• 제목/요약/키워드: Daum

검색결과 229건 처리시간 0.022초

윤리적 소비와 소비가치의 관계에 대한 소비자 인식 변화: 소셜 빅데이터를 활용한 윤리적 소비와 소비가치의 키워드 변화 분석을 중심으로 (A Study on the Changes in Consumer Perceptions of the Relationship between Ethical Consumption and Consumption Value: Focusing on Analyzing Ethical Consumption and Consumption Value Keyword Changes Using Big Data)

  • 신은정;고애란
    • Human Ecology Research
    • /
    • 제59권2호
    • /
    • pp.245-259
    • /
    • 2021
  • The purpose of this study was to analyze big data to identify the sub-dimensions of ethical consumption, as well as the consumption value associated with ethical consumption that changes over time. For this study, data were collected from Naver and Daum using the keyword 'ethical consumption' and frequency and matrix data were extracted through Textom, for the period January 1, 2016, to December 31, 2018. In addition, a two-way mode network analysis was conducted using the UCINET 6.0 program and visualized using the NetDraw function. The results of text mining show increasing keyword frequency year-on-year, indicating that interest in ethical consumption has grown. The sub-dimensions derived for 2014 and 2015 are fair trade, ethical consumption, eco-friendly products, and cooperatives and for 2016 are fair trade, ethical consumption, eco-friendly products and animal welfare. The results of deriving consumption value keywords were classified as emotional value, social value, functional value and conditional value. The influence of functional value was found to be growing over time. Through network analysis, the relationship between the sub-dimensions of ethical consumption and consumption values derived each year from 2014 to 2018 showed a significantly strong correlation between eco-friendly product consumption and emotional value, social value, functional value and conditional value.

소셜 미디어 빅데이터를 활용한 호캉스(hocance) 현상 분석 (An Analysis of the Hocance Phenomenon using Social Media Big Data)

  • 최홍열;박은경;남장현
    • 아태비즈니스연구
    • /
    • 제12권2호
    • /
    • pp.161-174
    • /
    • 2021
  • Purpose - The purpose of this study was to examine the recent popular consumption trend, the hocance phenomenon, using social media big data. The study intended to present practical directions and marketing measures for the recovery and growth of the hotel industry after COVID-19 pandemic. Design/methodology/approach - Big data analysis has been used in various fields, and in this study, it was used to understand the hocance phenomenon. For three years from January 1, 2018 to December 31, 2020, we collected text data including the keyword 'hocance' from the blog and cafe of NAVER and Daum. TEXTOM and UCINET 6 were used to collect and analyze the data. Findings - According to the results of analysis, the words such as 'hocance', 'hotel', 'Seoul', 'travel', 'swimming pool', 'Incheon', 'breakfast', 'child' and 'friend' were identified with high frequency. The results of CONCOR analysis showed similar results in all three years. It has been confirmed that 'swimming pool', 'breakfast', 'child' and 'friend' are important when deciding on the hocance package. Research implications or Originality - The study was differentiated in that it used social media big data instead of traditional research methods. Furthermore, it reflected social phenomena as a consumption trend so there was practical value in establishing marketing strategies for the tourism and hotel industry.

텍스트 마이닝을 활용한 웹툰 애플리케이션 사용자 리뷰 분석 (Analysis of User Reviews for Webtoon Applications Using Text Mining)

  • 신효림;최준호
    • 문화기술의 융합
    • /
    • 제8권4호
    • /
    • pp.457-468
    • /
    • 2022
  • 웹툰 산업이 급속도로 성장하며, 이러한 성장세와 함께 새로운 웹툰 애플리케이션 모델이 제시되었다. 웹툰 애플리케이션 1.0과 2.0을 지나 3.0의 시대가 시작된 것이다. 이러한 변화에도 불구하고 아직까지 웹툰 애플리케이션을 대상으로 한 사용자 리뷰 분석 연구는 부족한 실정이다. 이에 이 연구는 웹툰 애플리케이션 3.0 모델을 제시한 '카카오웹툰(다음웹툰)'을 대상으로 사용자 리뷰를 분석하고자 한다. 분석을 위해 애플리케이션 리뷰 20,382개를 수집한 후 전처리 과정을 버전 별로 TF-IDF, 네트워크 분석, 토픽 모델링, 감성 분석을 실시하였다. 이를 통해 웹툰 애플리케이션 변화에 따른 사용자 경험을 탐구하고 리뷰를 통한 사용성 평가를 진행하였다.

빅데이터 텍스트 마이닝 분석을 활용한 아메카지 패션 트렌드 특징 고찰 (A Study on the Characteristics of Amekaji Fashion Trends Using Big Data Text Mining Analysis)

  • 김지형
    • 패션비즈니스
    • /
    • 제26권3호
    • /
    • pp.138-154
    • /
    • 2022
  • The purpose of this study is to identify the characteristics of domestic American casual fashion trends using big data text mining analysis. 108,524 posts and 2,038,999 extracted keywords from Naver and Daum related to American casual fashion in the past 5 years were collected and refined by the Textom program, and frequency analysis, word cloud, N-gram, centrality analysis, and CONCOR analysis were performed. The frequency analysis, 'vintage', 'style', 'daily look', 'coordination', 'workwear', 'men's wear' appeared as the main keywords. The main nationality of the representative brands was Japanese, followed by American, Korean, and others. As a result of the CONCOR analysis, four clusters were derived: "general American casual trend", "vintage taste", "direct sales mania", and "American styling". This study results showed that Japanese American casual clothes are influenced by American casual clothes, and American casual fashion in Korea, which has been reinterpreted, is completed with various coordination and creative styles such as workwear, street, military, classic, etc., focusing on items and brands. Looks were worn and shared on social networks, and the existence of an active consumer group and market potential to obtain genuine products, ranging from second-hand transactions for limited edition vintages to individual transactions were also confirmed. The significance of this study is that it presented the characteristics of American casual fashion trends academically based on online text data that the public actually uses because it has been spread by the public.

The Effect of ChatGPT Factors & Innovativeness on Switching Intention : Using Theory of Reasoned Action (TRA)

  • Hee-Young CHO;Hoe-Chang YANG;Byoung-Jo HWANG
    • 유통과학연구
    • /
    • 제21권8호
    • /
    • pp.83-96
    • /
    • 2023
  • Purpose: This study examined the relationship between the factors (Credibility, Usability) and user Innovativeness of the ChatGPT on TRA (Theory of Reasoned Action; Subjective Norm, Attitude) and Switching Intention. TRA and Innovation Diffusion Theory (IDT) were used. Research design, data and methodology: From April 26 to 27, 2023, an online panel survey agency was commissioned to conduct a survey of GhatGPT users in their 20s and 40s in Korea, and a total of 210 people were used for the final analysis. Verification of the research model was performed using SPSS and AMOS. Results: First, ChatGPT factors (Credibility, Usability) were found to have positive effects on TRA (Subjective Norm, Attitude). Second, ChatGPT user Innovativeness was found to have a positive effect on TRA (Subjective Norm, Attitude). Third, ChatGPT users' TRA (Subjective Norm, Attitude) were found to have positive effects on Switching Intention. Conclusions: These results mean that the superior Usability and Credibility of ChatGPT and the Innovativeness of users have a significant effect on the Switching Intention from existing Portal Service (Naver, Google, Daum, etc.) to ChatGPT. Generative AI such as ChatGPT should strive to develop various services such as improving the convenience of functions so that innovative users can use them easily and conveniently in order to provide services that meet expectations.

A Study on the Trend Change of Restaurant Entrepreneurship through Big Data Analysis

  • Jong-Hyun Park;Yang-Ja Bae;Jun-Ho Park;Gi-Hwan Ryu
    • International Journal of Internet, Broadcasting and Communication
    • /
    • 제15권4호
    • /
    • pp.332-341
    • /
    • 2023
  • Notable trends in the restaurant start-up market after the lifting of social distancing include increasing interest in start-ups, emphasizing the importance of food quality and diversity, decreasing the relative importance of delivery services, and increasing interest in certain industries. The data collection period is three years from April 2021 to May 2023, including before and after social distancing, and texts extracted from blogs, news, cafes, web documents, and intellectuals provided by Naver, Daum, and Google were collected. For the collected data, the top 30 words were derived through a refining process. In addition, based on April 2021, the application period of social distancing, data from April 2021 to April 2022, and data from May 2022 to May 2023, Through these changes in trends, founders can capture new opportunities in the market and develop start-up strategies. In conclusion, this paper provides important insights for founders in accurately understanding the changes in food service start-up trends and in developing strategies appropriate to the current market situation.

Comparative Analysis of the Status of Restaurant Start-ups Before and After the Lifting of Social Distancing Through Big Data Analysis

  • Jong-Hyun Park;Yang-Ja Bae;Jun-Ho Park;Gi-Hwan Ryu
    • International journal of advanced smart convergence
    • /
    • 제12권4호
    • /
    • pp.353-360
    • /
    • 2023
  • This paper explores notable shifts in the restaurant startup market following the lifting of social distancing measures. Key trends identified include an escalated interest in startups, a heightened focus on the quality and diversity of food, a relative decline in the importance of delivery services, and a growing interest in specific industry sectors. The study's data collection spanned three years, from April 2021 to May 2023, encompassing the period before and after social distancing. Data were sourced from a range of online platforms, including blogs, news sites, cafes, web documents, and intellectual forums, provided by Naver, Daum, and Google. From this collected data, the top 50 words were identified through a refinement process. The analysis was structured around the social distancing application period, comparing data from April 2021 to April 2022 with data from May 2022 to May 2023. These observed trend changes provide founders with valuable insights to seize new market opportunities and formulate effective startup strategies. In summary, We offer crucial insights for founders, enabling them to comprehend the evolving dynamics in food service startups and to adapt their strategies to the current market environment.

A Study on the Change of Tourism Marketing Trends through Big Data

  • Se-won Jeon;Gi-Hwan Ryu
    • International journal of advanced smart convergence
    • /
    • 제13권2호
    • /
    • pp.166-171
    • /
    • 2024
  • Recently, there has been an increasing trend in the role of social media in tourism marketing. We analyze changes in tourism marketing trends using tourism marketing keywords through social media networks. The aim is to understand marketing trends based on the analyzed data and effectively create, maintain, and manage customers, as well as efficiently supply tourism products. Data was collected using web data from platforms such as Naver, Google, and Daum through TexTom. The data collection period was set for one year, from December 1, 2022, to December 1, 2023. The collected data, after undergoing refinement, was analyzed as keyword networks based on frequency analysis results. Network visualization and CONCOR analysis were conducted using the Ucinet program. The top words in frequency were 'tourists,' 'promotion,' 'travel,' and 'research.' Clusters were categorized into four: tourism field, tourism products, marketing, and motivation for visits. Through this, it was confirmed that tourism marketing is being conducted in various tourism sectors such as MICE, medical tourism, and conventions. Utilizing digital marketing via online platforms, tourism products are promoted to tourists, and unique tourism products are developed to increase city branding and tourism demand through integrated tourism content. We identify trends in tourism marketing, providing tourists with a positive image and contributing to the activation of local tourism.

Analysis on Types of Golf Tourism After COVID-19 by using Big Data

  • Hyun Seok Kim;Munyeong Yun;Gi-Hwan Ryu
    • International Journal of Advanced Culture Technology
    • /
    • 제12권1호
    • /
    • pp.270-275
    • /
    • 2024
  • Introduction. In this study, purpose is to analize the types of golf tourism, inbound or outbound, by using big data and see how movement of industry is being changed and what changes have been made during and after Covid-19 in golf industry. Method Using Textom, a big data analysis tool, "golf tourism" and "Covid-19" were selected as keywords, and search frequency information of Naver and Daum was collected for a year from 1 st January, 2023 to 31st December, 2023, and data preprocessing was conducted based on this. For the suitability of the study and more accurate data, data not related to "golf tourism" was removed through the refining process, and similar keywords were grouped into the same keyword to perform analysis. As a result of the word refining process, top 36 keywords with the highest relevance and search frequency were selected and applied to this study. The top 36 keywords derived through word purification were subjected to TF-IDF analysis, visualization analysis using Ucinet6 and NetDraw programs, network analysis between keywords, and cluster analysis between each keyword through Concor analysis. Results By using big data analysis, it was found out option of oversea golf tourism is affecting on inbound golf travel. "Golf", "Tourism", "Vietnam", "Thailand" showed high frequencies, which proves that oversea golf tour is now the re-coming trends.

A Study on User Perception of Tourism Platform Using Big Data

  • Se-won Jeon;Sung-Woo Park;Youn Ju Ahn;Gi-Hwan Ryu
    • International journal of advanced smart convergence
    • /
    • 제13권1호
    • /
    • pp.108-113
    • /
    • 2024
  • The purpose of this study is to analyze user perceptions of tourism platforms through big data. Data were collected from Naver, Daum, and Google as big data analysis channels. Using semantic network analysis with the keyword 'tourism platform,' a total of 29,265 words were collected. The collection period was set for two years, from August 31, 2021, to August 31, 2023. Keywords were analyzed for connected networks using TexTom and Ucinet programs for social network analysis. Keywords perceived by tourism platform users include 'travel,' 'diverse,' 'online,' 'service,' 'tourists,' 'reservation,' 'provision,' and 'region.' CONCOR analysis revealed four groups: 'platform information,' 'tourism information and products,' 'activation strategies for tourism platforms,' and 'tourism destination market.' This study aims to expand and activate services that meet the needs and preferences of users in the tourism field, as well as platforms tailored to the changing market, based on user perception, current status, and trend data on tourism platforms.