• Title/Summary/Keyword: Text Mining Analysis

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Big Data Analysis of the Women Who Score Goal Sports Entertainment Program: Focusing on Text Mining and Semantic Network Analysis.

  • Hyun-Myung, Kim;Kyung-Won, Byun
    • International Journal of Internet, Broadcasting and Communication
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    • v.15 no.1
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    • pp.222-230
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    • 2023
  • The purpose of this study is to provide basic data on sports entertainment programs by collecting data on unstructured data generated by Naver and Google for SBS entertainment program 'Women Who Score Goal', which began regular broadcast in June 2021, and analyzing public perceptions through data mining, semantic matrix, and CONCOR analysis. Data collection was conducted using Textom, and 27,911 cases of data accumulated for 16 months from June 16, 2021 to October 15, 2022. For the collected data, 80 key keywords related to 'Kick a Goal' were derived through simple frequency and TF-IDF analysis through data mining. Semantic network analysis was conducted to analyze the relationship between the top 80 keywords analyzed through this process. The centrality was derived through the UCINET 6.0 program using NetDraw of UCINET 6.0, understanding the characteristics of the network, and visualizing the connection relationship between keywords to express it clearly. CONCOR analysis was conducted to derive a cluster of words with similar characteristics based on the semantic network. As a result of the analysis, it was analyzed as a 'program' cluster related to the broadcast content of 'Kick a Goal' and a 'Soccer' cluster, a sports event of 'Kick a Goal'. In addition to the scenes about the game of the cast, it was analyzed as an 'Everyday Life' cluster about training and daily life, and a cluster about 'Broadcast Manipulation' that disappointed viewers with manipulation of the game content.

Analysis of New Market Structure Using Text Mining and Consumer Perceptions Map: The Case of the Korean Craft Beer Market (소비자 리뷰 텍스트마이닝을 이용한 신생 산업 시장 구조 분석: 국내 수제 맥주 시장의 경쟁 관계 및 시장 구조를 중심으로)

  • Lee, Yeon Soo;Kim, Hye Jin
    • The Journal of Information Systems
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    • v.30 no.2
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    • pp.189-214
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    • 2021
  • Purpose This paper aims to effectively utilize user-generated content (UGC) and analyze the market structure of a relatively new market which lacks rich user review information. Specifically, we propose a domain-specific text mining tool for the domestic craft beer market and visualize the market structure by incorporating how individual beer products are positioned in the perceptual map of consumers. Design/methodology/approach We collect user review information from Naver blogs, and extract words that describe beers. We identify semantic relationships between beer products through text mining, and then depending on these semantic relationships, construct a graph representing the market structure of the domestic craft beer market based on the consumer's perceptual map. Findings First, beer products produced in the same brewery are perceived as very similar to consumers. Second, only two products, 'Heukdang Milky Stout' and 'Gompyo', was noticeably distinguishable from other products. Third, even though 'Gyeongbokgung' is from a different brewery, it is located very close to the products of 'Jeju Beer' brewery such as 'Jeju Baeknokdam Ale' and 'Seongsan Ilchulbong Ale', which suggests the influence of 'landmark series.' We successfully show that our methodology effectively describes the market structure of the craft beer market.

Analysis of patterns in meteorological research and development using a text-mining algorithm (텍스트 마이닝 알고리즘을 이용한 기상청 연구개발분야 과제의 추세 분석)

  • Park, Hongju;Kim, Habin;Park, Taeyoung;Lee, Yung-Seop
    • The Korean Journal of Applied Statistics
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    • v.29 no.5
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    • pp.935-947
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    • 2016
  • This paper considers the analysis of patterns in meteorological research and development using a text-mining algorithm as the method of analyzing unstructured data. To analyze text data, we define a list of terms related to meteorological research and development, construct times series of a term-document matrix through data preprocessing, and identify terms that have upward or downward patterns over time. The proposed methodology is applied to multi-year plans funded by Korea Meteorological Administration research and development programs from 2011 to 2015.

Trend Analysis of Thyroid Cancer Research in Korea with Text Mining Techniques

  • Lee, Tae-Gyeong;Heo, Seong-Min;Shin, Seung-Hyeok;Yang, Ji-Yeon
    • Journal of the Korea Society of Computer and Information
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    • v.23 no.12
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    • pp.153-161
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    • 2018
  • In this paper, we propose a text-centered approach to identify the research trend of thyroid cancer in Korea. We incorporate statistical analysis, text mining and machine learning techniques with our clinical insights to find connective associations between terminologies and to discover informative clusters of literatures. The incidence of thyroid cancer in Korea increased rapidly in the 2000s, which fueled the debate regarding overdiagnosis, but recently the number of patients undergoing surgery has decreased significantly due to conscious reform efforts from various circles. We analyzed the abstracts and keywords of related research papers from DBpia. It was found that most were case reports in the 1980s, and some papers in the 1990s discussed the early detection of thyroid cancer by mass screening. While many papers focused on different diagnostic techniques and the detection of small cancers in the 2000s, many emphasized more on the quality of life of patients in the 2010s. There was an apparent change in the topics of thyroid cancer research over past decades. The results of this study would serve as a reference guide for current and future research directions.

A Text Mining Approach to the Comparative Analysis of the Blockchain Issues : South Korea and the United States (텍스트 마이닝을 활용한 블록체인 이슈 분석 : 한국과 미국)

  • Shon, Saeah;Jeon, Byeong-Jin;Kim, Hee-Woong
    • Journal of Information Technology Services
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    • v.18 no.1
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    • pp.45-61
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    • 2019
  • Blockchain technology, which enables transparent transactions among individuals without central control, opens up diverse business possibilities. It is also expected that blockchain will have a ripple effect on the entire area of society including finance, manufacturing, distribution, and the public sector. Previous studies related to the blockchain also deals with its functional features and application to industrial and public fields. In the new technology such as blockchain, it is necessary to know what social perception is in order to create technological development environment, but there is a lack of research on it. Therefore, this study aims to find out the implications for industrial and policy direction by analyzing issues related to the blockchain in South Korea and the US through text mining. From these two countries, we collected text data related to blockchain in online communities and internet articles. Then, we did co-occurrence analysis and topic modeling on them respectively. As a result of this study, we have found common points and differences in keywords and topics extracted from social media in the two countries. Based on them, we can offer helpful suggestions for building a sound blockchain ecosystem, and directions for future research.

Study on CEO New Year's Address: Using Text Mining Method (텍스트마이닝을 활용한 주요 대기업 신년사 분석)

  • YuKyoung Kim;Daegon Cho
    • Journal of Information Technology Services
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    • v.22 no.2
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    • pp.93-127
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    • 2023
  • This study analyzed the CEO New Year's addresses of major Korean companies, extracting key topics for employees via text mining techniques. An intended contribution of this study is to assist reporters, analysts, and researchers in gaining a better understanding of the New Year's addresses by elucidating the implicit and implicative features of messages within. To this end, this study collected and analyzed 545 New Year's addresses published between 2012 and 2021 by the top 66 Korean companies in terms of market capitalization. Research methodologies applied include text clustering, word embedding of keywords, frequency analysis, and topic modeling. Our main findings suggest that the messages in the New Year's addresses were categorized into nine topics-organizational culture, global advancement, substantial management, business reorganization, capacity building, market leadership, management innovation, sustainable management, and technology development. Next, this study further analyzed the managerial significance of each topic and discussed their characteristics from the perspectives of time, industry, and corporate groups. Companies were typically found to emphasize sound management, market leadership, and business reorganization during economic downturns while stressing capacity building and organizational culture during market transition periods. Also, companies belonging to corporate groups tended to emphasize founding philosophy and corporate culture.

The Impact of Product Review Usefulness on the Digital Market Consumers Distribution

  • Seung-Yong LEE;Seung-wha (Andy) CHUNG;Sun-Ju PARK
    • Journal of Distribution Science
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    • v.22 no.3
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    • pp.113-124
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    • 2024
  • Purpose: This study is a quantitative study and analyzes the effect of evaluating the extreme and usefulness of product reviews on sales performance by using text mining techniques based on product review big data. We investigate whether the perceived helpfulness of product reviews serves as a mediating factor in the impact of product review extremity on sales performance. Research design, data and methodology: The analysis emphasizes customer interaction factors associated with both product review helpfulness and sales performance. Out of the 8.26 million Amazon product reviews in the book category collected by He & McAuley (2016), text mining using natural language processing methodology was performed on 300,000 product reviews, and the hypothesis was verified through hierarchical regression analysis. Results: The extremity of product reviews exhibited a negative impact on the evaluation of helpfulness. And the helpfulness played a mediating role between the extremity of product reviews and sales performance. Conclusion: Increased inclusion of extreme content in the product review's text correlates with a diminished evaluation of helpfulness. The evaluation of helpfulness exerts a negative mediating effect on sales performance. This study offers empirical insights for digital market distributors and sellers, contributing to the research field related to product reviews based on review ratings.

Analysis on the Trends of Research Themes of the Korean Dance Using Text Mining (텍스트 마이닝을 활용한 한국무용 연구주제 동향 분석)

  • Kim, Woo-Kyung;Yoo, Ji-Young
    • Journal of Korea Entertainment Industry Association
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    • v.13 no.5
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    • pp.215-228
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    • 2019
  • The purpose of this study is to analyze the trends of research themes of the Korean dance in recent 20 years using text mining. The study has analyzed 3,047 words in 1,468 academic papers posted in the Research & Information Services Section(RISS). TEXTOM, a big data analysis solution, has been used to refine and analyse data, and the keyword analysis and topic modeling have been adopted during the text-mining process to come up with meaningful results. First, the theme of studies has shifted from the structure of the basic Korean dance moves to the use and transmission of the Korean dance. Second, those who participate in studies of the Korean dance have changed from middle-aged women to elderly women. Third, studies on dance records have been inactivated. Fourth, studies on Choi Seung-hee have consistently been a subject of interest. Fifth, the focus of studies has turned from the Korean creative dance to the Korean traditional dance. Sixth, there are no iconic research themes that would lead the academic trends with no clear boundaries of research themes.

The Impact of Comments on Music Download and Streaming: A Text Mining Analysis (댓글이 음원 판매량에 미치는 차별적 영향에 관한 텍스트마이닝 분석)

  • Park, Myeong-Seok;Kwon, Young-Jin;Lee, Sang-Yong Tom
    • Knowledge Management Research
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    • v.19 no.2
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    • pp.91-108
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    • 2018
  • This study mainly focused on measuring the impact of comments for a particular song on the number of streamings and downloads. We modeled multiple regression equations to perform this analysis. We chose digital music market for the object of analysis because of its inherent characteristics, such as experience goods, high bandwagon effect, and so on. We carefully utilized text mining technique in accordance with the algorithm of Naïve Bayes classifier to distinguish whether a comment for a piece of music be regarded as positive or negative. In addition, we used 'size of agency' and 'existence of hit song' as moderating variables. The reason for usage of those variables is that those are assumed to affect users' decision for selecting particular song especially when downloading or streaming via music sites. We found empirical evidences that positive comments for a particular song increase the number of both downloads and streamings. However, positive comments may decrease the number of downloads when the size of agency of the artist is big. As a result, we were able to say that a positive comment for a particular song functioned as 'word-of-mouth' effect, inducing other users' behavioral response. We also found that other features of an artist such as size of the agency that the artist belongs to functioned as an external factor along with feature of the song itself.

Ethical Fashion Research Trend Using Text Mining: Network Analysis of the Published Literature 2009-2019 (텍스트 마이닝을 활용한 윤리적 패션 연구동향: 2009-2019 연구 네트워크 분석)

  • Choi, Yeong-Hyeon;Lee, Kyu-Hye
    • Fashion & Textile Research Journal
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    • v.22 no.2
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    • pp.181-191
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    • 2020
  • The fashion industry has faced environmental, social, and ethical issues due to increased interest in ethical consumption. Numerous ethical studies have been conducted in the fashion industry. This study looked at the current state of research by year, academic journal, and detail in major related papers published in Scopus, KCI and KCI between 2009 and 2019. Ethical fashion studies began to appear in 2009 and were concentrated in certain academic journals and focused on fashion marketing and fashion design. Topics in ethical fashion were terms such as sustainable, eco-friendly, up-cycling, recycling, eco, zero-waist, and organic. In ethical fashion studies, environmental studies were conducted most often; in addition, the terms used along with ethical fashion tend to be frequently used for each particular major. Looking at key words used in research by period, the study showed that research was most diverse between 2016 and 2019. In particular, environmental and social issues of ethical fashion and convergence with animal protection, new distribution, science and technology sectors were newly added between 2016 and 2019. This study used text mining and network analysis to understand the overall trends of ethical fashion studies in Korea. In conclusion it is important to realize the relationship between the main words along with the current status analysis.