빅 데이터를 이용한 부산의 도시평판 분석에 관한 연구

• 영문명: A Study on Busan City Reputation Analysis by Using Big Data
• 발행기관: 관광경영학회
• 저자명: 허성란
• 간행물 정보: 『관광경영연구』제22권 제3호, 803~824쪽, 전체 22쪽
• 주제분류: 사회과학 > 관광학
• 파일형태: PDF
• 발행일자: 2018.05.31

영문 초록

We conducted a theoretical study on the Big Data Analyzer method and analysis software for the study. We used Leevi 's analysis tool, MediaLand, for 1 year from May 1, 2015 to April 30, 2016,Based on the keywords that measure tourism factors, environmental factors, traffic factors, civic factors, and cultural factors, which are urban reputation measurement factors in the data,As a result of analyzing the data for one year, a total of 104,810 data were collected with 33,399 media channels and 71,411 social channels, of which 71,411 tourist factors, 17,796 traffic factors, 9,396 cultural factors, 7,999 environmental factors And 7,986 civic factors. The results of analysis related to urban reputation measurement factors are as follows. First, the tourist factors are largely mentioned in terms of attractions and food related keywords. The most famous places were seaside places such as Haeundae and Gwanganri. Most of them were fish cake, pork soup, and seed loaf. Second, environmental factors of Pusan were cleaned, public facilities environment and natural environment, and 1,055 seas were surveyed, followed by a lot of search terms related to the beach. Third, the transportation elements of Busan were various transportation means such as automobile, train, taxi, KTX, etc. Fourth, the citizens' elements of Pusan were the keywords of citizens, honesty and kindness of Busan, and the words related to the communication with local residents when using the facilities and moving were searched. Fifth, the cultural elements of Busan showed many contents related to festival, Haeundae, museum, park, Pusan International Film Festival as a result of searching for cultural festivals, cultural events and cultural facilities.

목차

Ⅰ. 서론

Ⅱ. 이론적 배경

Ⅲ. 연구설계

Ⅳ. 부산의 도시평판 데이터 분석 결과

Ⅴ. 결론

참고문헌

키워드

Big Data Social Media City Reputation Opinion Mining