It is a general practice to evaluate food taste based on sensory tests, however, this test method’s disadvantage is that a lot of cost and time is required and significant deviation is taken place depending on each evaluator as well. Food taste evaluation by utilizing SNS-based big data for supplementing this disadvantage is considered to be a new challenge and innovative method. The objective of this study is to suggest a system that evaluates and recommends the level of domestic food taste by not only clustering food preference using k-means algorithm after sorting out food-related tweet contents from typical twitter of SNS, followed by scoring taste adjectives being mainly used in daily life by using rough set and selecting food-related adjectives among the scored adjectives, but also exploring the level of salty, sour, savory, bitter and sweet tastes through perception map.
목차
Abstract 1. Introduction 2. Relevant Research 2.1. Big Data and SNS 2.2. Taste Adjectives 2.3. Rough Set Theory and Clustering 3. Recommendation System Design based on Taste Adjectives 3.1. Outline of Recommendation System 4. Implementation 4.1. Source (Raw) Data 4.2. Morphologic Element Analysis 4.3. Clustering 5. Conclusion References
보안공학연구지원센터(IJUNESST) [Science & Engineering Research Support Center, Republic of Korea(IJUNESST)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of u- and e- Service, Science and Technology
간기
격월간
pISSN
2005-4246
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of u- and e- Service, Science and Technology Vol.9 No.1