• International Journal of Advanced Culture Technology
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• 제11권3호
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• pp.332-337
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• 2023

The purpose of the study was to analyze consumers' perceptions of gamification. Based on the analyzed data, we would like to provide data by systematically organizing the concept, game elements, and mechanisms of gamification. Recently, gamification can be easily found around medical care, corporate marketing, and education. This study collected keywords from social media portal sites Naver, Daum, and Google from 2018 to 2023 using TEXTOM, a social media analysis tool. In this study, data were analyzed using text mining, semantic network analysis, and CONCOR analysis methods. Based on the collected data, we looked at the relevance and clusters related to gamification. The clusters were divided into a total of four clusters: 'Awareness of Gamification', 'Gamification Program', 'Future Technology of Gamification', and 'Use of Gamification'. Through social media analysis, we want to investigate and identify consumers' perceptions of gamification use, and check market and consumer perceptions to make up for the shortcomings. Through this, we intend to develop a plan to utilize gamification.

• Journal of Contemporary Eastern Asia
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• 제16권2호
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• pp.110-144
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• 2017

Unlike most languages, written Chinese has no spaces between words. Word segmentation must be performed before semantic network analysis can be conducted. This paper describes how to perform Chinese word segmentation using the Stanford Natural Language Processing group's Stanford Word Segmenter v. 3.8.0, released in June 2017.

• Human Ecology Research
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• 제58권4호
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• pp.613-633
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• 2020

This study identifies consumers' perceived benefits and costs when using Samsung Health (a healthcare app) based on consumer reviews from Google Play Store's app and social media discourse. We examine the differences in the benefits and the costs of Samsung Health using these two sources of data. We conducted text frequency analysis, clustering analysis, and semantic network analysis using R programming. The major findings are as follows. First, consumers experience benefits and costs on several functions of the app, such as step counting, device interlocking, information acquisition, and competition with global consumers. Second, the results of semantic network analysis showed that there were eight benefit factors and three cost factors. We also found that the three costs correspond to the benefits, indicating that some consumers gained benefits from certain functions while others gained costs from the same functions. Third, the comparison between consumer app review and social media discourse showed that the former is appropriate to assess the performance of app functions, while the latter is appropriate to examine how the app is used in daily life and how consumers feel about it. The current study suggests managerial implications to healthcare app service providers regarding what they should strengthen and improve to enhance consumers' satisfaction. It also suggests some implications from the two media, which can be mutually complementary, for researchers who study consumer opinions.

• Journal of Contemporary Eastern Asia
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• 제23권1호
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• pp.18-34
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• 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 Contemporary Eastern Asia
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• 제18권1호
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• pp.189-216
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• 2019

The frequent occurrence of overwhelming disasters necessitates risk communication systems capable of operating effectively in disaster contexts. Few studies have examined risk communication networks during disasters through social networking services (SNS). This study therefore investigates the patterns of risk communication by comparing Korean and international networks based on the social amplification of risk communication in the context of the Sewol ferry disaster (SFD). In addition, differences in language use and patterns between Korean and international contexts are identified through a semantic analysis using KrKwick, NodeXL, and UCINET. The SFD refers to the sinking of the ferry while carrying 476 people, mostly secondary school students. The results for interpersonal risk communication reveal that the structure of the Korean risk communication network differed from that of the international network. The Korean network was more fragmented, and its clustering was more sparsely knitted based on the impact and physical proximity of the disaster. Semantic networks imply that the physical distance from the disaster affected the content of risk communication, as well as the network pattern.

• International journal of advanced smart convergence
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• 제11권2호
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• pp.95-101
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• 2022

The development of the 4th industry has increased social media, and the rise of COVID-19 has stimulated non-face-to-face services. People's consumption patterns are also changing a lot due to non-face-to-face services. In this paper, food content keywords are derived through social network-based semantic network analysis, emotions are analyzed, and keywords applied to food recommendation platforms are input. We collected food, influencer, and corona keyword analysis data through Textom. A lot of research has been done through online reviews of existing influencer content. However, there is a lack of research on keyword sentiment analysis provided by influencers rather than consumers and research perspectives. This paper uploads language and topics derived through online reviews of existing publications and subscribers, and goes beyond the limits used in marketing methods. By analyzing keywords that influencers suggest when uploading content, you can apply data that applies them to food recommendation platforms and applications.

• ETRI Journal
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• 제34권4호
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• pp.591-601
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• 2012

Unstructured peer-to-peer (p2p) networks usually employ flooding search algorithms to locate resources. However, these algorithms often require a large storage overhead or generate massive network traffic. To address this issue, previous researchers explored the possibility of building efficient p2p networks by clustering peers into communities based on their social relationships, creating social-like p2p networks. This study proposes a social relationship p2p network that uses a measure based on Hebbian theory to create a social relation weight. The contribution of the study is twofold. First, using the social relation weight, the query peer stores and searches for the appropriate response peers in social-like p2p networks. Second, this study designs a novel knowledge index mechanism that dynamically adapts social relationship p2p networks. The results show that the proposed social relationship p2p network improves search performance significantly, compared with existing approaches.

• International Journal of Computer Science & Network Security
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• 제24권2호
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• pp.189-195
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• 2024

The issue "Exploring Social Media and Other Crucial Success Elements of Attitude towards Politics and Intention for Voting in Pakistan" is a huge study embracing more issues. The politics of Pakistan is basically the politics of semantic groups. Pakistan is a multilingual state more than six languages. There are 245 religious parties in Pakistan, as elaborated by the Daily Times research. The use of social media sites in Pakistan peaked to its maximum after announcement of election schedule by the Election Commission of Pakistan in March 22, 2013. Most of the political parties used it for the recent elections in Pakistan to promote their agenda and attract country's 80 million registered electors. This study was aiming to investigate the role of social media and other critical variables in the attitude towards politics and intention for voting.

• 디지털산업정보학회논문지
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• 제8권4호
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• pp.43-51
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• 2012

Web 3.0 is the next-generation of the World Wide Web and is included two main platforms, semantic technologies and social computing environment. The basic idea of web 3.0 is to define structure data and link them in order to more effective discovery, automation, integration, and reuse across various applications. The semantic technologies represent open standards that can be applied on the top of the web. The social computing environment allows human-machine co-operations and organizing a large number of the social web communities. In the recent years, recommender systems have been combined with ontologies to further improve the recommendation by adding semantics to the context on the web 3.0. In this paper, we study previous researches about recommendation method and propose a recommendation method based on web 3.0. Our method scores documents based on context tags and social network services. Our social scoring model is computed by both a tagging score of a document and a tagging score of a document that was tagged by a user's friends.

• 임상간호연구
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• 제25권3호
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• pp.209-220
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• 2019

Purpose: A significant amount of public opinion about nurse bullying is expressed on the internet. The purpose of this study was to analyze the linkage structures among words extracted from comments on internet articles related to nurse workplace bullying using semantic network analysis. Methods: From February 2018 to April 2019, comments made on news articles posted to the Daum and Naver web portal containing keywords such as "nurse", "Taeum", and "bullying" were collected using a web crawler written in Python. A morphological analysis performed with Open Korean Text in KoNLPy generated 54 major nodes. The frequencies, eigenvector centralities, and betweenness centralities of the 54 nodes were calculated and semantic networks were visualized using the UCINET and NetDraw programs. Convergence of iterated correlations (CONCOR) analysis was performed to identify structural equivalence. Results: This paper presents results about March 2018 and January 2019 because these months had highest number of articles. Of the 54 major nodes, "nurse", "hospital", "patient", and "physician" were the most frequent and had the highest eigenvector and betweenness centralities. The CONCOR analysis identified work environment, nurse, gender, and military clusters. Conclusion: This study structurally explored public opinion about nurse bullying through semantic network analysis. It is suggested that various studies on nursing phenomena will be conducted using social network analysis.