• 패션비즈니스
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• 제23권5호
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• pp.81-95
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• 2019

The purpose of this study is to research consumers' perception of the HiSeoul fashion show, which is being used by new designers as a means of promotion, and to propose a strategy for revitalizing new designer brands. This was done in order to secure basic data from fashion consumers, to help guide marketing strategies and promote rising designers. In this research, the consumers' perception of HiSeoul fashion show was verified using text-mining, data refinement and word clouding that was undertaken by TEXTOM3.0. Also, semantic network analysis, CONCOR analysis and visualization of the analysis results were performed using Ucinet 6.0 and NetDraw. "HiSeoul fashion show" was used as the keyword for text-mining and data was collected from March 1, 2018 to April 30, 2019. Using frequency analysis, TF-IDF, and N-gram, it was also shown that consumers are aware of places where shows are held, such as DDP and Igansumun. It was also revealed that consumers recognize rising designer brands, designer's names, the names of guests attending the show and the photo times. This study is meaningful in that it not only confirmed consumers' interest in new designer brands participating in the HiSeoul Fashion Show through big data but also confirmed that it is available as a marketing strategy to boost brand sales. This study suggests using HiSeoul show room to induce consumer sales, or inviting guests that match the brand image to promote them on SNS on the day the show is held for a marketing strategy.

• 한국의류산업학회지
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• 제24권5호
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• pp.593-604
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• 2022

The purpose of this study is to analyze the flow of change in perception of metaverse before and after COVID-19 through big data analysis. This research method used Textom to collect all data, including metaverse for two years before COVID-19 (2018.1.1~2019.11.30) and after COVID-19 outbreak (2020.1.11~2021.12.31), and the collection channels were selected by Naver and Google. The collected data were text mining, and word frequency, TF-IDF, word cloud, network analysis, and emotional analysis were conducted. As a result of the analysis, first, hotels, weddings, and glades were commonly extracted as social issues related to metaverse before and after COVID-19, and keywords such as robots and launches were derived, so the frequency of keywords related to hotels and weddings was high. Second, the association of the pre-COVID-19 metaverse keywords was platform-oriented, content-oriented, economic-oriented, and online promotion-oriented, and post-COVID-19 clusters were event-oriented, ontact sales-oriented, stock-oriented, and new businesses. Third, positive keywords such as likes, interest, and joy before COVID-19 were high, and positive keywords such as likes, joy, and interest after COVID-19. In conclusion, through this study, it was found that metaverse has firmly established itself as a new platform business model that can be used in various fields such as tourism, travel, festivals, and education using smart technology and metaverse.

• 현장농수산연구지
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• 제22권1호
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• pp.113-129
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• 2020

본 연구는 2020년 한농대 입학생의 비정형 텍스트인 자소서에서 의미 있는 정보 혹은 규칙을 추출하기 위하여 고교 재학 중 '학업 및 학습경험'과 '교내 활동'을 기술한 두 개 문항에 대하여 텍스트 마이닝에 의한 토픽 분석과 연관성 분석을 하였다. 모집 전형을 구분하지 않은 텍스트 마이닝 분석 결과에서 '학업 및 학습 경험' 항목과 관련된 주요 키워드는 '공부', '생각', '노력', '문제', '친구' 등의 순으로 많이 나타났으며, '교내 활동' 항목과 관련된 주요 키워드는 '활동', '생각', '친구', '동아리', '학교' 등의 순으로 빈도가 높게 나타났다. 그러나 도시 인재 전형과 농수산 인재 전형 신입생들의 키워드 빈도 순위는 두 항목 모두 전형 특성에 따른 약간의 차이를 나타냈다. 빈도 분석에 결과는 빈도수 상위 50위까지의 키워드를 워드 클라우드로 시각화하여 키워드를 알기 쉽게 표현하였다. 연관 분석은 apriori() 함수를 사용하였으며 적정한 계산을 위하여 support(지지도)와 confidence(신뢰도)의 기준값을 항목별로 설정하였다. 먼저 '학업' 항목에 대한 연관 규칙은 46개를 추출하였으며, 그 가운데 {공부} => {생각}, {성적} => {공부} 및 {과목} => {공부} 등의 규칙에서 높은 연관성을 볼 수 있었다. 이 규칙을 바탕으로 매개체 역할의 키워드를 평가하는 관계 중심성 평가와 노드에 연결된 edge의 수에 따라 중요도를 파악하는 연결 중심성 평가에서는 '생각', '공부', '노력', '시간' 등의 키워드가 중심적인 역할을 하는 정보를 획득하였다. 다음으로 '교내 활동' 항목에서는 45개의 연관 규칙을 생성하여 {활동} => {생각}, {동아리} => {활동} 등의 규칙에서 높은 연관성을 볼 수 있었으며, 관계 중심성 평가와 연결 중심성 평가에서는 '생각', '활동', '학교', '시간', '친구' 등의 키워드가 중심 키워드라는 결과를 얻었다. 다음 연구에서는 자소서의 나머지 두 개의 문항 '배려·나눔·협력·갈등관리' 항목과 한농대 '지원동기와 향후 진로계획' 항목을 분석한다. 분석에는 '키워드의 빈도'에 '문서 빈도의 역수'를 곱하여 주로 다량의 문서에서 핵심어를 추출하는 TF-IDF(Term Frequency-Inverse Document Frequency) 분석을 추가한다.

• 한국컴퓨터정보학회논문지
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• 제25권7호
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• pp.27-37
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• 2020

본 연구의 목적은 다양한 도메인에 대한 소프트웨어 요구사항 명세서로부터 수집된 요구사항을 데이터로 활용하여 데이터 중심적 접근법(Data-driven Approach)의 연구를 통해 요구사항을 분류한다. 이 과정에서 기존 요구사항의 특징과 정보를 바탕으로 다양한 자연어처리를 이용한 데이터 전처리와 기계학습 모델을 통해 요구사항을 기능적 요구사항과 비기능적 요구사항으로 분류하고 각 조합의 결과를 제시한다. 그 결과로, 요구사항을 분류하는 과정에서, 자연어처리를 이용한 데이터 전처리에서는 어간 추출과 불용어제거와 같은 토큰의 개수와 종류를 감소하여 데이터의 희소성을 좀 더 밀집형태로 변형하는 데이터 전처리보다는 단어 빈도수와 역문서 빈도수를 기반으로 단어의 가중치를 계산하는 데이터 전처리가 다른 전처리보다 좋은 결과를 도출할 수 있었다. 이를 통해, 모든 단어를 고려하여 가중치 값은 기계학습에서 긍정적인 요인을 볼 수 있고 오히려 문장에서 의미 없는 단어를 제거하는 불용어 제거는 부정적인 요소로 확인할 수 있었다.

• 응용통계연구
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• 제32권3호
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• pp.451-462
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• 2019

문서-용어 빈도행렬은 텍스트 마이닝 분야에서 보편적으로 사용되는 데이터의 한 유형으로, 여러 개체들이 제공하는 문서를 기반으로 만들어진다. 그러나 대다수의 연구자들은 개체 정보에 무게를 두지 않고 여러 문서에서 공통적으로 등장하는 공통용어 중 핵심적인 용어를 효과적으로 찾아내는 방법에 집중하는 경향을 보인다. 공통용어에서 핵심어를 선별할 경우 특정 문서에서만 등장하는 중요한 용어들이 공통용어 선정단계에서부터 배제될 뿐만 아니라 개별 문서들이 갖는 고유한 정보가 누락되는 등의 문제가 야기된다. 본 연구에서는 이러한 문제를 극복할 수 있는 데이터를 근접성 데이터라 정의한다. 그리고 근접성 데이터를 생성할 수 있는 12가지 방법 중 개체 군집화의 관점에서 가장 최적화된 방법을 제안한다. 개체 특성 파악을 위한 군집화 알고리즘으로는 다차원척도법과 K-평균 군집분석을 활용한다.

• 한국의상디자인학회지
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• 제25권2호
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• pp.111-122
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• 2023

This study aims to collect Big Data related to DIY clothing, analyze the results on a year-by-year basis, understand consumers' perceptions, the status, and reality of DIY clothing. The reference period for the evaluation of DIY clothing trends was set from 2012 to 2022. The data in this study was collected and analyzed using Textom, a Big Data solution program certified as a Good Software by the Telecommunications Technology Association (TTA). For the analysis of fabric-related DIY products, the keyword was set to "DIY clothing", and for data cleansing following collection, the "Espresso K" module was employed. Also, via data collection on a year-by-year basis, a total of 11 lists were generated and the collected data was analyzed by period. The following are the findings of this study's data collection on DIY clothing. The total number of keywords collected over a period of ten years on search engines "Naver" and "Google" between January 1, 2012 and December 31, 2022 was 16,315, and data trends by period indicate a continuous upward trend. In addition, a keyword analysis was conducted to analyze TF-IDF (Term Frequency-Inverse Document Frequency), a statistical measure that reflects the importance of a word within data, and the relationship with N-gram, an analysis of the correlation concerning the relationship between words. Using these results, it was possible to evaluate the popularity and growing tendency of DIY clothing products in conjunction with the evolving social environment, as well as the desire to explore DIY trends among consumers. Therefore, this study is valuable in that it provides preliminary data for DIY clothing research by analyzing the status and reality of DIY products, and furthermore, contributes to the development and production of DIY clothing.

• Journal of Information Technology Applications and Management
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• 제25권1호
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• pp.19-32
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• 2018

Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection method using Artificial Intelligence techniques over the past years. But, unfortunately, there have been no prior studies proposed an automated fake news detection method for Korean news. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (Topic Modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as multiple discriminant analysis, case based reasoning, artificial neural networks, and support vector machine can be applied. To validate the effectiveness of the proposed method, we collected 200 Korean news from Seoul National University's FactCheck (http://factcheck.snu.ac.kr). which provides with detailed analysis reports from about 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

• 패션비즈니스
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• 제25권3호
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• pp.90-107
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• 2021

This study concludes that communication based on interaction between broadcasting hosts and consumers is differently characterized by fashion live commerce types. Subcategories of the types of fashion live commerce were created and used in the analyses of domestic consumer awareness. Three subcategories were created: The department store type, Designer brand type, and Influencer host type. Comments representing consumers' awareness that appear immediately during real-time broadcasting were collected and used for the analyses. The frequency and TF-IDF-based top keywords were selected to analyze the semantic network and CONCOR, and the top keywords were analyzed by deriving the values of degree of centrality. The analysis identified that a group of product attributes and a group of live commerce offered value were common between the three types. As for the group characteristics classified by type, for the department store types, brand attributes, benefits, and values from pursuing the products were identified. For designer brand types, a group of viewers' responses and inquiries were identified. It is interpreted that the satisfaction value gained from hosts with product expertise has been clustered. Influencer host types have affirmed a group of external product values. A close relationship is formed and it is thought to have led a group of values to trust the external image of the product. This study carries significance in analyzing real-time comment data from consumers using fashion live commerce to empirically reveal the characteristics of each type.

• 한국의상디자인학회지
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• 제23권2호
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• pp.17-28
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• 2021

The baby-tech industry, which combines IT with existing parenting product, is attracting increasing amounts of attention. Consequently various types of baby products incorporating functionality and design are being launched. In recent years, particularly as the market segments increases for babywearing products, parenting products that account for the child's comfort and parents' convenience are required. Therefore, this study examines the characteristics and consumer perception of babywear products, which are important for the emotional stability, development, and rearing of children. The study utilizes text mining and a network analysis by collecting unstructured text data. An examination of the network, based on the frequency of keywords for each babywear product and the degree of the connection to the centering index, revealed that consumers value convenience and price when purchasing products. The consumer perception and consideration factors that appear individually according to the product were also identified. In addition, studying body parts with high TF-IDF values revealed a difference in the body parts considered by consumers for each product. Lastly, through the visualization data based on the keywords that appeared in public, commonly appearing keywords, and those that appeared individually were examined. Through SNS, product characteristics as well as a new parenting culture that shared child-rearing routines were confirmed. This study suggests planning and marketing directions for the development of babywear products that meet consumer needs.

• 한국식생활문화학회지
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• 제38권1호
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• pp.1-14
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• 2023

This study was conducted to explore the change in the market issues on HMR (Home Meal Replacements) using local foods after the COVID-19 outbreak. Online text data were collected from internet news, social media posts, and web documents before (from January 2016 to December 2019) and after (from January 2020 to November 2022) the COVID-19 outbreak. TF-IDF analysis showed that 'Trend', 'Market', 'Consumption', and 'Food service industry' were the major keywords before the COVID-19 outbreak, whereas 'Wanju-gun', 'Distribution', 'Development', and 'Meal-kit' were main keywords after the COVID-19 outbreak. The results of topic modeling analysis and categorization showed that after the COVID-19 outbreak, the 'Market' category included 'Non-face-to-face market' instead of 'Event,' and 'Delivery' instead of 'Distribution'. In the 'Product' category, 'Marketing' was included instead of 'Trend'. Additionally, in the 'Support' category, 'Start-up' and 'School food service' appeared as new topics after the COVID-19 outbreak. In conclusion, this study showed that meaningful change had occurred in market issues on HMR using local foods after the COVID-19 outbreak. Therefore, governments should take advantage of such market opportunity by implementing policy and programs to promote the development and marketing of HMR using local foods.