Earticle

초록

영어

The primary objective of this study is to collect, clean, and analyze big data centered around news articles from portal sites and social media pertaining to League of Legends(LoL), a representative game in the esports industry. By extracting valuable information, semantic connections, and context from this unstructured data, we aim to provide practical implications for the esports industry. In order to collect popularity data of the most 'League of Legends' game among e-sports games, Textom, a big data solution service, was used to collect related keywords from October 8, 2023 to June 30, 2024. Textom collected data for Naver and Google. Specifically, 2,024 news sections, 8,874 blog sections, and 2,969 cafe sections were collected on the Naver channel. On the Google channel, 3,734 news sections and 59 Facebook sections were collected. Amounting to 17,660 materials. The collected data was analyzed using Textom and Ucinet 6.0. We conducted TF analysis and TF-IDF analysis through text mining, followed by matrix analysis and semantic network analysis. Additionally, CONCOR analysis was used to derive clusters of keywords with similar meanings. Based on the analysis results, the following conclusions were drawn. First, the most frequent keywords in the collected data were ‘LOL’, ‘game’, ‘Riot Games Inc.’, ‘sale’, and ‘skin’. The TF-IDF ranking was ‘game’, ‘Riot Games Inc.’, ‘sale’, ‘skin’, and ‘T1’. These two analysis results suggest that there is a high level of interest and issues related to purchasing LOL games and the developer. Second, through semantic network analysis, we identified three types of centrality. Considering the overall centrality, keywords related to competitions, developers, the T1 team, and time or seasons showed high centrality. Third, CONCOR analysis resulted in four clusters. First, as the main topic of this study is LOL, Cluster A consisted of keywords related to ‘e-Sports Game’. This cluster included the most influential and popular player, Faker, and tournament names such as the World Championship. Cluster B was the ‘LOL’ cluster, which is the main topic of the study. Keywords related to actual participation, such as game companies, skins, patches, and play, were central to this cluster. Cluster C centered around keywords related to ‘Strategy’ for winning games, such as ‘item build’, ‘Howling Abyss’, ‘strategy’, ‘Rune’, ‘item’, and ‘Counter’. Cluster D focused on keywords related to ‘Transaction’, such as ‘sale’, ‘price’, ‘deal’, ‘completion’, ‘private transaction’, ‘Ahri’, and ‘direct payment’.

목차

Abstract
1. Introduction
2. Research method
2.1. Data collection and analysis method
2.2. Analysis process
3. Results
3.1. Top keyword frequency analysis TF-IDF analysis through text mining
3.2. Semantic network analysis
3.3. CONCOR analysis
4. Conclusion
Acknowledgement
References

키워드

저자

• Kyung-Won Byun [ Assistant Professor, Department of graduate school of business Administration, Dankook University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380