• 아태비즈니스연구
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• 제12권2호
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• pp.161-174
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• 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.

• 한국조리학회지
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• 제23권7호
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• pp.1-10
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• 2017

This study proposed SNS big data analysis method of food service industry in the 4th industrial revolution. This study analyzed the keyword of the fourth industrial revolution by using Google trend. Based on the data posted on the SNS from January 1, 2016 to September 5, 2017 (1 year and 8 months) utilizing the "Social Metrics". Through the social insights, the related words related to cooking were analyzed and visualized about attributes, products, hobbies and leisure. As a result of the analysis, keywords were found such as cooking, entrepreneurship, franchise, restaurant, job search, Twitter, family, friends, menu, reaction, video, etc. As a theoretical implication of this study, we proposed how to utilize big data produced from various online materials for research on restaurant business, interpret atypical data as meaningful data and suggest the basic direction of field application. In order to utilize positioning of customers of restaurant companies in the future, this study suggests more detailed and in-depth consumer sentiment as a basic resource for marketing data development through various menu development and customers' perception change. In addition, this study provides marketing implications for the foodservice industry and how to use big data for the cooking industry in preparation for the fourth industrial revolution.

• 문화기술의 융합
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• 제6권1호
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• pp.413-418
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• 2020

관광코스 개발을 위한 관광객의 이동패턴을 통신사의 빅데이터를 바탕으로 관광객 정보를 수집 분석하여 관광코스의 질적 향상을 도모하고자 하며, 특히, 분석된 데이터를 통해 관광객의 관광 유입 효과를 추정할 수 있는 실증적인 데이터를 도출하고, 이를 바탕으로 관광코스의 특성과 향후 새로운 관광코스 개발에 필요한 기초자료로 활용하고자 한다. 지역 관광 코스 개발을 위한 관광객의 이동패턴을 통신사, 카드사, 기타의 수집 빅데이터를 바탕으로 관광객의 이동경로 및 체류시간 정보를 수집 분석하여 관광코스 개발의 질적 향상을 도모하기 위함이며, 특히 분석된 데이터를 통해 관광객의 관광유입 효과를 추정할 수 있는 실증적인 데이터를 도출하고, 이를 바탕으로 관광코스의 특성과 향후 새로운 관광코스 개발에 필요한 기초자료로 활용하고자 한다.

• International Journal of Internet, Broadcasting and Communication
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• 제12권1호
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• pp.55-60
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• 2020

Adaptive recommendation systems have been developed with big data processing as a system that provides services tailored to users based on user information and usage patterns. Deep learning can be used in these adaptive recommendation systems to handle big data, providing more efficient user-friendly recommendation services. In this paper, we propose a system that uses deep learning to categorize and recommend tourism types to suit the user's personality. The system was divided into three layers according to its core role to increase efficiency and facilitate maintenance. Each layer consists of the Service Provisioning Layer that real users encounter, the Recommendation Service Layer, which provides recommended services based on user information entered, and the Adaptive Definition Layer, which learns the types of tourism suitable for personality types. The proposed system is highly scalable because it provides services using deep learning, and the adaptive recommendation system connects the user's personality type and tourism type to deliver the data to the user in a flexible manner.

• International Journal of Internet, Broadcasting and Communication
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• 제12권2호
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• pp.113-119
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• 2020

Numerous tourist-related data produced on the Internet contain not only simple tourist information but also diverse ideas and opinions from users. In order to derive meaningful information about tourist sites from such big data, the social network analysis of tourist keywords can identify the frequency of keywords and the relationship between keywords. Thus, it is possible to make recommendations more suitable for users by utilizing the clear recommendation criteria of tourist attractions and the relationship between tourist attractions. In this paper, a recommendation system was designed based on tourist site information through big data social network analysis. Based on user personality information, the types of tourism suitable for users are classified through deep learning and the network analysis among tourist keywords is conducted to identify the relationship between tourist attractions belonging to the type of tourism. Tour information for related tourist attractions shown on SNS and blogs will be recommended through tagging.

• International Journal of Internet, Broadcasting and Communication
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• 제15권2호
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• pp.112-117
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• 2023

This study aims to find out what tourists' interests and perceptions are like through online big data. Big data for a total of five years from 2018 to 2022 were collected using the Textom program. Sentiment analysis was performed with the collected data. Sentiment analysis expresses the necessity and emotions of city tours in online reviews written by tourists using city tours. The purpose of this study is to extract and analyze keywords representing satisfaction. The sentiment analysis program provided by the big data analysis platform "TEXTOM" was used to study positives and negatives based on sentiment analysis of tourists' online reviews. Sentiment analysis was conducted by collecting reviews related to the city tour. The degree of positive and negative emotions for the city tour was investigated and what emotional words were analyzed for each item. As a result of big data sentiment analysis to examine the emotions and sentiments of tourists about the city tour, 93.8% positive and 6.2% negative, indicating that more than half of the tourists are positively aware. This paper collects tourists' opinions based on the analyzed sentiment analysis, understands the quality characteristics of city tours based on the analysis using the collected data, and sentiment analysis provides important information to the city tour platform for each region.

• 한국전자통신학회논문지
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• 제15권4호
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• pp.739-746
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• 2020

2016년 세계 경제포럼에서 처음 소개된 4차 산업혁명은 빅데이터 분석, 사물인터넷, 인공 지능 등의 핵심 정보통신 기술의 변화뿐만 아니라 관광 업계에도 엄청난 파급 효과를 가져 오고 있다. 본 연구는 빅데이터 분석과 설문조사를 통하여 여수시의 관광활성화 방안을 제시한다. 소셜 메트릭스를 사용하여 여수 관광에 대하여 감성어와 긍부정 추이를 추출하고, 네이버 데이터랩을 사용하여 여수 관광에 관련된 키워드를 추출하여 R 언어로 시각화하였다. 그리고 여수지역을 방문하는 493명의 여행객의 설문조사를 바탕으로 SPSS를 사용하여 빈도, 요인, 차이, 상관관계 및 회귀분석을 수행하였다. 여수 여행과 해양 케이블카의 감성어 분석에서는 긍정이 부정보다 월등히 많았다. 설문조사 분석에서 여수지역이 여수 관광 만족도와 활성화에 유의미하고, 연령별로 선호하는 관광지와 검색 기기가 다르다는 것을 확인하였다. 빅데이터 분석과 설문조사에서 관광객들은 함께 즐기면서 힐링 할 수 있는 해양공원 같은 소프트 컨텐츠가 있는 관광지를 선호한다는 것을 보여주었다.

• 경영정보학연구
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• 제21권2호
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• pp.1-27
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• 2019

정보기술의 발전으로 스마트한 관광서비스가 가능해졌는데, 관광산업이 기존 산업 범위를 벗어나 다른 산업과 융합을 통해 새로운 비즈니스 모델이 지속적으로 창출되기 위해서는 관광객들의 소비유형, 서비스 이용패턴 등을 정확히 이해하도록 빅데이터를 이해하고 활용하는 것이 필요하다. 본 연구는 제주 스마트 관광 활성화 방안을 제시하고자 제주도를 방문한 국내외 관광객의 카드사용 데이터와 위치기반 데이터를 기반으로 관광 빅데이터 분석을 수행하였다. 분석결과 사드의 영향으로 중국인 관광객의 제주도 방문율이 감소한 것을 알 수 있다. 소비현황분석 결과, 공항과 면세점이 위치한 북부지역에서 중국인 관광객 소비 대부분이 발생하고 있으며, 다른 지역에서의 중국인 관광객 소비수준은 매우 낮게 나타났다. 제주시 구도심 및 서귀포시 지역 경제는 전반적인 정체를 보이며, 정책 변화가 없는 한, 현재 지역 특성에 따른 소비트렌드 및 성장성이 유지될 전망이다. 셋째, 젊은 관광객의 고객 수뿐만 아니라 소비금액 비중도 증가하는 추세로, 젊은 세대들이 한 공간에서 먹고, 마시고, 쇼핑할 수 있는 제주도 복합쇼핑몰 설립으로 지속적 유입을 유도할 수 있는 방안 마련이 필요하다. 마지막으로 제주도의 와이파이 경로 분석을 통해, 관광객의 이동경로를 파악하여 날씨, 쇼핑, 편의시설, 교통 등 생활밀착형 서비스를 제공할 필요가 있다. 이러한 분석결과를 토대로 제주 관광객의 관광 행동을 이해하고 활성화 방안을 위한 공공정책과 마케팅 전략을 제시하였다.

• 한국컴퓨터정보학회논문지
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• 제24권9호
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• pp.119-125
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• 2019

The purpose of this study is to find out the relevant factors of the choice tendency of tourism users to Intelligent Tourism platform through big data analysis, which will help enterprises to make accurate positioning and improvement according to user information feedback in the tourism market in the future, so as to gain the favor of users' choice and achieve long-term market competitiveness. This study takes the Intelligent Tourism platform as the independent variable and the user choice tendency as the dependent variable, and explores the related factors between the Intelligent Tourism platform and the user choice tendency. This study make use of text mining and R language text analysis, and uses SPSS and AMOS statistical analysis tools to carry out empirical analysis. According to the analysis results, the conclusions are as follows: service quality has a significant positive correlation with user choice tendency; service quality has a significant positive correlation with tourism trust; Tourism Trust has a significant positive correlation with user choice tendency; service quality has a significant positive correlation with user experience; user experience has a significant positive correlation with user choice tendency Positive correlation effect.

• International Journal of Internet, Broadcasting and Communication
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• 제15권3호
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• pp.1-7
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• 2023

Since the first outbreak of COVID-19 in 2019, it has caused a huge blow to the restaurant industry. However, as social distancing was lifted as of April 2022, the restaurant industry gradually recovered, and as a result, interest in restaurant start-ups increased. Therefore, in this paper, big data analysis was conducted by selecting "restaurant start-up" as a key keyword through social media big data analysis using Textom and then conducting word frequency and CONCOR analysis. The collection period of keywords was selected from May 1, 2022 to May 23, 2023, after the lifting of social distancing due to COVID-19, and based on the analysis, the development of a restaurant start-up consulting chatbot service is proposed.