the Chinese version of Twitter, the groom Weibo has become an important communication means for Chinese SNS users to obtain and share information. As a result, in China, the phenomenon of the power shift has emerged from the traditional opinion leaders to SNS opinion leasers. The relationship analysis of demographic variables of the Chinese SNS users and their Information on the relationship between keywords was made by utilizing the centrality analysis using Social Network Program NetMiner. China's SNS opinion leaders have general interest in daily activities with their families or friends rather than in social issues. And in case of SNS opinion leaders of high betweenness centrality, it was analyzed that general users was a key mediator role that organically out lead to the adjacent information. These properties are not independent of demographic variables, such as professional, therefore, the demographic characteristics of SNS opinion leaders showed a significant effect on the parameters of betweenness centrality. This study analyzed the characteristics of SNS users, especially opinion leaders in China by looking at the aspects of Chinese social phenomenon in terms of information. Through this study, we expect to provide basic information about the social characteristics of China through collective communication.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.47-51
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• 2021

In this study, we try to find out and analyze the results of reduced self-esteem and loss using text mining. Physical health is important, of course, but these days, mental health is considered more important. In order for the mind to be healthy, it is important to have self-esteem and self-confidence first. Self-esteem decreases, and if lost, it directly leads to depression. If depression is severe, the worst will lead to self-harm and suicide. However, more and more people are committing suicide these days because both ordinary people and entertainers cannot overcome depression. For this reason, the seriousness of depression and loss of self-esteem are also considered important and become an issue. Therefore, we want to collect data for a certain period of time through Naver, Instagram, and Twitter searches and extract the words of the data to anticipate and analyze the cause of loss of self-esteem, how serious the recent depression is, and what the consequences of loss of self-esteem are.

• The Journal of Bigdata
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• v.7 no.2
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• pp.235-244
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• 2022

The rental history data of public bicycles in Seoul were analyzed to examine how pandemic phenomena such as COVID-19 caused changes in people's micro mobility. Data for 2019 and 2021 were compared and analyzed by dividing them before and after COVID-19. Data were collected from public data portal sites, and data marts were created for in-depth analysis. In order to compare the changes in the two periods, the riding direction type dimension and the rental station type dimension were added, and the derived variables (rotation rate per unit, riding speed) were newly created. There is no significant difference in the average rental time before and after COVID-19, but the average rental distance and average usage speed decreased. Even in the mobility of Ttareungi, you can see the slow rhythm of daily life. On weekdays, the usage rate was the highest during commuting hours even before COVID-19, but it increased rapidly after COVID-19. It can be interpreted that people who are concerned about infection prefer Ttareungi to village buses as a means of micro-mobility. The results of data mart-based visualization and analysis proposed in this study will be able to provide insight into public bicycle operation and policy development. In future studies, it is necessary to combine SNS data such as Twitter and Instagram with public bicycle rental history data. It is expected that the value of related research can be improved by examining the behavior of bike users in various places.

• Information Systems Review
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• v.21 no.1
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• pp.91-111
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• 2019

Around the end of 2017, the investment fever for cryptocurrencies-especially Bitcoin-has started all over the world. Especially, South Korea has been at the center of this phenomenon. Sinceit was difficult to find the profitable investment opportunities, people have started to see the cryptocurrency markets as an alternative investment objects. However, the cryptocurrency fever inSouth Korea is mostly based on psychological phenomenon due to expectation of short-term profits and social atmosphere rather than intrinsic value of the assets. Therefore, this study aimed to analyze influence of people's social sentiment on price movement of cryptocurrency. The data was collected for 181 days from Nov 1st, 2017 to Apr 30th, 2018, especially focusing on Bitcoin-related post in Twitter along with price of Bitcoin in Bithumb/UPbit. After the collected data was refined into neutral, positive and negative words through sentiment analysis, the refined neutral, positive, and negative words were put into regression model in order to find out the impacts of social sentiments on Bitcoin price. After examining the relationship by the regression analyses and Granger Causality tests, we found that the positive sentiments had a positive relationship with Bitcoin price, while the negative words had a negative relation with it. Also, the causality test results show that there exist two-way causalities between social sentiment and Bitcoin price movement. Therefore, we were able to conclude that the Bitcoin investors'behaviors are affected by the changes of social sentiments.

• Journal of Platform Technology
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• v.12 no.1
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• pp.55-66
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• 2024

The K-POP market has become influential not only in culture but also in society as a whole, including diplomacy and environmental movements. As a result, various papers have been conducted based on machine learning to identify the success factors of idols by utilizing traditional data such as music and recordings. However, there is a limitation that previous studies have not reflected the influence of new media platforms such as Instagram releases, YouTube shorts, TikTok, Twitter, etc. on the popularity of idols. Therefore, it is difficult to clarify the causal relationship of recent idol success factors because the existing studies do not consider the daily changing media trends. To solve these problems, this paper proposes a data collection system and analysis methodology for idol-related data. By developing a container-based real-time data collection automation system that reflects the specificity of idol data, we secure the stability and scalability of idol data collection and compare and analyze the clusters of successful idols through a K-Means clustering-based outlier detection model. As a result, we were able to identify commonalities among successful idols such as gender, time of success after album release, and association with new media. Through this, it is expected that we can finally plan optimal comeback promotions for each idol, album type, and comeback period to improve the chances of idol success.